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Orgnostic Data Orchestration & Health API (0.1.0)

This page covers all the ways in which you can access Orgnostic’s MetaModel, Data Health and Analytics information through our API.

Authentication

Each call to this API is authenticated via the API key.

ApiKey

Example: Authorization: ApiKey <identifier>:<token>

Security Scheme Type: API Key
Header parameter name: Authorization

API Versioning

The API version can be set using X-Api-Version. If the header is not set, the latest version will be used.

Pagination

All API calls that are returning lists are paginated. The default size of returned results is 1000, and this can be changed by using the page_size parameter. After the initial call, the response will contain the pagination key, which will contain the continuation_token. This token could be passed as the GET param in the request to fetch the next page. If the next page does not exist, this value will be empty.

Fetching data by date

Some of the data entities in API calls can be fetched by date fields. For this purpose, you can use either a fixed value or provide a range of values a field can be in with the YYYYMMDD-YYYYMMDD syntax. If a range is used, the values are inclusive.

Examples:

  • employees?start_date=20220203 will fetch all employees that started working on 3 February
  • employees?start_date=20220101-20221231 will fetch all employees that started working in 2022.

Changelog

2023-01-31

Released 0.1.0 version of the API.

HRIS

HRIS metamodel access.

Get the list of employees

Get the list of employees

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

start_date
string
Example: start_date=20200101-20221231

Fetch employees by start date. Can be a single or a range value.

end_date
string
Example: end_date=20200101-20221231

Fetch employees by end date. Can be a single or a range value.

location
string
Example: location=London

Fetch employees by location.

department
string
Example: department=Marketing

Fetch employees by department.

role
string
Example: role=Senior Manager

Fetch employees by role.

team
string
Example: team=Customer Service

Fetch employees by team.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch a single employee

Fetch a single employee

Authorizations:
ApiKey

Responses

Response samples

Content type
application/json
{
  • "employee_id": "string",
  • "first_name": "string",
  • "last_name": "string",
  • "full_name": "string",
  • "email": "string",
  • "location": "string",
  • "department": "string",
  • "role": "string",
  • "team": "string",
  • "gender": "string",
  • "age": 0,
  • "nationality": "string",
  • "ethnicity": "string",
  • "compensation": {
    },
  • "status": {
    },
  • "reports_to": {
    },
  • "division": "string",
  • "branch": "string",
  • "avatar": "string",
  • "functional_level": "string",
  • "tenure": 0,
  • "custom_fields": { },
  • "grade": "string",
  • "generation": "string",
  • "previous_company": "string",
  • "performance_rating": "string"
}

Fetch employment history details

Fetch employment history details

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

start_date
string
Example: start_date=20200101-20221231

Get employment history by start date. Can be a single or a range value.

end_date
string
Example: end_date=20200101-20221231

Get employment history by end date. Can be a single or a range value.

employee_id
string
Example: employee_id=123

Get employment history for employee.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch salary history details

Fetch salary history details

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

effective_from
string
Example: effective_from=20200101-20221231

Fetch salary history by start date. Can be a single or a range value.

effective_to
string
Example: effective_to=20200101-20221231

Fetch salary history by end date. Can be a single or a range value.

employee_id
string
Example: employee_id=123

Fetch salary history for employee.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch bonus details

Fetch bonus details

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

effective_date
string
Example: effective_date=20200101-20221231

Fetch bonus history by date. Can be a single or a range value.

employee_id
string
Example: employee_id=123

Fetch an employee’s bonus history.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch job history details

Fetch job history details

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

start_date
string
Example: start_date=20200101-20221231

Fetch employment history by start date. Can be a single or a range value.

employee_id
string
Example: employee_id=123

Fetch an employee’s job history.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

ATS

ATS metamodel access.

Fetch a list of all applications

Fetch a list of all applications

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

created_at
string
Example: created_at=20200101-20221231

Fetch applications by creation date. Can be a single or a range value.

hired_at
string
Example: hired_at=20200101-20221231

Fetch applications by date when they were hired. Can be a single value or range.

rejected_at
string
Example: rejected_at=20200101-20221231

Fetch applications by their reject date. Can be a single or a range value.

posting_id
string
Example: posting_id=123

Fetch applications by posting id.

recruiter_id
string
Example: recruiter_id=123

Fetch applications by recruiter.

location
string
Example: location=London

Fetch applications by location.

department
string
Example: department=Marketing

Fetch applications by department.

role
string
Example: role=Senior Manager

Fetch applications by role.

team
string
Example: team=Customer Service

Fetch applications by team.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch a list of offers

Fetch a list of offers

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

sent_at
string
Example: sent_at=20200101-20221231

Fetch offers by date sent. Can be a single or a range value.

signed_at
string
Example: signed_at=20200101-20221231

Fetch offers by sign date. Can be a single or a range value.

rejected_at
string
Example: rejected_at=20200101-20221231

Fetch offers by reject date. Can be a single or a range value.

application_id
string
Example: application_id=123

Fetch offers by their application id.

location
string
Example: location=London

Fetch offers by location.

department
string
Example: department=Marketing

Fetch offers by department.

role
string
Example: role=Senior Manager

Fetch offers by role.

team
string
Example: team=Customer Service

Fetch offers by team.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the list of postings

Fetch the list of postings

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

created_at
string
Example: created_at=20200101-20221231

Fetch postings by sent date. Can be a single or a range value.

published_at
string
Example: published_at=20200101-20221231

Fetch postings by publish date. Can be a single or a range value.

closed_at
string
Example: closed_at=20200101-20221231

Fetch postings by close date. Can be a single or a range value.

recruiter_id
string
Example: recruiter_id=123

Fetch postings by their recruiter id.

location
string
Example: location=London

Fetch postings by location.

department
string
Example: department=Marketing

Fetch postings by department.

role
string
Example: role=Senior Manager

Fetch postings by role.

team
string
Example: team=Customer Service

Fetch postings by team.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the candidate list

Fetch the candidate list

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch interview list

Fetch interview list

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

location
string
Example: location=London

Fetch interviews by location.

department
string
Example: department=Marketing

Fetch interviews by department.

role
string
Example: role=Senior Manager

Fetch interviews by role.

team
string
Example: team=Customer Service

Fetch interviews by team.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch list of requisitions

Fetch list of requisitions

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

location
string
Example: location=London

Fetch requisitions by location.

department
string
Example: department=Marketing

Fetch requisitions by department.

role
string
Example: role=Senior Manager

Fetch requisitions by role.

team
string
Example: team=Customer Service

Fetch requisitions by team.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the list of recruiters

Fetch the list of recruiters

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the list of interview stages

Fetch the list of interview stages

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the list of stage pipelines

Fetch the list of stage pipelines

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the feedback list

Fetch the feedback list

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

created_at
string
Example: created_at=20200101-20221231

Fetch feedback by the dates created. Can be a single or a range value.

posting_id
string
Example: posting_id=123

Fetch feedback by posting id.

application_id
string
Example: application_id=123

Fetch feedback by application id.

candidate_id
string
Example: candidate_id=123

Fetch feedback by candidate id.

location
string
Example: location=London

Fetch feedback by location.

department
string
Example: department=Marketing

Fetch feedback by department.

role
string
Example: role=Senior Manager

Fetch feedback by role.

team
string
Example: team=Customer Service

Fetch feedback by team.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

PTO

PTO metamodel access.

Fetch personal time off details

Fetch personal time off details

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

start_date
string
Example: start_date=20200101-20221231

Fetch PTO by start date. Can be a single or a range value.

end_date
string
Example: end_date=20200101-20221231

Fetch PTO by end date. Can be a single or a range value.

employee_id
string
Example: employee_id=123

Fetch an employee’s PTO history.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch all available PTO types

Fetch all available PTO types

Authorizations:
ApiKey

Responses

Response samples

Content type
application/json
{
  • "items": [
    ]
}

PMS

PMS metamodel access.

Fetch a list of PMS users

Fetch a list of PMS users

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the list of PMS Feedback Sessions

Fetch the list of PMS Feedback Sessions

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the list of PMS Peer Recognitions

Fetch the list of PMS Peer Recognitions

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the list of PMS Objectives

Fetch the list of PMS Objectives

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Fetch the list of PMS Reviews

Fetch the list of PMS Reviews

Authorizations:
ApiKey
query Parameters
page_size
integer [ 1 .. 1000 ]
Default: 1000

Sets the page size for pagination.

continuation_token
string

Used to provide the paging continuation token returned in the previous request.

Responses

Response samples

Content type
application/json
{
  • "pagination": {
    },
  • "items": [
    ]
}

Data Health

Data Health information access.

Fetch the list of attributes data health

Fetch the list of attributes data health

Authorizations:
ApiKey

Responses

Response samples

Content type
application/json
{
  • "items": [
    ]
}

Headcount

Headcount analytics information access.

Headcount Overview & Growth Rate

Definition

Headcount Overview & Growth Rate represent the number of active employees expressed over a time period since the organization onset.

How we calculate this

Headcount Growth Rate (T) = Number of new Employees employed between T-1 and T / Number of Employees working at time T-1

Insight to

The size of your organization and its growth are reflected in this metric. When observed through time it can be one of the indicators of organizational growth. Serves as a good benchmarking indicator for organizational stage and maturity, and an indicator of certain types of organizational pathologies that are associated with high growth.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of ethnicities. Examples ethnicity=Asian or ethnicity[]=Black&ethnicity[]=White.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

FTE History

Definition

FTE History represent the number of employees expressed over a time period since the organization onset.

How we calculate this

All employees (T) = Inclusive number of employees who worked in the organization even if it was just one day

Insight to

The size of your organization and its growth are reflected in this metric. When observed through time it can be one of the indicators of organizational growth. Serves as a good benchmarking indicator for organizational stage and maturity, and an indicator of certain types of organizational pathologies that are associated with high growth.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of ethnicities. Examples ethnicity=Asian or ethnicity[]=Black&ethnicity[]=White.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

End Of Month Employee History

Definition

FTE History represent the number of employees expressed over a time period since the organization onset.

How we calculate this

All employees (T) = Number of employees who worked in the organization on the last day of month

Insight to

The size of your organization and its growth are reflected in this metric. When observed through time it can be one of the indicators of organizational growth. Serves as a good benchmarking indicator for organizational stage and maturity, and an indicator of certain types of organizational pathologies that are associated with high growth.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of ethnicities. Examples ethnicity=Asian or ethnicity[]=Black&ethnicity[]=White.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Number of Starters

Definition

Number of Starters is a metric that shows the number of new employees that joined the organization.

How we calculate this

Count employees with a start date within the selected period of time T.

Insight to

It is an indicator of growth and can provide an insight to the workload of HR on onboarding new employees. When observed against the hiring plan it can show the success of the Talent Acquisition team and ability of the organization to scale.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of ethnicities. Examples ethnicity=Asian or ethnicity[]=Black&ethnicity[]=White.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Unplanned Leaves

Definition

As an alternative way of measuring absenteeism, Unplanned Leaves represents a metric that indicates the share of unplanned leaves in total absence days of the employee.

How we calculate this

Unplanned Leaves (T) = Leaves classified as unplanned (in days) / All Leaves (in days) in a period T

Insight to

Provides a better understanding of unexpected loss in productivity. When observed over time frequent short-term, unscheduled absence can also be an indicator of lack of engagement.

Unplanned leave is usually considered to be sick leave, sick child, caregiving, personal emergencies, bereavement leave, personal time off, and similar.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Leave Type Distribution

Definition

Leave Type Distribution is a metric that provides an overview of the most frequent type of absences in your organization.

How we calculate this

Leave Type Distribution (T) = Distribution(Leave Types in period T)

It represents the share of different type of leave of absence days per employee out of all leaves for the selected time period.

Insight to

Analyse the data to learn which types of leaves make the highest portion of total leaves and decide if they are planned or unplanned. Look for increasing trends in unwanted types of leaves and investigate how does that reflect on your cost increase as well as the reasons behind it. Observe this metric alongside Unplanned Leaves and Cost of Unplanned Leaves metrics.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Employment Type Distribution

Definition

Employment Type Distribution shows the distribution of contract types (permanent, fixed term, internship, student, consultancy) per currently active employees.

How we calculate this

Employment Type Distribution (T) = Distribution(Employment Type for each Employee working at time T)

Insight to

It helps understand the models of engagement the organization has with its employees that can heavily influence cost structure, taxation and legal exposure.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of ethnicities. Examples ethnicity=Asian or ethnicity[]=Black&ethnicity[]=White.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Employee Net Growth

Definition

Employee Net Growth shows the true number of new employees added to the organization by also calculating the number of employees who left the company within the selected period. It represent the net headcount growth month over month.

How we calculate this

Employee Net Growth (T) = Number of Employees that started at time T – Number of Employees that left at time T

Insight to

A better understanding of the true increase in the number of employees over time by contrasting the number of hires with the number of leavers. This metric combines data from talent acquisition and turnover to showcase organizational actual growth.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of ethnicities. Examples ethnicity=Asian or ethnicity[]=Black&ethnicity[]=White.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Working vs Absent

Definition

Working vs Absent is a metric that provides an overview of active employees (employees working from the office, remotely or from a business trip) compared to the rest of the workforce. An employee is considered to be absent whenever he/she is on any type of leave.

How we calculate this

Working vs Absent (T) = Percentage of the workforce that worked (including remote work and business trips) at time T compared to the percentage of the workforce that was absent at time T. Percentages are calculated using person-days.

Working = remote work, working from the office, business trip

Absent = any type of leave

Insight to

When observed over time its distribution can be an important input for effective workforce planning and bridging the gaps in productivity inefficiencies.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Remote Work Ratio

Definition

Remote Work Ratio refers to the workforce capacity who work from home or other remote locations compared to workforce capacity working from the office.

How we calculate this

Remote Work Ratio (T) = Percentage of workforce that worked onsite at time T compared to Percentage of workforce that worked offsite at time T.

Percentages are calculated using person-days.

Insight to

When observed over time its distribution can be an important input for effective workforce planning and bridging the gaps in productivity inefficiencies.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Cost of Unplanned Leaves

Definition

Cost of Unplanned Leaves is a metric that indicates how much is unplanned leave costing the organization.

How we calculate this

Cost of Unplanned Leaves (T) = Number of work days on unplanned leaves at time T * Daily salary rate at time T

Insight to

This metric gives an insight into the direct cost of remuneration for unplanned leaves while indirect cost (administrative handling, training of the replacement, the stress of workload transfer on other team members) incurred by the employer could be onerous.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Tenure

Definition

Tenure represents the length of time (years) an employee has worked for the organization.

How we calculate this

Tenure (T) = Sum(Tenure per Employee working at time T) / Number of Employees working at time T

Insight to

It provides an insight into the employee structure from the perspective of service length and years of internal experience. When compared with performance and turnover data it can showcase whether the company retains employees at the peak of their productivity.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Diversity

Diversity analytics information access.

Women in Leadership

Definition

Women in leadership metrics helps you track the representation of women in different levels of your management.

How we calculate this

Women in Leadership Distribution(T) = Distribution(Female Employees per Management Level at time T)

Insight to

To get a closer perspective on diversity in your company you want to explore whether you have an even spread of women in leadership positions relative to their overall representation in the company.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Diversity Index

Definition

Simpson's Diversity Index is a metric that helps you quantify ethnic diversity in a single score.

How we calculate this

We use Simpson's diversity index formula to calculate an overall diversity score across all ethnicities represented in the company.

Insight to

It measures richness, or the number of the groups represented in an employee sample, and evenness, which refers to the spread across those groups or the number of individuals in each ethnic group.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Age Distribution

Definition

Age Distribution is one of the metrics that explain the element of diversity in a company and it shows which percentage of employees belong to which age group.

How we calculate this

Age Distribution(T) = Distribution(Employee Age for each Employee working at time T)

The proportion of employees in successive age groups out of all employees in your company.

Insight to

It can help identify demographic risks the firm faces with retiring and ageing employees. If they're difficult to replace the organization faces capacity or productivity risk.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Gender Distribution

Definition

Gender distribution is one of the most basic metrics that explains the simple element of diversity in company. You should use gender distribution with different filters to explore the share of women in managerial roles and various other departments. Explore historical trends to note any changes in gender distribution as your company grows and develops.

How we calculate this

Gender Distribution(T) = Distribution(Employee Gender for each Employee working at time T)

The proportion of employees identifying as a certain gender out of all employees in your company.

Insight to

It indicates changes in gender distribution as your company grows and develops. Use gender distribution with different filters to explore the share of women in managerial roles and various other departments.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

Array of strings or string

Calculate metric only for single or list of ethnicities. Examples ethnicity=Asian or ethnicity[]=Black&ethnicity[]=White.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Talent Acquisition

Talent Acquisition analytics information access.

Bad Hires

Definition

Bad Hires are considered all employees who leave in the span of the first six months, or the ones who score below 50% on the Quality of Hire metric.

How we calculate this

Count all leavers in their first six months and low scoring employees from filter surveys.

Insight to

It indicates the effectiveness of the hiring process. It is not enough just to meet the expected time to fill a position to consider it a successful hire. This metric can be used in evaluation of the recruitment team as well.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Feedback Rating Distribution

Definition

Interview feedback ratings allow you to see if there are any systematic differences in the way you score candidates across different teams, positions and demographics.

Insight to

It can be an indicator of biases that exist in the recruitment process.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Job Offer Competitiveness

Definition

Job Offer Competitiveness indicates the key reason why your desired candidates reject your offer.

Insight to

Analyzing this metric can provide an organization with a clear picture of how attractive is your offer to the targeted top talent.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

New Hires Ratio

Definition

New Hires Ratio represents the ratio of new hires compared to total currently active employees.

How we calculate this

New Hires Ratio (T) = Number of new hires at time T / Number of active employees at time T

Insight to

A high New Hires Ratio often results in a lower organizational output/productivity due to additional workforce effort to onboard new employees.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Number of Open Positions

Definition

Number of Open Positions provides an overview of all open vacancies for the most immediate hiring needs.

How we calculate this

Count vacancies with an active status in T

Insight to

It is an indicator of how fast the organization is growing, how big is the workload on the recruitment team and what are the currently active vacancies an organization is looking to fill.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of recruiters. Examples recruiter=John Smith or recruiter[]=John Smith&recruiter[]=Alice Smith.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Offers Accepted

Definition

Number of candidates who successfully completed the hiring process and accepted a job offer from the company.

How we calculate this

Count number of job offers marked as accepted in T

Insight to

This metric show the total number of accepted job offers and organizational capability to grow. When observed alongside Offer Acceptance Rate it indicates organizational ability to close the deal with desired candidates.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Offer Acceptance Rate

Definition

This metric shows the number of accepted, rejected, and pending job offers that wait for candidates' feedback. The offer acceptance rate compares the number of candidates who accepted a job offer with the total number of candidates who received an offer from your organization.

Insight to

This metric represents your organization's ability to attract and get desired candidates on board. Offer Acceptance Rate indicates whether candidates had a positive experience that compelled them to accept your offer and whether your job offers are attractive enough for the best people in your talent pipeline.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Quality of Hire

Definition

Quality of Hire (QoH) is considered the ultimate talent acquisition metric. In its essence, it measures the success of the hiring process. The way we calculate quality of hire is by combining direct manager's inputs towards progress and productivity of the new employee against the initial expectations, as well as new employee's assessment of the job-fit. We focus on the time-period of six months.

How we calculate this

QoH(%) = (EA% + DM%) / 2

EA - employee's assessment of the job-fit

DM - direct manager's inputs towards progress and productivity of the new employee against the initial expectations

If employee left in the first 6 months, QoH is 1.

Insight to

It indicates whether the hires meet the expectations in terms of skills and cultural fit for the organization. When observed next to Time to Hire and Cost per Hire, these metrics provide a full picture on the success of the hiring process.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Source of Hire

Definition

Source of Hire is a metric that shows how many new hires (out of total hires) came from each individual channel.

Insight to

It is a visualization of how successful is each source and where you should continue, stop or increase investment of your budget. Referrals can be a good indicator of employee engagement and can be compared to eNPS data.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of recruiters. Examples recruiter=John Smith or recruiter[]=John Smith&recruiter[]=Alice Smith.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of races. Examples race=Asian or race[]=Black&race[]=White.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Candidate Drop-Off Rate

Definition

Candidate drop-off rate tells you how many candidates drop from the recruitment process during a specific selection stage.

How we calculate this

We measure how many applications are rejected at each stage of your hiring pipeline(s) in absolute and relative terms. We also take into account who initiated the termination of the recruitment process and what was the reason behind it. Additionally, we utilize statistical procedures to extract potential insights.

Insight to

This metric helps you evaluate your hiring process by presenting information on number of drop-offs and reasons behind them. Reasons for drop-offs might indicate why are some stages too strict or not selective enough. Additionally, our statistical procedures will surface insight if some stages have a particularly high number of drop-offs initiated by candidates.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of recruiters. Examples recruiter=John Smith or recruiter[]=John Smith&recruiter[]=Alice Smith.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of races. Examples race=Asian or race[]=Black&race[]=White.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{ }

Time in Recruitment Stage

Definition

Time in Recruitment Stage tells you how long the candidates are staying in each step in your selection process.

How we calculate this

Each application goes through the stages of your hiring pipeline(s). We measure the time individuals spend in each stage and calculate the median, minimum and maximum value in days.

Insight to

This metric is here to help you discover potential hiring bottlenecks. Think of the ideal process and how much time you'd like your candidates to spend in each stage of the selection process. In the "all" view, you can explore if some stages keep your candidates waiting longer than the others. By changing views, you can highlight differences between recruitment stage velocities in particular departments, teams, or roles.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of recruiters. Examples recruiter=John Smith or recruiter[]=John Smith&recruiter[]=Alice Smith.

Array of strings or string

Calculate metric only for single or list of genders. Examples gender=Female or gender[]=Male&gender[]=Other.

Array of strings or string

Calculate metric only for single or list of races. Examples race=Asian or race[]=Black&race[]=White.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Talent Management

Talent Management analytics information access.

Courses enrolment & completion

Definition

This metric shows the number of employees who enrolled in a learning program and the ones who completed it.

How we calculate this

This metric is calculated by counting employees who enrol in a course and those who meet the requirements to consider the course as completed.

Source: LMS

Insight to

Looking at this data an organization can understand more about the usability of available learning programs by employees as well as their persistence in completing the courses. Once compared with performance data it can also be an indicator whether it is influencing positively employee output.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

External Talent Mobility

Definition

Employee Mobility shows the movement of employees inside and out of the organization. It provides an overview of the best internal talent pools and external hiring sources followed by internal employee churn and external turnover to talent competitors.

How we calculate this

This metric is calculated by extracting hiring sources with the most accepted candidates, as well as competitors who were most frequently mentioned by your leaving employees as their next employers.

Insight to

External Talent Mobility gives you an insight into the best talent sources which you can utilize in future instances, as well as your greatest competitors when it comes to talent outflow.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Internal Talent Mobility

Definition

Employee Mobility shows the movement of employees inside and out of the organization. It provides an overview of the best internal talent pools and external hiring sources followed by internal employee churn and external turnover to talent competitors.

How we calculate this

This metric is calculated by summing up the numbers of employees who leave a department for another department, and by summing up the numbers of employees who join a department from another department.

Insight to

Internal Talent Mobility gives you an insight into which departments act as talent pools for other departments, and which departments are the most sought after for internal transfers.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Learner Drop-Off Rate

Definition

This metric shows the number of employees who enrolled in a learning program but dropped out for some reason and didn’t complete the program.

How we calculate this

This metric is calculated by counting employees who enrolled in a course and dropped out without completing the course.

Source: LMS

Insight to

Looking at this data an organization can understand more about the usability of available learning programs by employees as well as their persistence in completing the courses. It can also be an indicator of how popular some learning courses are.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Assessment Scores

Definition

This metric shows the average score employees get on their learning courses.

How we calculate this

This metric is calculated average score of all participants on a specific course/learning program.

Source: LMS

Insight to

Looking at this data an organization can understand more about the performance of employees on learning courses and their engagement to complete the course.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Learning Impact

Definition

Learning Impact uses the Kirkpatrick’s four-level training evaluation model to define the impact a training program had on the employee and the organization:

  • reaction - the learner’s reaction and response to training,
  • learning - the knowledge ands skills learned during the training
  • behaviour - the behavioural change due to the training
  • impact - the training impact on business goals and results

How we calculate this

By calculating the average scores from the Learning Impact employee lifecycle survey.

Source: Survey

Insight to

This metric is a KPI framework for your training program’s effectiveness.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Amount of Time Invested in Learning

Definition

This metric shows the number of hours employees invested in training programs.

How we calculate this

This metric is calculated by number of hours needed to complete a course and/or reported study leaves.

Source: LMS & HRIS

Insight to

Amount of time invested in learning can be an indicator of a company culture and openness to invest in learning, workload on the employees and their ability to dedicate time to trainings as well as time employees spend on self-development. Ideally this data would be combined with performance data to check if the time invested in learning resulted in improved skills and therefore output.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Onboarding Expectations Management

Definition

Onboarding expectations management assessment helps you understand how well do you communicate what is expected of them at the job they were hired for, and whether they know who can they ask for support.

How we calculate this

Distribution of the employee's answers.

Insight to

This metric provides valuable insight to the efficiency of setting proper expectations about the role during the hiring process and what actually happens through the onboarding. It aims to validate whether these two match. A misalignment here might lead to disappointment and disengagement of the new hire.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Post-onboarding Assessment

Definition

Post-onboarding assessment is manager's review of the new employee overall performance and job fit at the end of the onboarding process.

How we calculate this

Distribution of the manager’s answers.

Insight to

A metric that is used to evaluate the effectiveness and efficiency of the hiring process as well as the performance of Recruiters. It is also used as an input information to determine a Quality of Hire.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Skillset Level at Hiring

Definition

Skillset level at hiring refers to the manager's assessment of whether our new hires are at the skill level expected to do the job they were hired for. Depending on the talent market competitiveness, companies may decide to hire candidates that are above or below the needed skill level. Use this metric to find your talent development gaps, assess time-to-productivity, and the overall quality of such hires.

How we calculate this

Distribution of the manager’s answers.

Insight to

Depending on the talent market competitiveness, companies may decide to hire candidates that are above or below the needed skill level. Use this metric to find your talent development gaps, assess Time to Productivity, and the overall quality of such hires.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Time to Productivity

Definition

Time to productivity is a measurement of tells you how long it takes for a new hire to contribute to the organization at the expectations level for their role.

Insight to

It can be used to predict when can you have a fully operational new hire as well as the efficiency of the hiring process, whether the estimated TTP met the actual TTP of a new hire.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Training Cost per Employee

Definition

This metric shows the cost that the organization has for training purposes.

How we calculate this

This metric is calculated by dividing the total training cost with number of employees you trained.

Source: LMS

Insight to

This data provides information about the organizational investment in learning per employee and the impact that cost will have on the bottom line. When compared with engagement survey results it can also be a strong indicator if the employees feel the company is investing in learning and development.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Rewards

Rewards analytics information access.

Bonus Distribution

Definition

Bonus Distribution shows the spread of bonus size per business unit and job role.

Insight to

Use this metric to understand the distribution of variable pay within organization and trends through time. To get a full insight on the efficiency of your compensation combine the results with Internal Compensation Equity metric.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Headcount Cost

Definition

Headcount Cost shows the total spend of the organization on employees. The total cost is split into different costs due to (1) current salaries, (2) promotions and (3) new hires. (4) Turnover Cost Deduction is the amount of money deducted due to turnover for a specific period.

How we calculate this

Total Cost = sum of monthly current employees’ salaries, promotions of current employees, and new hires’ salaries

Current Salaries Cost = sum of monthly current employees’ salaries

Promotions Cost = sum of salary increases for current employees

New Hires Cost = sum of new hires’ salaries

Turnover Cost Deduction = sum of salaries for leaving employees

Insight to

Since labor cost represents one of the highest operating expenses for any organization, this metric can be compared to total operating cost providing an insight into how much of your entire operating cost is being allocated to labor cost. To interpret results, dismantling what your rate means will heavily depend on your business values, practices and goals.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Internal Compensation Equity

Definition

Internal Compensation Equity shows the spread of pay per job role and seniority levels.

Insight to

Use this metric to ensure that employees within organization are paid fairly versus each other.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Min and Max Pay

Definition

Min and Max Pay is a metric indicating what is the company's minimum pay for the lowest grade and maximum for the highest grade in a business or demographic unit.

How we calculate this

Min and Max Pay is calculated by simply taking the lowest and highest monthly salary.

Insight to

It provides and insight to outliers when it comes to equity in compensation for the same demographic unit.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Pay Mix

Definition

Pay Mix represents the ratio of fixed pay in salary to other variable types of pay such as bonus and equity.

How we calculate this

Pay Mix is calculated as a ratio of sum of variable payments to sum of base salaries on a monthly level.

Insight to

This metrics shows you the distribution of different compensation elements in your overall across your company and can be used when planning your total rewards strategy.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Average Raise Percentage

Definition

Average Raise Percentage shows the average percentage of salary increases/decreases within a specific time frame.

How we calculate this

Average Raise Percentage(T) = Sum of all salary changes (increases and decreases) at time T / Number of salary changes at time T

Insight to

It provides an insight into the rate of salaries growth in your organization and headcount cost increase. It can be used for salary forecasting and benchmarking internally and externally as well as identifying outliers.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Turnover

Turnover analytics information access.

Avoidable Turnover

Definition

Avoidable Turnover indicates the percentage of employees that communicated their considerations to change a job with superiors or HR Team.

How we calculate this

Avoidable Turnover is calculated as a ratio of number of employees leaving who communicated their exit consideration, and total number of employees voluntarily leaving the company in a given period.

Insight to

This metric is often observed alongside Exit Consideration to understand how proactively does the organization react once the employee starts considering leaving.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Ethical Reasons Distribution

Definition

Exits Due to Ethical Reasons metric indicates the number of employees who reported that they left the company due to ethical concerns.

How we calculate this

Number and percentage of employees who left the organization answering positively on the questions from Exit Surveys related to offboarding due to ethical reasons.

Insight to

Research shows that ethical leadership practices have an impact on employees' job outcomes and their willingness to stay. This metric gives insight into whether employees were motivated to leave the company due to the presence of ethical issues related to the work environment, other employees, or the job itself. This might be indicative of work practices that should be improved immediately.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Exit Reasons Distribution

Definition

Exit Reasons Distribution helps you structure and understand your ex-employees' self-reported reasons for leaving the company.

How we calculate this

Exit Reasons Distribution is calculated by a simple count of answers from a subset of questions from Exit Surveys filled by your leaving employees, represented as a heatmap.

Insight to

The metric explores what your employees expect to gain or lose with the change of the employer. Values marked as ‘Gain a lot’ represent the ones that might be a current challenge in your organization.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Experience Loss

Definition

Experience Loss represents the total amount of employee tenure lost in a given period as a result of voluntary turnover.

How we calculate this

Experience Loss for a given period is calculated by adding up tenures of employees leaving voluntarily the company in that period, expressed in years.

Insight to

Experience Loss shows how many years of internal knowledge you lost and it can be an indicator of costs related to turnover in reduced productivity and a need for replacement hiring. The quality of experience is also important so you should explore break down by performance rating and other employee segments.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Number of Leavers

Definition

Number of contract terminations provides an overview of employees exiting the organization. It shows all leavers regardless if they resigned or their contract was terminated by the company.

How we calculate this

Number of Leavers is a simple count of employees leaving the company for a given period.

Insight to

It provides an insight into the attrition of organizational headcount and the workload on offboarding employees. Understanding these numbers in time context, per department, location etc. is essential to uncover outliers and spikes. Complement this data with exit surveys to get a deeper view on the reasons behind these leaves.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Regrettable Exits

Definition

Regrettable Exits are the ones when an employee's departure from a company has a negative impact on the team. It is measured by the direct manager's willingness to rehire employees and the assessment of competency loss that needs to be replaced in the team.

How we calculate this

Regrettable Exits is calculated as a ratio of number of employees leaving the company, who are also eligible for rehiring as indicated by their direct managers, and total number of employees leaving the company in a given period.

Insight to

Organizational ability to retain talent. To create a strategy of retention and prevent such leaves in the future HR can observe reasons for leaving provided through the exit process.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Turnover Rate

Definition

This metric shows the absolute number of contract terminations (resignations and terminations) and turnover rate over time. The Turnover Rate is the percentage of employees who left the company in the observed period of time. Unknown type can appear due to data health problems within your HRIS.

How we calculate this

Count employees leaving the company (voluntary and involuntary) for a given period. The Turnover Rate is calculated by dividing the number of employees that left the company by the total number of employees in a given period. The Unknown label shows only if you didn't enter an exit type in a dedicated filed in your HRIS.

Insight to

Attrition overview of your headcount indicating talent loss trends. It also shows the workload of HR on offboarding employees. Understanding these numbers in the context of time and demographics is essential to uncover outliers and spikes in turnover. Complement this data with exit surveys to get a deeper view of the reasons behind these leaves and why do you lose talent.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of employment types. Examples employment_type=Full Time or employment_type[]=Part Time&employment_type[]=Internship.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

string

Calculate metric for critical people. Possible values for filter are Critical people or Others. Examples critical_people=Others.

string

Calculate metric for critical people. Possible values for filter are Top talent or Others. Examples top_talent=Others.

Array of strings or string

Calculate metric based on performance rating.

Array of strings or string

Calculate metric based on grade cluster.

Array of strings or string

Calculate metric based on last enps score.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Leadership

Leadership analytics information access.

Leadership Promotions

Definition

Leadership Promotions shows the total number of managers per month, with an information on the number of managers who were promoted within the company and the number of managers who were hired directly into managerial roles. This metric can also be observed from the standpoint of top, middle, and front-line leadership promotions.

How we calculate this

This metric is calculated by counting the number of managers who were in managerial positions from their start date (i.e. hired into managerial positions) and the number of managers who had non-managerial roles beforehand (i.e. promoted into managerial positions).

Insight to

This metric helps to identify the dis-balance between number of managers who are hired directly into managerial roles and the number of managers who were promoted within the company.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of roles. Examples role=HR Consultant or role[]=Designer&role[]=QA Engineer.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

Array of strings or string

Calculate metric only for single or list of tenure groups. Available tenure groups: 0-1 year, 1-2 year(s), 2-3 years, 3-4 years, 4-5 years, 5-10 years, > 10 years.

Examples tenure=2-3 years or tenure[]=4-5 years&tenure[]=5-10 years.

Array of strings or string

Calculate metric only for single or list of age groups. Available age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50+.

Examples age=40-44 or age[]=25-29&age[]=35-39.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Organizational Depth

Definition

Organizational Depth indicates the extent of company layering by showing the number of organizational levels.

How we calculate this

Organizational Depth (T) = Number of organizational levels at time T

Insight to

It indicates organizational complexity and hierarchical structure.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

Array of strings or string

Calculate metric only for single or list of locations. Examples location=Berlin or location[]=London&location[]=Paris.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Team Dispersion

Definition

Team dispersion shows how geo-distributed your teams are. The closer the number is to 0 your organizational unit would be more collocated. Inversely, the closer the number is to 100%, it would mean that your company is completely distributed.

How we calculate this

100*(1 - Sum (Proportion of individuals in Location L)² for each Location L)

Insight to

Team dispersion provides insights into your organizational structure by exploring the geo remoteness of your teams and departments. More dispersed teams usually bring into the organization strong diversity bonuses and the quality-of-work payoffs are usually high, but are often harder to manage due to their complex nature. Geo-agnostic teams require tighter team norms, structured employee onboarding, and managed cultural brokerage.

Authorizations:
ApiKey
query Parameters
date_from
required
string
Example: date_from=2022-01-01

Sets the start date for calculation. Format YYYY-MM-DD.

date_to
required
string <YYYY-MM-DD>
Example: date_to=2022-12-31

Sets the end date for calculation. Format YYYY-MM-DD.

Array of strings or string

Calculate metric only for single or list of teams. Examples team=Frontend or team[]=Sales&team[]=Engineering.

Array of strings or string

Calculate metric only for single or list of departments. Examples department=Customer Support or department[]=Sales&department[]=Engineering.

group_by
string
Example: group_by=department

Group metric calculations by one of demographic filters defined above.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

Insights

Insights information access.

Current Pay Gap Insight

The Pay Gap Insight warns you about possible pay disparities between men and women in your company at various career stages, in various locations, and in different organizational structures. The Current Pay Gap Insight tells you about differences in salaries between your male and female employees at the current moment. The Current Pay Gap Insight is calculated using a statistical algorithm that takes into account a number of variables that could explain some of the observed differences (e.g. tenure, age, number of leaves).

Authorizations:
ApiKey
query Parameters
department
string
Example: department=Marketing

Fetch Current Pay Gap Insight for a single department.

team
string
Example: team=Customer Service

Fetch Current Pay Gap Insight for a single team.

location
string
Example: location=London

Fetch Current Pay Gap Insight for a single location.

leadership_level
string
Enum: "Front line" "Middle" "Top"
Example: leadership_level=Top

Fetch Current Pay Gap Insight for a single leadership level.

Responses

Response samples

Content type
application/json
{
  • "analytics": {
    },
  • "descriptive": {
    }
}

The Offers Pay Gap Insight

The Pay Gap Insight warns you about possible pay disparities between men and women in your company at various career stages, in various locations, and in different organizational structures. The Offers Pay Gap Insight tells you about differences in first salaries your male and female employees are offered when they start working at your organization. The Offers Pay Gap Insight is calculated using a statistical algorithm that takes into account a number of variables that could explain some of the observed differences (e.g. tenure, age, number of leaves).

Authorizations:
ApiKey
query Parameters
department
string
Example: department=Marketing

Fetch Offers Pay Gap Insight for a single department.

team
string
Example: team=Customer Service

Fetch Offers Pay Gap Insight for a single team.

location
string
Example: location=London

Fetch Offers Pay Gap Insight for a single location.

leadership_level
string
Enum: "Front line" "Middle" "Top"
Example: leadership_level=Top

Fetch Offers Pay Gap Insight for a single leadership level.

Responses

Response samples

Content type
application/json
{
  • "analytics": {
    },
  • "descriptive": {
    }
}

Salary Increase Pay Gap Insight

The Pay Gap Insight warns you about possible pay disparities between men and women in your company at various career stages, in various locations, and in different organizational structures. The Salary Increase Pay Gap tells you about differences in salary hikes between your male and female employees. The Salary Increase Pay Gap Insight is calculated using a statistical algorithm that takes into account a number of variables that could explain some of the observed differences (e.g. tenure, age, number of leaves).

Authorizations:
ApiKey
query Parameters
department
string
Example: department=Marketing

Fetch Salary Increases Pay Gap Insight for a single department.

team
string
Example: team=Customer Service

Fetch Salary Increases Pay Gap Insight for a single team.

location
string
Example: location=London

Fetch Salary Increases Pay Gap Insight for a single location.

leadership_level
string
Enum: "Front line" "Middle" "Top"
Example: leadership_level=Top

Fetch Salary Increases Pay Gap Insight for a single leadership level.

Responses

Response samples

Content type
application/json
{
  • "days_between_increases": {
    },
  • "increase_amount": {
    },
  • "relative_increase_amount": {
    }
}

Turnover Insight

The Turnover Insight tells you about factors often associated with turnover in your organization across different locations, teams, and departments. This insight looks into historical data on turnover and other factors temporally associated with it and surfaces turnover patterns. This insight is calculated using a machine learning model trained and validated on your data.

Authorizations:
ApiKey
query Parameters
department
string
Example: department=Marketing

Fetch Turnover Drivers Insight for a single department.

team
string
Example: team=Customer Service

Fetch Turnover Drivers Insight for a single team.

location
string
Example: location=London

Fetch Turnover Drivers Insight for a single location.

Responses

Response samples

Content type
application/json
{
  • "analytics": {
    },
  • "descriptives": {
    }
}

Who's at risk of leaving?

Definition

The probability of leaving indicates the expected probability that an employee will leave the company at the specific year of tenure.

How we calculate this?

The probability an employee will leave the company after a T period of time is the product of number of employees left at time Ti / number of employees not left until Ti for all previous Ti period of times.

Insight to

When analyzed for all employees, it provides an insight into a general turnover with regards to tenure. When analyzed per different filters, it provides an insight into groups that are at high immediate risk of leaving the company. Examining a specific group will give further insights into the turnover probability of employees from that group with respect to their tenure.

Authorizations:
ApiKey
query Parameters
department
string
Example: department=Marketing

Fetch Risk of Leaving Insight for a single department.

team
string
Example: team=Customer Service

Fetch Risk of Leaving Insight for a single team.

gender
string
Example: gender=Male

Fetch Risk of Leaving Insight for a single gender.

age
string (Age)
Enum: "18-24" "25-29" "30-39" "40-49" "50+" "Unknown"
Example: age=18-24

Fetch Risk of Leaving Insight for a single age group.

Responses

Response samples

Content type
application/json
{
  • "group1": {
    },
  • "group2": {
    }
}

The Hiring Bottleneck Insight

The Hiring Bottleneck Insight warns you about applicants who have spent more time in a particular stage of your hiring pipeline compared to the historical trends for your organization. This insight offers a view into which stage applicants are currently in, how many days they have already spent in that stage, as well as the expected number of days each applicant should spend in that stage.

Authorizations:
ApiKey

Responses

Response samples

Content type
application/json
{
  • "items": [
    ]
}

The Candidate Drop-Off Insight

The Candidate Drop-Off Insight warns you about any significant trends in the number of candidates dropping out of your recruitment pipeline. This insight is calculated using various statistical techniques to identify stages with unusually high drop-off rates, as well as what those stages should look like based on your data.

Authorizations:
ApiKey

Responses

Response samples

Content type
application/json
{
  • "total_time_insight": 0,
  • "affected_stages": {
    }
}

Burnout Risk Analysis

Explore the most relevant characteristics of your employees that predict the likelihood of employee burnout. Characteristics are explored using our machine learning models trained and validated on your data. The negative impact on burnout means that employees who display specified characteristics and experiences have higher chances of experiencing burnout. The positive impact on burnout means that employees who display specified characteristics and experiences have lower chances of experiencing burnout.

Authorizations:
ApiKey
query Parameters
survey_date
string
Example: survey_date=2022-01-01

Fetch Burnout Analysis for a single survey.

Responses

Response samples

Content type
application/json
{
  • "items": [
    ]
}

Employee Engagement Analysis

Explore the most relevant characteristics of your employees that predict the likelihood of employee engagement. Characteristics are explored using our machine learning models trained and validated on your data. The negative impact on engagement means that employees who display specified characteristics and experiences have lower chances of being highly engaged. The positive impact on enagement means that employees who display specified characteristics and experiences have higher chances of being highly engaged.

Authorizations:
ApiKey
query Parameters
survey_date
string
Example: survey_date=2022-01-01

Fetch Employee Engagement Analysis for a single survey.

Responses

Response samples

Content type
application/json
{
  • "items": [
    ]
}

eNPS Text Summarization

eNPS Text Summarization offers you a view into the most important themes within open-ended question related to an employee’s particular net promoter score. Our machine learning model extracts the five most important answers within each eNPS group - promoters, passives, and detractors.

Authorizations:
ApiKey
query Parameters
survey_date
string
Example: survey_date=2022-01-01

Fetch eNPS text summarization for a single survey

Responses

Response samples

Content type
application/json
{
  • "items": [
    ]
}

Employee

employee_id
string

Employee's ID from HRIS.

first_name
string

Employee's first name.

last_name
string

Employee's last name.

full_name
string

Employee's full name.

email
string

Employee's email.

location
string

Employee's location.

department
string

Employee's department.

role
string

Employee's role.

team
string

Employee's team.

gender
string

Employee's gender.

age
integer

Employee's age in years.

nationality
string

Employee's nationality.

ethnicity
string

Employee's ethnicity.

object

Employee's compensation information.

object

Current employment status for an employee.

object

Employee’s current supervisor.

division
string

Employee's division.

branch
string

Employee's branch.

avatar
string or null

Employee's avatar url.

functional_level
string or null

Employee’s functional level in the organization.

tenure
integer

Employee's tenure in years.

custom_fields
object

All custom fields for the organization.

grade
string

Employee's grade.

generation
string

Employee's generation.

previous_company
string

Employee's previous company.

performance_rating
string

Employee's performance rating.

{
  • "employee_id": "string",
  • "first_name": "string",
  • "last_name": "string",
  • "full_name": "string",
  • "email": "string",
  • "location": "string",
  • "department": "string",
  • "role": "string",
  • "team": "string",
  • "gender": "string",
  • "age": 0,
  • "nationality": "string",
  • "ethnicity": "string",
  • "compensation": {
    },
  • "status": {
    },
  • "reports_to": {
    },
  • "division": "string",
  • "branch": "string",
  • "avatar": "string",
  • "functional_level": "string",
  • "tenure": 0,
  • "custom_fields": { },
  • "grade": "string",
  • "generation": "string",
  • "previous_company": "string",
  • "performance_rating": "string"
}

Employment History

employee_id
string

Employee ID from HRIS.

start_date
string <date-time>

Employment history effective start date.

end_date
string or null <date-time>

Employment history effective end date.

employment_type
string or null

Type of employment.

object
{
  • "employee_id": "string",
  • "start_date": "2019-08-24T14:15:22Z",
  • "end_date": "2019-08-24T14:15:22Z",
  • "employment_type": "string",
  • "termination": {
    }
}

Job History

employee_id
string

Employee's ID from HRIS.

start_date
string <date-time>

Job change effective date.

location
string or null

Employee's location.

department
string or null

Employee's department.

team
string or null

Employee's team.

role
string or null

Employee's role.

{
  • "employee_id": "string",
  • "start_date": "2019-08-24T14:15:22Z",
  • "location": "string",
  • "department": "string",
  • "team": "string",
  • "role": "string"
}

Salary History

employee_id
string

Employee ID from HRIS.

effective_from
string or null <date-time>

Salary history effective start date.

effective_to
string or null <date-time>

Salary history effective end date.

pay_period
string
Enum: "UNKNOWN" "HOUR" "DAY" "WEEK" "TWO_WEEKS" "MONTH" "HALF_A_MONTH" "QUARTER" "YEAR" "PAY_PERIOD" "PIECE" "TWICE_A_MONTH" "HALF_YEAR"

Payment period.

amount
number or null

Salary amount.

currency
string or null

Salary currency.

{
  • "employee_id": "string",
  • "effective_from": "2019-08-24T14:15:22Z",
  • "effective_to": "2019-08-24T14:15:22Z",
  • "pay_period": "UNKNOWN",
  • "amount": 0,
  • "currency": "string"
}

Bonus

employee_id
string

Employee ID from HRIS.

effective_date
string <date-time>

Bonus effective date.

amount
number or null

Bonus amount.

currency
string or null

Bonus currency.

{
  • "employee_id": "string",
  • "effective_date": "2019-08-24T14:15:22Z",
  • "amount": 0,
  • "currency": "string"
}

Application

application_id
string

Application ID from ATS.

candidate_id
string or null

Candidate ID from ATS.

posting_id
string or null

Posting ID from ATS.

name
string or null

Candidate name.

object or null

Application referral information.

stage_id
string or null

Current Stage ID.

stage_changed_at
string or null <date-time>

Last time the stage was changed.

hired
boolean

Whether the candidate has been hired or not.

hired_at
string or null <date-time>

Candidate hire time.

object or null

Reason this application was archived.

archived_at
string or null <date-time>

Application archive time.

created_at
string or null <date-time>

Application create time.

rejected_at
string or null <date-time>

Application reject time.

location
string or null

Application location.

department
string or null

Application department.

role
string or null

Application role.

team
string or null

Application team.

gender
string or null

Applicant gender.

race
string or null

Applicant gender.

origin
string or null

Application origin.

sources
Array of strings

Application source.

recruiter_id
string or null

Application recruiter ID.

Array of objects

List of all application stage changes.

last_activity_at
string or null

Application last activity time.

candidate_location
string or null

Application candidate location.

{
  • "application_id": "string",
  • "candidate_id": "string",
  • "posting_id": "string",
  • "name": "string",
  • "application_referral": {
    },
  • "stage_id": "string",
  • "stage_changed_at": "2019-08-24T14:15:22Z",
  • "hired": true,
  • "hired_at": "2019-08-24T14:15:22Z",
  • "archive_reason": {
    },
  • "archived_at": "2019-08-24T14:15:22Z",
  • "created_at": "2019-08-24T14:15:22Z",
  • "rejected_at": "2019-08-24T14:15:22Z",
  • "location": "string",
  • "department": "string",
  • "role": "string",
  • "team": "string",
  • "gender": "string",
  • "race": "string",
  • "origin": "string",
  • "sources": [
    ],
  • "recruiter_id": "string",
  • "stage_changes": [
    ],
  • "last_activity_at": "string",
  • "candidate_location": "string"
}

Offer

offer_id
string or null

Offer ID from ATS.

application_id
string

Application ID from ATS.

sent_at
string or null <date-time>

Sent time.

signed
boolean or null

Whether the offer has been signed.

signed_at
string or null <date-time>

Offer signed time.

rejected
boolean or null

Whether the offer has been rejected.

rejected_at
string or null <date-time>

Offer rejection time.

location
string or null

Offer location.

department
string or null

Offer department.

role
string or null

Offer role.

team
string or null

Offer team.

{
  • "offer_id": "string",
  • "application_id": "string",
  • "sent_at": "2019-08-24T14:15:22Z",
  • "signed": true,
  • "signed_at": "2019-08-24T14:15:22Z",
  • "rejected": true,
  • "rejected_at": "2019-08-24T14:15:22Z",
  • "location": "string",
  • "department": "string",
  • "role": "string",
  • "team": "string"
}

Posting

posting_id
string or null

Posting ID from the ATS.

recruiter_id
string

Recruiter ID from the ATS.

pipeline_id
string

Recruiter ID from the ATS.

title
string or null

Posting title.

created
string or null <date-time>

Time the posting was created.

published
string or null <date-time>

Time the posting was published.

closed_at
string or null <date-time>

Time the posting was closed.

location
string or null

Posting location.

department
string or null

Posting department.

role
string or null

Posting role.

team
string or null

Posting team.

{
  • "posting_id": "string",
  • "recruiter_id": "string",
  • "pipeline_id": "string",
  • "title": "string",
  • "created": "2019-08-24T14:15:22Z",
  • "published": "2019-08-24T14:15:22Z",
  • "closed_at": "2019-08-24T14:15:22Z",
  • "location": "string",
  • "department": "string",
  • "role": "string",
  • "team": "string"
}

Candidate

candidate_id
string

Candidate ID from the ATS.

name
string or null

Candidate name.

referred
boolean or null

Whether the candidate has been referred.

sourced
boolean or null

Whether the candidate has been sourced.

{
  • "candidate_id": "string",
  • "name": "string",
  • "referred": true,
  • "sourced": true
}

Interview

interview_id
string

Interview ID from the ATS.

stage
string or null

Interview stage.

candidate_id
string

Candidate ID from the ATS.

candidate_name
string

Candidate name.

interview_date
string or null <date-time>

Interview date.

Array of objects

Interviewers.

location
string or null

Interview location.

department
string or null

Interview department.

role
string or null

Interview role.

team
string or null

Interview team.

{
  • "interview_id": "string",
  • "stage": "string",
  • "candidate_id": "string",
  • "candidate_name": "string",
  • "interview_date": "2019-08-24T14:15:22Z",
  • "interviewers": [
    ],
  • "location": "string",
  • "department": "string",
  • "role": "string",
  • "team": "string"
}

Requisition

requisition_id
string

Requisition ID from the ATS.

created_at
string or null <date-time>

Time the requisition was created.

closed_at
string or null <date-time>

Requisition close time and date.

status
string or null

Requisition status.

name
string or null

Requisition name.

headcount_total
integer or null

Total required headcount.

headcount_hired
integer or null

Total hired.

headcount_infinite
boolean or null

Indicates whether this requisition is for hiring an undefined or unknown number of candidates.

location
string or null

Requisition location.

department
string or null

Requisition department.

role
string or null

Requisition role.

team
string or null

Requisition team.

posting_ids
Array of strings

Postings connected with this requisition.

object

Salary range for this requisition.

{
  • "requisition_id": "string",
  • "created_at": "2019-08-24T14:15:22Z",
  • "closed_at": "2019-08-24T14:15:22Z",
  • "status": "string",
  • "name": "string",
  • "headcount_total": 0,
  • "headcount_hired": 0,
  • "headcount_infinite": true,
  • "location": "string",
  • "department": "string",
  • "role": "string",
  • "team": "string",
  • "posting_ids": [
    ],
  • "salary_range": {
    }
}

Recruiter

recruiter_id
string

Recruiter ID.

recruiter_name
string

Recruiter name.

{
  • "recruiter_id": "string",
  • "recruiter_name": "string"
}

Interview Stage

stage_id
string

Interview Stage ID.

name
string or null

Interview Stage name.

stage_type
string
Enum: "UNKNOWN" "INBOX" "IN_PROCESS" "TERMINAL"

Interview Stage type.

{
  • "stage_id": "string",
  • "name": "string",
  • "stage_type": "UNKNOWN"
}

Pipeline

pipeline_id
string

Pipeline ID.

Array of objects
{
  • "pipeline_id": "string",
  • "stages": [
    ]
}

Feedback

feedback_id
string or null

Feedback ID from the ATS.

posting_id
string

Posting ID from the ATS.

application_id
string

Application ID from the ATS.

candidate_id
string

Candidate ID from the ATS.

stage
string or null

Feedback stage.

created_at
string or null <date-time>

Time feedback was created.

rating
string or null

Candidate rating.

object or null

Candidate reviewer.

location
string or null

Feedback location.

department
string or null

Feedback department.

role
string or null

Feedback role.

team
string or null

Feedback team.

{
  • "feedback_id": "string",
  • "posting_id": "string",
  • "application_id": "string",
  • "candidate_id": "string",
  • "stage": "string",
  • "created_at": "2019-08-24T14:15:22Z",
  • "rating": "string",
  • "reviewer": {
    },
  • "location": "string",
  • "department": "string",
  • "role": "string",
  • "team": "string"
}

PMS User

user_id
string or null

User’s ID from the PMS.

email
string or null

User's email.

first_name
string or null

User's first name.

last_name
string or null

User's last name.

{
  • "user_id": "string",
  • "email": "string",
  • "first_name": "string",
  • "last_name": "string"
}

PMS Feedback Session

feedback_session_id
string or null

Feedback Session ID.

scheduled_at
string or null <date-time>

Feedback session time.

object or null

Feedback session participants.

session_type
string or null
Enum: "UNKNOWN" "PEER" "MANAGER_EMPLOYEE" "MANAGER_MANAGER"

Feedback session type.

{
  • "feedback_session_id": "string",
  • "scheduled_at": "2019-08-24T14:15:22Z",
  • "participants": {
    },
  • "session_type": "UNKNOWN"
}

PMS Peer Recognition

peer_recognition_id
string or null

Peer Recognition ID from the PMS.

created_at
string or null <date-time>

Peer Recognition created time.

content
string or null

Peer Recognition content.

giver
string or null

Peer Recognition giver.

receiver
string or null

Peer Recognition recipient.

{
  • "peer_recognition_id": "string",
  • "created_at": "2019-08-24T14:15:22Z",
  • "content": "string",
  • "giver": "string",
  • "receiver": "string"
}

PMS Objective

objective_id
string or null

Objective ID from the PMS.

parent_id
string or null

Objective Parent ID from the PMS.

user_id
string or null

Objective User ID from the PMS.

team
string or null

PMS Objective team.

description
string or null

PMS Objective description.

scope
string or null
Enum: "UNKNOWN" "COMPANY_WIDE" "DEPARTMENT" "INDIVIDUAL" "SELF_DEVELOPMENT"

PMS Objective scope.

start_date
string or null <date-time>

PMS Objective start date.

end_date
string or null <date-time>

PMS Objective end time.

completion_progress
number or null

PMS Objective completion progress.

Array of objects or null

PMS Objective key results.

tags
Array of strings

PMS Objective tags.

is_archived
boolean

Whether the PMS Objective is archived.

is_closed
boolean

Whether the PMS Objective is closed.

is_future
boolean

Whether the PMS Objective is in the future.

is_active
boolean

Whether the PMS Objective is active.

is_past_due
boolean

Whether the PMS Objective is past due.

{
  • "objective_id": "string",
  • "parent_id": "string",
  • "user_id": "string",
  • "team": "string",
  • "description": "string",
  • "scope": "UNKNOWN",
  • "start_date": "2019-08-24T14:15:22Z",
  • "end_date": "2019-08-24T14:15:22Z",
  • "completion_progress": 0,
  • "key_results": [
    ],
  • "tags": [
    ],
  • "is_archived": true,
  • "is_closed": true,
  • "is_future": true,
  • "is_active": true,
  • "is_past_due": true
}

PMS Review

review_id
string

Review ID.

reviewer_id
string or null

Review ID.

reviewee_id
string or null

Review ID.

review_type
string
Enum: "UNKNOWN" "SELF_REVIEW" "PEER_REVIEW" "DOWNWARD_REVIEW" "UPWARD_REVIEW"

Type of review.

transparency
string or null
Enum: "UNKNOWN" "ANONYMOUS" "ANONYMOUS_TO_MANAGER" "ANONYMOUS_TO_REVIEWEE" "TRANSPARENT"

Transparency of review.

theme
string or null

Review theme.

Array of objects
due_date
string or null <date-time>

Review due date.

completed_on
string or null <date-time>

Review completed date.

{
  • "review_id": "string",
  • "reviewer_id": "string",
  • "reviewee_id": "string",
  • "review_type": "UNKNOWN",
  • "transparency": "UNKNOWN",
  • "theme": "string",
  • "questions": [
    ],
  • "due_date": "2019-08-24T14:15:22Z",
  • "completed_on": "2019-08-24T14:15:22Z"
}

Data Health

id
string

Attribute Data Health unique identifier.

run_id
string

Unique identifier of a particular Data Health run. Data Health runs periodically and in one run each available attribute is checked for health.

capability_id
string

The unique identifier of the attribute.

sample_size
number

Number of checked data samples for a given attribute.

healthy_samples
number

Number of healthy data samples for a given attribute (ex. The value is considered valid).

unhealthy_samples
number

Number of unhealthy data samples for a given attribute (ex. The value is considered invalid or not set).

created_at
string <date-time>

Date and time when the attribute data health was calculated.

capability_supported
boolean

Whether capability is supported by HR tool used.

Array of objects or null

Context about attribute health, e.g. list of primary identifiers for unhealthy entities.

object

Some basic information about the data attribute (name, description etc.).

{
  • "id": "string",
  • "run_id": "string",
  • "capability_id": "string",
  • "sample_size": 0,
  • "healthy_samples": 0,
  • "unhealthy_samples": 0,
  • "created_at": "2019-08-24T14:15:22Z",
  • "capability_supported": true,
  • "unhealthy_samples_context": [
    ],
  • "capability": {
    }
}