vivainsights.identify_churn

Identify and count employees who have churned from or joined the dataset.

This is done by measuring whether an employee who is present in the first n (n1) weeks of the data, is also present in the last n (n2) weeks of the data. An additional use case of this function is the ability to identify “new-joiners” by using the argument flip.

vivainsights.identify_churn.identify_churn(data, n1=6, n2=6, return_type='message', flip=False, date_column='MetricDate', date_format='%Y-%m-%d')[source]

Identify employees who have churned from or joined the dataset.

Measures whether employees present in the first n1 weeks are still present in the last n2 weeks. Set flip=True to identify new joiners instead of churned employees.

Parameters:

  • data (pandas.DataFrame) – Person query data.

  • n1 (int, default 6) – Number of initial weeks to check for presence.

  • n2 (int, default 6) – Number of final weeks to check for presence.

  • return_type (str, default "message") – "message" prints a diagnostic, "text" returns it as a string, "data" returns the set of matching PersonId values.

  • flip (bool, default False) – If True, identify new joiners rather than churned employees.

  • date_column (str, default "MetricDate") – Name of the date column.

  • date_format (str, default "%Y-%m-%d") – strftime format of dates in date_column.

Returns:

A printed message, a diagnostic string, or a set of PersonId values depending on return_type.

Return type:

None, str, or set

Examples

Return a diagnostic text summary:

>>> import vivainsights as vi
>>> pq_data = vi.load_pq_data()
>>> vi.identify_churn(pq_data, return_type="text")

Return the set of churned PersonIds:

>>> vi.identify_churn(pq_data, return_type="data")

Flip the logic to detect employees who appear only in later weeks:

>>> vi.identify_churn(pq_data, flip=True, return_type="text")

Customize the number of boundary weeks to compare:

>>> vi.identify_churn(pq_data, n1=3, n2=3, return_type="text")