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Employee attrition is expensive — recruiting, onboarding, and lost productivity can cost 50-200% of an employee’s annual salary. Most HR teams track attrition reactively, compiling spreadsheets after the fact. By the time a pattern is visible, the damage is done. Wayak connects your HRIS data, runs Python-powered trend analysis in playbooks, and delivers monthly attrition reports with breakdowns by department, tenure band, job level, and manager. Your HR leadership team gets the signals they need to intervene early, backed by data instead of anecdotes.

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What you need

Data sources

  • HRIS database — Employee records with hire date, termination date, department, job level, manager, compensation band, and exit reason
  • Engagement survey database — Survey responses with eNPS scores, satisfaction ratings, and open-ended feedback timestamps
  • Performance review database — Review scores and promotion history

Knowledge spaces

  • Exit interview summaries — Anonymized exit interview notes uploaded as documents for thematic analysis
  • Retention playbooks — Internal guides on retention strategies, stay interview templates, and compensation benchmarking references
Semantic layer: Define these in your ontology before setting up the agent.
See building a semantic layer for a step-by-step guide.

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Agent setup

1

Create the agent

Go to Agent Space → New agent.
2

Set the description

You analyze workforce data to identify attrition trends and risk factors. Present findings with specific numbers — attrition rates, tenure distributions, and department comparisons. Avoid speculation; base every insight on data. When recommending interventions, reference industry benchmarks or internal retention playbooks. Use a neutral, analytical tone appropriate for leadership audiences. Always include period-over-period comparisons so trends are visible.
3

Scope data access

Grant access to:
  • HRIS database (employees, terminations, departments, compensation bands)
  • Engagement survey database (eNPS scores, satisfaction ratings)
  • Performance review database (review scores, promotion history)
  • Exit interview summaries knowledge space
  • Retention playbooks knowledge space
  • Employee object, AttritionRate and AverageTenure metrics, TenureBand and ExitReason dimensions
4

Add skills

Trigger: HR leader asks about attrition trends or monthly review
  1. Pull all terminations for the requested period from the HRIS.
  2. Calculate the attrition rate for each segment: department, tenure band, job level, and manager.
  3. Compare each segment’s rate against the company average and the prior period.
  4. Identify the top 3 segments with the highest attrition and the top 3 with the largest increase.
  5. For each high-attrition segment, break down by exit reason.
  6. Search exit interview summaries for recurring themes in those segments.
  7. Present findings as a ranked table with rates, trends, and key themes.
Trigger: HR leader requests a risk assessment
  1. Query the HRIS for all active employees.
  2. For each employee, gather: tenure, time since last promotion, latest engagement survey score, performance review trend, and compensation band relative to role midpoint.
  3. Flag employees with three or more risk indicators: low eNPS, no promotion in 2+ years, below-midpoint compensation, declining performance reviews.
  4. Group flagged employees by department and manager.
  5. Recommend targeted actions: stay interviews, compensation reviews, or development conversations.

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Automation

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Playbook: Monthly attrition report

1

Set the trigger

Schedule: First business day of each month at 9:00 AM.
2

Build the workflow

  1. Query the HRIS for all terminations in the previous calendar month and calculate the monthly attrition rate.
  2. Query the same data for the prior 12 months to build a trend line.
  3. Run Python analysis to calculate:
    • Month-over-month and year-over-year attrition rate changes.
    • Rolling 3-month average by department.
    • Tenure distribution of departures (median, mean, standard deviation).
    • Correlation between engagement survey scores and attrition by department.
  4. Condition: If any department’s rolling 3-month attrition rate exceeds 20% annualized, flag it as “critical” and include a deep-dive section.
  5. Query exit interview summaries for the flagged departments and extract the top 3 themes.
The trend analysis step uses a Python code block to compute rolling averages, standard deviations, and correlation coefficients. You can adjust the lookback window and the critical threshold in the code.
3

Configure delivery

Send an email to the HR leadership team and the CHRO with the subject line: “Monthly attrition report — [Month Year]”. Attach a formatted summary with charts. Post a condensed version to the #people-analytics Slack channel.
4

Test and activate

Click Run now to test with live data, then toggle to Active.

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What’s next

Policy Q&A

Pair attrition analysis with policy transparency — employees who understand their benefits are more likely to stay.

All HR use cases

See the full list.