Improve Team Retention

Proof required

Submit: (1) a baseline voluntary turnover calculation for the past 12 months: number of voluntary departures, average team size, calculated voluntary turnover rate, and the data source; (2) exit interview synthesis or equivalent evidence (stay interview summaries, anonymised 1-on-1 feedback, engagement survey open-text responses) covering at least 3 data points — what were the stated or implied reasons people left or considered leaving; (3) a retention risk diagnosis (200 words minimum): the 1–2 dominant risk factors based on the exit data, and a pre-committed intervention with the specific change you will implement to address the dominant risk factor.

What gets checked

• Voluntary turnover calculation uses a clear methodology — include the formula used, the data source for departure counts, and the time period; a single figure with no supporting data is not a baseline
• Exit data is genuine — a diagnosis based on 'I think people left because of X' without exit interview evidence or equivalent is speculation; minimum 3 data points (each a departed employee's stated reason, or a stay interview response, or an engagement survey open-text)
• Intervention is specific to the dominant risk factor — 'improve culture' does not address a specific risk factor; 'implement quarterly career development conversations with a written growth plan for each team member' addresses a risk factor of limited career visibility

Common mistakes

• Conflating involuntary and voluntary turnover — performance-based departures, layoffs, and voluntary resignations are different signals; the baseline must isolate voluntary departures only
• Diagnosing compensation as the root cause when it is a symptom — compensation dissatisfaction often masks a deeper driver (no promotion path, lack of recognition, poor management); a diagnosis that goes no deeper than 'people want more money' will produce an intervention that does not address the real cause
• Setting the pre-committed intervention before diagnosing the risk factor — an intervention decided before the exit data is reviewed is a hypothesis, not a diagnosis-driven intervention; the M1 evidence must clearly precede and inform the intervention choice

Resources

What a verifier looks for

• Turnover methodology: ask the submitter to state the formula they used and the data source — '30% turnover' needs the underlying numbers; ask how they counted average team size
• Exit data quality: ask how the exit data was collected — retrospective recall of 'what I think people said' has low reliability; actual exit interview notes, anonymised 1-on-1 records, or engagement survey data has higher reliability
• Intervention specificity: ask what a team member would experience differently after the intervention is implemented — a vague intervention description produces uninterpretable results

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Proof required

Submit: (1) an intervention implementation log: the start date, the specific changes implemented (one entry per change with the date and description), and any design adjustments made during implementation; (2) 3–6 months of post-intervention leading indicator data: the specific leading indicator relevant to your intervention type — career development conversation completion rates, engagement survey scores, stay interview sentiment summaries, number of at-risk situations surfaced and resolved; (3) the first available 12-month voluntary turnover datapoint post-intervention (if the 12-month window has elapsed); if not yet elapsed, a quarterly voluntary turnover rate showing direction of change with honest caveats about statistical significance.

What gets checked

• Leading indicator is relevant to the intervention — a career development conversation initiative must show career conversation completion rates; an engagement-survey-based intervention must show engagement score changes; the leading indicator must be logically connected to the specific risk factor diagnosed in M1
• Post-intervention data covers at least 3 months — a single post-implementation measurement is noise; 3 months of data shows direction of change; less than 3 months does not constitute leading signal evidence
• 12-month data or honest quarterly proxy — if the 12-month window has not yet elapsed, a quarterly turnover rate comparison with an explicit caveat that it may not reflect the final annual rate satisfies the standard; claiming 12-month improvement on 3 months of data does not

Common mistakes

• Selecting a leading indicator that is too easy to move — completion rates for activities the manager schedules are easy to hit; the leading indicator must measure a real signal of retention risk reduction, not task completion compliance
• Treating no departures during the measurement period as evidence of retention improvement — zero voluntary departures in a 3-month period may simply reflect a healthy labour market, a difficult economy, or coincidence; zero departures is not evidence of intervention effectiveness without a comparison baseline
• Conflating retention-adjacent metrics with retention — lower absenteeism or higher performance scores may be correlated with retention but are not leading indicators of it; the leading indicator must be directly connected to voluntary turnover risk

Resources

What a verifier looks for

• Leading indicator relevance: ask why the chosen leading indicator is connected to the M1 dominant risk factor — a leading indicator selected for convenience rather than logical connection to the diagnosed risk factor will not show a real retention effect
• Data period: confirm that the leading indicator data covers at least 3 months — ask for the dates of the first and last data points
• Absence of departures: if the submitter cites zero voluntary departures as evidence, ask what the base rate of voluntary departures is in a typical 3-month period for a team this size — zero departures may not be below the expected baseline

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Proof required

Submit: (1) a retention outcome report: the M1 baseline voluntary turnover rate, the post-intervention rate (12-month preferred), the delta, and a 200-word attribution analysis — which factors besides the intervention might explain the change; (2) a lessons-learned document (300 words minimum): what the intervention addressed, what it did not address, and what the next retention improvement would be if continuing; (3) documentation of a real-time Q&A session with a named reviewer who has ≥3 years of people management experience and a verifiable professional profile — the documentation must include specific challenge questions and your responses; the reviewer must not be your current manager or a direct report.

What gets checked

• Retention outcome uses the same voluntary turnover methodology as M1 — the same formula, the same data source, the same time-period calculation method
• Attribution analysis names macroeconomic and contextual factors — a retention improvement during a tech layoff period cannot be solely attributed to a management intervention; honest attribution acknowledges the labour market context
• Q&A shows the reviewer challenged attribution and intervention design — a reviewer who only confirmed the turnover figures without asking 'how do you know this was your intervention and not the economic environment?' did not meet the adversarial standard

Common mistakes

• Claiming 12-month retention improvement on less than 12 months of data without a clear proxy methodology — an honest quarterly proxy with explicit caveats is acceptable; an uncaveated improvement claim on 6 months of data is not
• Attribution analysis that credits only the intervention — retention is influenced by macroeconomics, team composition changes, competitive dynamics, and company performance; an attribution analysis with no alternative factors considered is not credible
• Reviewer who focuses only on the process rather than the causal logic — a reviewer who asks only 'how did you implement the stay interviews?' rather than 'why do you believe the stay interviews changed retention rather than simply being correlated with a period of low attrition?' did not meet the adversarial standard

Resources

What a verifier looks for

• Data period: confirm whether the outcome report uses 12-month data or a quarterly proxy — if quarterly, ask the submitter how they are accounting for the fact that 3–6 months of low attrition may not predict the 12-month rate
• Attribution: ask the submitter to describe the labour market conditions during the post-intervention period — if it was a period of high unemployment or tech layoffs, low voluntary turnover may reflect macroeconomics rather than the intervention
• Reviewer challenge: look for causal logic questions in the Q&A documentation ('how do you know it was the career conversations and not the compensation changes that happened at the same time?') rather than only process questions

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