Adoption
I measure whether reps and CSMs actually use the system as designed, because a workflow nobody follows is worse than no workflow: it produces data that looks real and is not. The failure mode is a team that logs into the CRM, updates the one field comp reads, and does everything else in a spreadsheet on the side.
Shape of it
6 metrics
- Active login rate
- Share of licensed users who log in within a period.
- users logging in / licensed users
- Example 120 licensed reps, 96 log in during the week → 80%. The 24 who did not are either inactive licenses to reclaim or reps running the deal in a spreadsheet.
- Benchmark Practitioner target 90%+ weekly for a sales team the CRM is core to; a low rate on paid licenses is wasted spend and shadow process
- Login is the floor of adoption; if they are not in the system, none of the downstream data is being maintained by them.
- Feature adoption rate
- Share of target users actively using a specific feature or workflow (guided selling, a new LWC, MEDDPICC fields).
- users using feature / target users
- Example 150 reps, 39 fill the MEDDPICC fields on live opps → 26%. If the forecast leans on those fields, three quarters of the pipeline has no qualification data behind it.
- Benchmark Judge per feature against effort-vs-payoff; features that only add rep work stall in the 20-40% range without enforcement
- A half-adopted feature produces partial data that is more dangerous than none, because reports treat the gaps as real zeros.
- Data entry compliance
- Share of records updated per the process (stage moves logged, next steps set, close dates current).
- compliant records / records in scope
- Example 400 open opps, 120 have a close date in the past → 70% compliant on close date. Every stale date inflates the current-quarter forecast with deals that already slipped.
- Benchmark Practitioner target 90%+ on the fields that feed forecast and routing; below that the pipeline report is fiction
- Forecasts and coverage math read these fields directly; compliance is the difference between a pipeline report and a guess.
- Activity capture rate
- Share of real customer interactions (calls, emails, meetings) logged to the CRM, ideally automatically.
- Example A rep sends 40 customer emails a week but logs 6 manually → 15% capture. Switch on auto-capture and it jumps to ~95%, and only then does the activity dashboard mean anything.
- Benchmark Manual logging captures a fraction of reality; auto-capture tools target 90%+ of email and calendar activity synced
- Activity data drives engagement scoring and manager coaching; if capture is manual and low, every activity metric understates reality.
- Time in system
- Median active time users spend working in the CRM per day or week.
- Example Reps average 22 minutes a day in the CRM but their real notes live in a personal doc; the low time is the tell that the system is a comp-reporting chore, not a workspace.
- Benchmark No universal target; trend it and pair with output. Rising time with flat output means the system is friction, not value
- Time in system, read against output, tells you whether the CRM is where work happens or a place reps visit to satisfy comp.
- Shadow-system prevalence
- Extent to which teams run the real process in spreadsheets or side tools instead of the CRM.
- Example Three of five AEs maintain a personal deal tracker in Sheets because the CRM opp view is missing two fields they need; the CRM forecast is now downstream of a spreadsheet nobody can see.
- Benchmark Qualitative but critical; surface it in adoption interviews. Any team-wide side spreadsheet is a signal the CRM design failed that team
- Shadow systems are the loudest adoption failure: the data leadership reports on is a stale copy of the real one living off-platform.
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