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Marketing Academy · Field Work●Product Marketing
All projects·From: Beta Programs
MiniAudit· 25 minutes

The Cut List: Auditing a Beta Applicant Pool for Fit

Go Digit General Insurance

Objective: Given a real-style pool of 20 beta applicants with segment, usage intent, and engagement signals, select the strongest 8 for a wave-one cohort using the lesson's fit-over-enthusiasm framework.

You're the marketing analyst at Go Digit General Insurance, the Bengaluru-founded general insurer, validating a new usage-based motor insurance app feature before its public rollout. 340 people applied for the 50-person wave-one beta; you've been handed a 20-row sample to triage first.

Score each applicant on target-segment fit and real usage intent, not just enthusiasm, and flag the ones who dilute the cohort.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets
FreeScore and sort the applicant pool

Free, familiar, sufficient for a 20-row triage

The process

1 step

Step 01 of 01

Qualifying beta applicants on fit over enthusiasm

The lesson's Phase 1 warns that a power user in your target segment is worth ten curious people outside it, so qualification should filter on fit, not just enthusiasm.

Of 20 applicants, 8 are daily commuters who already track mileage manually, 7 are insurance-curious hobbyists with no vehicle, and 5 are competitor-app power users just scouting features. Who makes wave one?

Google Sheets— Import the applicant export, add a fit-score column, sort descending.

Procedure

  1. Import the 20-row applicant export and freeze the header row
  2. Tag each row's segment: daily commuter, insurance-curious hobbyist, or competitor-app scout
  3. Score fit 1-3 based on whether the person has real, recurring usage today
  4. Sort by fit score and select the top 8 for wave one
  5. Flag the competitor-app scouts separately, useful for competitive intel, not cohort quality
Sample output
WAVE ONE (fit score 3, 8 selected)
  1. R. Sharma - daily commute 42km, manual mileage log for 8 months
  ... 7 more rows

HOLD (fit score 2, informational only)
  9. Insurance-curious hobbyist, no vehicle yet

FLAG - COMPETITIVE INTEL (fit score 1)
  15. Active user of a competitor's telematics app, applied to compare features

Healthy

8 of 8 wave-one seats go to people already doing the target behavior manually.

Unhealthy

Filling wave one with the 8 fastest form submissions regardless of segment.

What this means

Enthusiasm to join is not the same signal as fit to test; sorting by fit first keeps a small cohort genuinely useful.

So what do I do about it?

SymptomActionEffort
Wave-one feedback reads generic ('cool app!') instead of specificRe-screen for real recurring usage before the next wave, not just interest30 min
YouYou can do this yourself, no engineering access required.

Final deliverable

A wave-one cohort list of 8 applicants scored and sorted by fit, with competitor scouts flagged separately.

See a reference example
Sample output
Five-Star Business Finance wave-one beta shortlist (excerpt)

FIT SCORE 3
  1. Branch loan officer, processes 15+ MSME applications/month
  2. Field collection agent, tracks repayments manually today
  ... 6 more rows

FLAGGED - COMPETITOR SCOUT
  9. Active user of a rival lending-ops app, applied to compare dashboards

Success criteria

You're done when you can:

  • Correctly separates high-fit applicants from merely enthusiastic ones
  • Flags competitor scouts as a distinct category instead of excluding them silently