Joined from your SFA calls, Rx audit panel & stockist secondary
Doctor coverage
92%
measured on calls, not scripts
Doctors you pay to call · vs the scripts they actually write■ prescribes ■ paid-for, no Rx
What "92% coverage" hides — the recovery ladder · ₹0 → ₹0 Cr
1Reallocate reps off the no-Rx doctors · same headcount, more frequency on convertible doctors — 70–80% of calls today land on low-yield targets+₹14 Cr/yr
2Right-size dispatch to offtake · trim over-stuffed stockists → cut expiry & returns a quarter to a third (mostly margin)+₹16 Cr/yr
3Split real Rx growth from channel fill · join primary ↔ secondary ↔ Rx so "growth" isn't tomorrow's write-offblind spot
Coverage & growth · what the CEO sees92% coverage, primary sales up — looks healthy
92%under ₹40–80 Cr/yr of returns & expiry that surface 6–12 months late
Growth —or channel fill?
✓Field force on target — 92% doctor coverage, primary sales and growth up.Measured on calls made and stock sold in (primary). What those calls produced in prescriptions — and whether the growth is real Rx or stock pushed into the channel — isn't on this dashboard.
⚠ You pay reps to call doctors who don't prescribe — coverage is wide, Rx-yield is thin — and the channel stock behind "growth" returns later as expiry.
Join SFA coverage ↔ Rx audit ↔ stockist secondary and about ~₹30 Cr/yr becomes recoverable — ~₹14 Cr by redeploying reps off no-Rx doctors + ~₹16 Cr by right-sizing over-stuffed stockists. The data already exists; the join doesn't (three silos, reconciled by hand). We reallocate, not reduce the field force, and right-size dispatch, not cut supply — protecting the stockist from return fights. Confidence-scored, not census.
Don't trust the ₹30 Cr? Book an exec briefing — we map your coverage-vs-yield on a sample and show 3× the fee in recoverable spend, or you don't pay.
Illustrative · representative model, not a client. Rx is sampled, secondary is lagged — we confidence-score yield and flag where the data is too weak to trust; the bottom-yield / top-cost doctor segment is robust to sampling error.