Challenge
A Series C B2B SaaS company — an analytics platform for mid-market retailers — was growing users but not revenue. ARPU had been flat at ₹41,000/year for three years while feature depth tripled. Their flat "per-seat" pricing meant the largest retailers (20,000+ stores) paid barely 6x what a 50-store customer paid, despite consuming 200x the value. Worse, the sales team had created 130 bespoke discount permutations — price books were unmanageable, and the CFO suspected revenue leakage of ₹6–9 crore annually.
The CEO's question: "Our product is 3x better than 2021 — why does our pricing still look like 2021?"
Approach
We ran a six-week value-based pricing engagement in four modules:
- Value discovery (weeks 1–2). We quantified the economic value the platform created per customer: labour hours saved in reporting, shrinkage reduction, and inventory-carrying-cost avoidance — triangulated from usage data, customer interviews (n=24), and CFO surveys (n=18) on willingness-to-pay for specific outcomes.
- Segmentation & metric design (week 3). We tested six candidate pricing metrics (per-seat, per-store, per-location, per-transaction, per-GMV, tiers by feature) against two tests: correlation with value delivered and customer comprehension.
- Price level & architecture (week 4). Conjoint analysis (n=210 respondents across segments) on feature bundles, with Van Westendorp checks per segment. We modelled migration paths from the legacy per-seat plan.
- Launch & governance (weeks 5–6). A controlled rollout: grandfathering policy, sales-enablement toolkit (value-calculator, negotiation guardrails), and a price-governance charter to kill the 130-permutation sprawl.
Analysis
Per-seat pricing was pricing the wrong unit. Usage analytics showed the platform's value scales with data complexity (store count × SKU count × data sources), not headcount. The 20 largest customers generated 41% of platform value but only 9% of revenue. The value-metric correlation matrix was decisive:
| Metric | Correlation with value delivered | Customer comprehension (0–10) | Implementation effort |
|---|---|---|---|
| Per-seat (current) | 0.34 | 9 | none |
| Per-store tiered | 0.78 | 8 | low |
| Per-transaction | 0.86 | 6 | high |
| Per-GMV % | 0.83 | 7 | medium |
| Feature-tier + usage | 0.81 | 8 | medium |
Conjoint revealed the "anchor tier" problem. Customers anchored on the entry price (₹29,000/yr) and treated everything above ₹75,000 as "enterprise." Redesigning to a 4-tier ladder with clear value steps (Starter / Growth / Scale / Enterprise) shifted the reference point: the mid-tier (₹64,000) became the default choice in simulations, not the entry tier.
Discount leakage was structural, not cultural. 70% of the 130 discount permutations had no link to deal size, term, or competitive pressure — they were legacy favours. A clean "discount for value" policy (standard 5% for annual prepay, up to 12% only with competitive evidence) was worth ~2.1% of revenue annually.
Findings
- The addressable uplift was ₹14–18 crore ARR — a 23–29% increase — from: metric re-anchoring (+11%), tier ladder reprice (+7%), discount governance (+2.1%), and expansion-motion improvement (+3%).
- Grandfathering was the make-or-break decision. Full migration would lift ARR fastest but risk churn among the 28 largest logos; grandfathering existing logos at current price while migrating on renewal terms protected the base and still captured 84% of the uplift within 18 months.
- The sales team would quietly undo the pricing without tools. Post-launch monitoring showed reps defaulting to legacy discounts; a value-calculator (customer inputs → recommended price band) recovered ~1.5 points of discount leakage.
- Per-transaction pricing is the endgame but not the now — customers lacked the telemetry to trust it; the per-store tiered model was the pragmatic bridge.
Recommendation
We recommended a four-tier, per-store tiered architecture with usage bands, launched as a "versioned refresh" rather than a price increase:
- Starter ₹29,000/yr (≤5 stores) — repositioned as SMB entry, de-emphasised;
- Growth ₹64,000/yr (6–50 stores) — the new default;
- Scale ₹1,49,000/yr (51–500 stores) — per-store increment beyond 500;
- Enterprise custom (500+ stores) — outcome-based pricing with a floor at ₹6,00,000/yr.
Execution guardrails: grandfather all existing logos for 24 months; enforce the discount charter through CRM approval flows; equip sales with the value-calculator; and monitor ARPU, logo churn, and discount rate weekly for 90 days post-launch.
Outcome
Twelve months after launch, blended ARPU was up 23% (₹41,000 → ₹50,400) with logo churn at 0.4% (vs. 0.9% the prior year). New-logos ARR grew 31% without incremental sales headcount, and the discount-leakage recovery added ₹2.3 crore. The CFO's "revenue leakage" concern reversed into a governance asset: the price charter became the template for the company's expansion into APAC. The platform subsequently raised its Series D on the back of the improved net-revenue-retention (118% vs. 104% pre-project).
Case study reconstructed for illustration from typical engagement patterns. Client and figures are hypothetical.
