Case Study — SaaS Pricing Audit

SaaS Pricing Strategy Audit — Tier Restructuring Identified $840K Annual Revenue Lift

B2B SaaS project management and workflow platform — ~$8M ARR, late Series A, 50–70 employees. The CEO knew pricing was leaving money on the table. What they didn't know: the how much and exactly where.

Stack

Van Westendorp · SPSS

$840K

Projected annual revenue lift from tier restructuring

+22%

Increase in average deal size

Clear pricing tiers from a single flat rate

94%

Grandfathered customer retention

Churn increase during transition

Before ProductQuant.

The CEO knew the pricing model was leaving money on the table. The company charged a single flat rate of $79/mo for every customer — from a three-person agency running a handful of projects to a 200-person enterprise managing complex cross-department workflows. Everyone paid the same price.

The problem: This created two distinct pain points simultaneously. Enterprise prospects wanted to pay more for premium features, dedicated support, and advanced controls but had no option to do so. Meanwhile, smaller teams found $79/mo too expensive for their limited use case, leading to price objections at the bottom of the market. The single tier was simultaneously too expensive for SMB and too cheap for enterprise.

Revenue per seat was roughly flat across all customer segments, leaving two distinct willingness-to-pay clusters completely unaddressed. The company was leaving money on both sides of the market.

The Problem

Single $79/mo tier priced out SMBs while underpricing enterprise
No feature-based differentiation between segments
Enterprise prospects requesting premium features with no upgrade path
Lost deals at both ends of the market with no data on optimal price points
Annual discount promos eroded LTV without moving the needle on acquisition

What they tried before us.

Attempt 1 — Competitor price matching

The team lowered their price to match competitor entry-level plans, hoping to capture more SMB volume and compete on cost.

Outcome: Race to the bottom. Lower price attracted more signups but with higher support costs and worse retention. Revenue per seat dropped without sufficient volume to compensate.

Attempt 2 — Annual discount promos

They offered aggressive annual-only discounts (up to 30% off) to improve upfront cash flow and lock in customers longer.

Outcome: Eroded LTV on existing customer base. Discounted annual customers churned at roughly the same rate as monthly at renewal time, but with 30% less revenue collected. The discount didn't improve retention; it just reduced margin.

Attempt 3 — Feature gating surveys

The team surveyed users asking which features they would pay more for, hoping to identify natural tier boundaries.

Outcome: Inconclusive. Stated preference surveys produced unreliable data — users said they'd pay for everything and nothing. No clear signal on where tier boundaries should be drawn or what price points would work.

Why it didn't work: All three attempts treated pricing as a single-variable problem (the number). The real problem was structural: one tier for multiple segments with different willingness to pay, different feature needs, and different price sensitivities. You can't optimise what you haven't segmented.

The diagnosis.

Working through usage data, feature telemetry, and price sensitivity analysis, the root cause became clear. The single-tier model was a symptom of a deeper problem: nobody had measured how much each segment was actually willing to pay.

Finding 1 — Two distinct willingness-to-pay clusters

Van Westendorp price sensitivity analysis across 200 users revealed two statistically distinct willingness-to-pay clusters. The lower cluster (small teams, 1–10 users) had an optimal price point near $49/mo. The upper cluster (mid-market, 25+ users) was comfortable with $149/mo or higher. The single $79/mo sat in the dead zone between both clusters — too expensive for SMB, too cheap for enterprise, and capturing less total value than either cluster was willing to pay.

Finding 2 — Feature usage revealed natural tier boundaries

Usage data showed that 60% of users never touched advanced features like workflow automation rules, custom reporting, or API integrations — yet they were paying the same price as power users who depended on those features daily. Feature consumption clustered naturally into three groups: basic project management only, advanced workflow needs, and full-suite enterprise requirements with admin controls and SSO. The product already had natural tier boundaries; the pricing just didn't reflect them.

Finding 3 — Competitor value maps showed underpricing by 40%

Competitive price benchmarking against six comparable project management tools revealed that the company's feature set was roughly 40% underpriced for mid-market buyers. Competing tools with comparable functionality charged $120–$180/mo for their professional tiers. The company's single $79/mo tier was leaving an estimated $40–$100/mo per mid-market customer on the table. The value was there; the price was not.

The fix.

A complete pricing restructure grounded in actual willingness-to-pay data, feature segmentation analysis, and a carefully managed transition strategy for existing customers.

Fix 1 — Van Westendorp Price Sensitivity Study

Conducted a Van Westendorp price sensitivity meter study across 200 customers and prospects, segmented by company size, feature usage, and industry vertical. Identified optimal price points for each cluster: $49/mo for SMB (too cheap, cheap, expensive, too expensive ranges collected), $149/mo for mid-market. Also identified an enterprise custom tier starting above $299/mo with the right feature bundle.

Fix 2 — Feature-Usage Segmentation into Three Tiers

Using 12 months of product telemetry, every feature was mapped to a usage decile. Features used by 80%+ of accounts went into Basic. Features used by 30–80% became Pro-tier differentiators. Enterprise admin controls, SSO, custom reporting, and audit logs were gated at the Enterprise tier. Each tier had a defensible feature rationale backed by actual usage data, not guesswork.

Fix 3 — Price Anchoring at New Tier Points

New pricing launched with Basic at $49/mo, Pro at $149/mo, and Enterprise at custom (starting $299/mo). Each tier page included a comparison table anchored against the Pro tier as the recommended option. Competitive value messaging highlighted the $149/mo Pro tier as delivering $200+ value compared to competitors' equivalent plans.

Fix 4 — Grandfathering Strategy for Existing Customers

All existing $79/mo customers were grandfathered at their current price with their current feature set for 12 months. High-usage power users were proactively contacted about upgrading to Pro with a loyalty discount. Low-usage users were offered migration to Basic at a reduced rate. Communication was transparent: explain why tiers exist, what changes, and what stays the same.

The result.

Before vs After metrics with quantified revenue impact across the full customer base.

$840K

Projected annual revenue lift — from tier restructuring, pricing optimization, and enterprise upsell capture within 12 months of rollout

+22%

Average deal size increase — mid-market prospects now buying Pro at $149/mo instead of the previous flat $79/mo

Clear pricing tiers — Basic ($49/mo), Pro ($149/mo), Enterprise (custom) — each backed by usage data and willingness-to-pay analysis

94%

Grandfathered customer retention — transparent migration strategy kept nearly all existing customers during the transition

Churn increase during transition — despite price changes and tier restructuring, churn remained flat against the prior period

40%

Revenue per power user improvement — high-usage customers who upgraded to Pro now paying a price that reflects the value they actually consume

What you can do now.

Know exactly what each segment is willing to pay

Van Westendorp price sensitivity analysis across your actual customer base reveals optimal price points for each segment. No more guessing whether $79 or $149 is the right number.

Identify natural tier boundaries in your feature usage data

Usage decile analysis shows exactly which features belong in Basic, Pro, and Enterprise. The data is already there — most teams just haven't looked at it through a pricing lens.

Capture $840K+ in latent revenue

Feature-usage segmentation, price anchoring, and a structured grandfathering plan can unlock significant annual revenue without increasing churn. The value is already there; the pricing needs to match it.

See What This Costs → Book a 15-Minute Call

What this looks like for your company

Pricing Audit.

A structured review of your pricing model, customer willingness to pay, feature-usage data, and competitive positioning — finding the tier structure that captures maximum value without increasing churn.

Van Westendorp price sensitivity study across your customer base, revealing optimal price points per segment
Feature-usage decile analysis identifying natural tier boundaries in your product data
Competitive value mapping to benchmark your pricing against comparable products
Full tier structure recommendation with pricing, feature bundles, and positioning for each tier
Grandfathering and migration strategy to protect existing customer relationships during transition

$3,497 · 14 days

Right for you if

Single pricing tier serving multiple customer segments with different needs
Suspect you're leaving money on the table with enterprise but don't know how much
Enterprise prospects requesting premium features but no upgrade path to offer them
Feature usage data exists but has never been analysed through a pricing or segmentation lens

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