Willingness-to-Pay Research That Holds Up

Your pricing is not based on evidence. It is based on what the founding team thought was reasonable at launch, adjusted for the handful of deals where a customer pushed back hard, modified by whatever competitive pricing someone found on a competitor's website. This is not unusual. It is how nearly every B2B SaaS company sets prices.

It is also a choice with a quantifiable cost.

The Real Cost

Pricing based on instinct rather than evidence has two failure modes, both expensive. The first is underpricing: charging less than your customers would pay, which compounds every month as annual recurring revenue (ARR) grows. The second is mispositioning: pricing in a way that attracts the wrong customers while repelling your highest-value segment.

Underpricing at $15M ARR typically costs $1M to $3M annually, depending on how far below the true price ceiling your list rates sit. Mispositioning is harder to quantify but shows up in high churn rates among buyers who bought for the wrong reason, long sales cycles caused by ideal customer profile (ICP) mismatch, and expansion rates that trail peers in the same category.

Neither of these problems requires expensive external research to diagnose. They require a structured look at data you already have.

The Framework

Step 1: Map where your pricing conviction is weakest using deal desk data.

Pull your last 24 months of deals and calculate average discount rate by segment, by deal size, and by sales rep. High discount rates in specific segments signal that either your price is too high for that segment or your sales team lacks conviction to defend the price. These two diagnoses require different interventions, and your deal desk data can distinguish between them: if one rep consistently discounts more than peers on similar deals, it is a conviction problem. If discount rates are uniformly high in a specific segment, it is a pricing problem.

Step 2: Separate price objections from post-hoc rationalizations in your win-loss data.

Most lost deals recorded as "price" in your CRM were not actually lost on price. Run 5 to 8 structured win-loss interviews with recent losses where price was cited. Ask the buyer what they did after the decision. If they bought a cheaper alternative, price was the objection. If they delayed or chose a different category of solution, price was a proxy for a deeper concern about value, fit, or risk. This distinction tells you whether to change your price or your value narrative.

Step 3: Use expansion data to identify packaging gaps.

Your highest-net revenue retention (NRR) cohort is your most price-elastic segment: they expanded because they found value worth paying more for. What did they expand into? If most expansion happens within a tier rather than between tiers, your tiers are not structured around natural value breakpoints. If expansion rates are low across the board, your upsell packaging may be missing the features customers actually want to pay more for.

The Failure Case

A $11M ARR B2B analytics platform had never changed its pricing since launch 3 years earlier. The founding team believed the market would not support an increase because they remembered two early deals that required significant discounting. Those two deals had become anchors for every subsequent pricing conversation.

A 3-week internal audit using existing CRM data found that: discount rates over the past 18 months averaged 8 percent, well below the 20 percent threshold where sales teams typically signal pricing pressure; win rates had been rising, not falling; and the two early deals that shaped pricing intuition were in a segment that now represented only 6 percent of their pipeline.

The company raised prices 25 percent. Win rates dropped by 1 percentage point in the following quarter and recovered fully within 6 months. Annual ARR growth accelerated from 34 percent to 51 percent in the 12 months post-change.

The data that supported this decision existed the entire time. Nobody had looked at it.

What to Do This Week

Pull your discount rate data for the last 12 months, segmented by deal size. If your average discount rate is above 12 percent and you have not recently changed your pricing, you either have a pricing problem or a conviction problem. Run the 3-step analysis above to find out which one.

Take the FintastIQ pricing assessment to get a structured diagnostic based on your revenue and segment data.

For related reading, see the hidden costs of bad willingness to pay research and first principles of willingness to pay research.