Willingness to Pay: The Pricing Signal Most Marketers Ignore

Willingness to pay is the maximum price a customer will accept before choosing not to buy. It is not the price they prefer, it is not the price they expect, and it is not a number you can reliably guess from a competitor’s rate card. Getting it right is one of the highest-leverage decisions in product marketing, and most teams either skip the work entirely or confuse it with cost-plus logic dressed up as strategy.

Done well, willingness-to-pay analysis tells you where your pricing ceiling sits, which customer segments value your product most, and where you are almost certainly leaving money on the table. Done poorly, or not done at all, it produces pricing decisions that feel commercially sensible but are built on assumption.

Why Pricing Gets Handed to the Wrong People

I have sat in a lot of pricing conversations over the years. At agencies, with clients, inside businesses going through a turnaround. The pattern is almost always the same: finance sets a floor based on cost, someone benchmarks the nearest competitor, and the number lands somewhere in between. Then marketing is handed the price and asked to sell it.

That sequencing is backwards. Pricing is a marketing problem before it is a finance problem. The people closest to the customer, who understand what the product actually means to a buyer, what problem it solves and how urgently, should be informing price from the start. Not after the number is set.

The reason this matters is that cost-plus pricing and competitor benchmarking both answer the wrong question. Cost-plus tells you what you need to charge to make money. Competitor benchmarking tells you what others are charging. Neither tells you what a customer is willing to pay, which is the only number that reflects actual market value.

Product marketing sits at the intersection of customer insight and commercial strategy. If you want a fuller picture of how pricing fits into that broader discipline, the product marketing hub at The Marketing Juice covers the territory in detail, from positioning and messaging to launch strategy and channel fit.

What Willingness to Pay Actually Measures

Willingness to pay, often abbreviated to WTP, is not a fixed property of a product. It is a property of the relationship between a product and a customer in a specific context. That distinction matters enormously in practice.

The same software product might have a WTP of £50 per month from a freelancer and £500 per month from a mid-market operations team, not because the product is different, but because the value it creates is different. The freelancer saves two hours a week. The operations team eliminates a manual process that was costing them a headcount. Same product, entirely different economic value, entirely different price ceiling.

This is why segmentation is inseparable from pricing strategy. Aggregate willingness to pay, averaged across your entire addressable market, is not a useful number. It smooths out the very variation you need to see. The interesting question is not “what will people pay?” but “which people, for what outcome, in what context?”

Forrester has written extensively on how product marketing and management intersect at exactly this kind of commercial decision. The argument is consistent: product marketers who stay in the messaging lane and leave pricing to finance are operating below their actual value to the business.

The Research Methods That Actually Work

The Research Methods That Actually Work

There are several ways to measure willingness to pay. Two are worth knowing in detail because they are practical, well-established, and answer different questions.

Van Westendorp Price Sensitivity Meter

The Van Westendorp method uses four survey questions to identify an acceptable price range. You ask respondents: at what price would this product be so cheap it seems low quality? At what price would it be a bargain? At what price would it start to feel expensive? At what price would it be too expensive to consider?

Plotting the cumulative response curves for each question gives you a range with a floor and a ceiling, and two intersection points that indicate the optimal price point and the acceptable price range. It is not perfect, it is stated preference rather than revealed preference, but it is fast, relatively cheap to run, and gives you a defensible starting position that is grounded in customer data rather than internal assumption.

I have used this method with clients who had never done any formal pricing research. The output is often surprising, not because customers want to pay less than expected, but because the acceptable range is frequently wider than the business assumed. Teams often price defensively, sitting at the bottom of the range because they are nervous, when the ceiling is considerably higher than where they landed.

Conjoint Analysis

Conjoint analysis is more sophisticated and more resource-intensive. It works by presenting respondents with a series of product configurations, each with different combinations of features and prices, and asking them to choose between them. By analysing which combinations get chosen and which get rejected, you can statistically infer how much each feature contributes to perceived value, and how much price sensitivity varies across the population.

The advantage of conjoint over Van Westendorp is that it captures trade-off behaviour. It does not just ask what someone would pay, it observes what they choose when price is one variable among several. That is closer to how purchase decisions actually work. The disadvantage is complexity: it requires larger sample sizes, more careful survey design, and statistical analysis that most marketing teams will need external support to run properly.

For most product marketing teams, Van Westendorp is the right starting point. Conjoint becomes relevant when you are making significant pricing architecture decisions, when you are considering tiered pricing, or when you need to understand feature-level value attribution.

Perceived Value Is the Real Lever

Here is something that gets underweighted in most pricing discussions: willingness to pay is not fixed. It moves with perceived value. And perceived value is, to a meaningful degree, a marketing output.

When I was growing an agency from around 20 people to over 100, pricing was a constant conversation. We were not always the cheapest option, and we did not want to be. The question was whether we could articulate the value clearly enough that clients felt the price was justified. The answer was not always yes. Sometimes we lost on price because we had not done the work to make the value tangible. That was a positioning failure before it was a pricing failure.

The same dynamic plays out in product marketing. If your messaging leads with features, you are asking the customer to calculate the value themselves. If it leads with outcomes, you are doing that work for them. The product that says “reduces manual data entry by 80%” has a higher WTP ceiling than the identical product that says “automates your workflow,” because the first one makes the value concrete and the second one leaves it abstract.

This is why positioning and pricing are not separate decisions. How you frame a product at launch shapes how customers think about its value, which shapes what they are willing to pay for it. Teams that treat these as sequential steps, first position, then price, miss the feedback loop between them.

Competitive Intelligence Has a Role, But a Limited One

Competitor pricing is not irrelevant. If you are pricing into a market where buyers have established reference points, those reference points constrain you whether you like it or not. A customer who has been paying £200 per month for a category of software will have a strong prior about what that category is worth, and you will need to either justify a premium clearly or work within that frame.

But competitive pricing data tells you what competitors have decided, not what the market will bear. Competitors make the same mistakes everyone else makes. They price based on cost, or based on what they think the market expects, or based on what they needed to charge to hit a margin target. Anchoring your own pricing to their decisions is anchoring to their assumptions, not to market reality.

Competitive intelligence is most useful in pricing when it helps you understand the reference frame your customer is operating in, not when it becomes the primary input to your own price-setting. Use it to understand context. Do not use it as a substitute for customer research.

The same applies to market research more broadly. Good market research helps you understand demand, segment behaviour, and buyer psychology. It does not replace the specific work of measuring willingness to pay, but it gives you the context to interpret that data correctly.

Price Architecture: Tiers, Anchors, and Decoys

Once you have a working understanding of willingness to pay across your key segments, the next question is how to structure your pricing to capture value from each of them. This is where price architecture becomes relevant.

Tiered pricing is the most common approach: different price points for different levels of access, usage, or support. The logic is straightforward. If your WTP evidence suggests a meaningful gap between what small customers will pay and what enterprise customers will pay, a single price point either leaves enterprise money on the table or prices out the small customers you also want. Tiers let you serve both without discounting.

Anchor pricing works on a different principle. When customers see a high-priced option first, it recalibrates their sense of what is reasonable. A £500 per month enterprise tier makes a £150 per month mid-tier feel affordable by comparison, even if £150 was the number you actually wanted people to choose. The anchor does not need to convert at high volume to do its job.

Decoy pricing is a refinement of the same idea. A third option, priced and configured to make one of the other two look like obviously better value, nudges buyers toward the option you most want them to choose. It is a well-documented behavioural effect, and it works because buyers rarely evaluate price in isolation. They evaluate it relative to the alternatives in front of them.

None of these techniques replace willingness-to-pay research. They are ways of structuring your pricing once you know what different segments will bear. Without that underlying data, you are designing architecture for a building you have not measured.

Where Willingness to Pay Breaks Down in Practice

A few failure modes come up consistently, and they are worth naming directly.

The first is treating stated preference as revealed preference. When you ask someone in a survey what they would pay, you are getting their best guess under hypothetical conditions. Real purchase decisions involve friction, alternatives, budget cycles, and social pressure that surveys do not replicate. WTP data is directionally useful, not precisely predictive. Use it to set ranges and test hypotheses, not to set a number with false confidence.

The second is running the research once and treating the output as permanent. Willingness to pay shifts with market conditions, competitive dynamics, and how well your positioning is working. A number you measured two years ago during a period of high demand may not reflect what the market will bear today. Pricing research should be periodic, not a one-time project.

The third, and the one I see most often, is doing the research but ignoring the output when it conflicts with internal expectations. I have watched businesses commission willingness-to-pay analysis, get back data showing their price ceiling is significantly higher than their current price, and then fail to act on it because someone senior is nervous about the reaction. The research is only valuable if you are willing to let it change something.

Early in my career, I learned a version of this lesson in a different context. When I needed budget to build a website and was told no, I did not accept the constraint passively. I taught myself to code and built it. The point is not the story, it is the mindset: if you have a clear view of what needs to happen and the data to support it, the discomfort of acting on it is usually smaller than the cost of not acting.

Testing Price in the Market

Survey-based research gives you a starting point. Market testing gives you confirmation. The two work best together.

The most straightforward form of price testing is A/B testing different price points against equivalent audiences and measuring conversion. If you have enough traffic volume and a clean testing setup, this gives you revealed preference data, real buyers making real decisions, rather than hypothetical responses. The challenge is that it requires volume, patience, and the discipline not to call the test early.

For products that are not yet live, or where live testing would create customer relations problems, landing page tests can work as a proxy. Show different price points to different segments, measure intent signals like click-through to checkout, and use that as a directional indicator. It is not the same as a real transaction, but it is more grounded than survey data alone.

Sales conversation data is underused as a pricing signal. If your sales team is consistently hearing “that is more than we expected” or “we need to think about budget,” that is willingness-to-pay feedback in real time. Most organisations do not capture it systematically. They should. The objections your sales team fields every day contain more pricing intelligence than many formal research projects.

I have seen this play out in performance marketing too. Early in my career, I launched a paid search campaign for a music festival and watched six figures of revenue come in within roughly a day from a relatively simple setup. The product was right, the audience was ready, and the price was not an obstacle. When pricing and perceived value are aligned, conversion is not a struggle. When they are not, no amount of media spend fixes it.

There is more on how product marketing connects to broader commercial performance in this Unbounce conversation on the discipline’s growing role in revenue outcomes. The pricing dimension is central to that argument.

If you are working through the broader product marketing challenge, the full range of topics from ICP definition to launch strategy and channel fit is covered in the product marketing section of The Marketing Juice. Pricing does not exist in isolation from those decisions, and it makes more sense when it is connected to the rest of the commercial picture.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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