Pricing Research, Decoded: What These Buzzwords Actually Mean (and When to Use Them)

Pricing is full of jargon. “Van Westendorp.” “TURF.” “Conjoint.”

You’ll hear these terms tossed around in strategy sessions or LinkedIn threads like everyone’s fluent,  but most founders aren’t sure what they actually mean, let alone when to use them.

This guide is here to fix that. Below, we break down common pricing research methods, what they are, what they’re good for, and when they actually matter.

Before anything else: define the job

We don’t recommend picking a method until you’ve done the strategic work — especially defining your Jobs to Be Done. Pricing tools don’t tell you what your product is worth; they tell you how well different pricing structures match how customers perceive that worth.

Once you’ve done that, the right method depends on the question you’re trying to answer:

• What’s a fair price for this new product?
• How should we package features?
• What happens if we raise prices next quarter?

Different tools answer different questions.

Common Pricing Research Methods (Explained Without the Jargon)

1. Van Westendorp: Finds a psychological price range by asking when something feels “too cheap,” “too expensive,” or “just right.” Good early signal for new pricing zones. We recommend this as the first step for building new pricing.
2. Gabor-Granger: Structured survey to understand willingness to pay at different price points. Helps estimate price elasticity. Useful to get specific pricing after establishing ranges with the Van Westendorp model.
3. Conjoint Analysis: Simulates trade-offs, price vs. features vs. brand. Great for redesigning packaging or tiered pricing.
4. Monadic Price Testing: Show different prices to different customers and compare reactions. Simple A/B format.
5. In-Market Price Tests: Real pricing with real users interacting. High signal, high risk. Best used with traffic segmentation and baseline controls.
6. Brand Price Tradeoff (BPT): Quantifies how much premium people will pay for your brand vs. a competitor’s. Useful when you’ve achieved product-market-fit or are going up against an established competitor.
7. TURF (Total Unduplicated Reach and Frequency) Analysis: Tests bundles and combinations to determine what mix reaches the most of your market.
8. Regression Analysis: Post-launch, analyzes how price changes affect churn, conversion, and usage. Needs clean (large) data.
9. Qualitative Research: Interviews or open-ended surveys. High context, low scale. Often a good starting point for early stage and new products.
10. A/B Testing in Product or Ads: Live experiments with pricing variants. Great for PLG introducing pricing or performance marketing-driven teams.

For AI-native products: behavior-first, not just usage-first

If your product is powered by AI or priced by tokens, compute, or output, traditional methods still apply, but how you apply them matters:

• Use behavioral clustering to understand natural usage groups
• Run fairness sensitivity testing for opaque or probabilistic pricing logic
• Anchor usage ranges to give customers value context, not just units
• Watch for the “pain of paying”: unpredictable microcharges hurt trust even when they’re fair

For more, see our guide to usage based pricing.

Behavioral pricing principles that actually matter

You don’t need a psych degree to use behavioral economics, but you do need to understand how people interpret price. Here’s what the research says, and what that means for your pricing:

• Just noticeable difference (Monroe, 1973): Tiny price changes don’t register, until they do. If you’re going to raise prices, raise them with purpose. Drip increases feel sneaky; bold moves with clear value stories land better.
• Price = quality signal (Zeithaml, 1988): Underpricing doesn’t feel generous, it feels low-value. If customers don’t understand why your product costs what it does, they’ll assume it’s not worth much.
• Pain of paying (Prelec & Loewenstein, 1998): Every unpredictable or repeated charge creates emotional drag. Especially in usage-based pricing, make spend feel predictable and fair — not like a penalty.
• Loss aversion (Thaler & Kahneman, 1985): People hate losing more than they enjoy gaining. If you remove a feature, raise a price, or cut support, expect some pushback, this is not necessarily a bad thing, we teach leaders how to plan for this when implementing price raises. You can find price raising scripts in our .Behavioral Pricing Playbook, a guide to de-risked, research-backed pricing decisions.
• Anchoring and framing: Most customers don’t know what your product is “worth.” They decide based on the context you create. Use tiers, comparisons, and behavioral cues to frame value on your terms.

We go deeper into this inPsychological Pricing Mistakes, but the short version is this: how you structure and communicate price matters as much as the number itself.

Final thought

You don’t need every tool on this list. You just need the right one, at the right moment, matched to the right question.

If you’re pre-launch, lean into Van Westendorp or Gabor-Granger. If you’re tweaking packaging, look at conjoint. If you’ve got traffic, test in-market. And if you’re unsure? Start by asking customers what they think,  then test it at the edges.

References

Husemann-Kopetzky, M. (2018). Handbook on the Psychology of Pricing: 100 Effects on Persuasion and Influence Every Entrepreneur, Marketer, and Pricing Manager Needs to Know. Pricing School Press.

Monroe, K. B. (1973). Buyers’ subjective perceptions of price. Journal of Marketing Research, 10(1), 70–80. https://doi.org/10.1177/002224377301000110

Prelec, D., & Loewenstein, G. (1998). The red and the black: Mental accounting of savings and debt. Marketing Science, 17(1), 4–28.  https://doi.org/10.1287/mksc.17.1.4

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. https://doi.org/10.1126/science.7455683

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302