Insights that enable decisions

Depending on your decision situation and the maturity of your pricing, different price research set-ups are suitable. The selection of methods below is proven in practice and can be adapted or combined as needed to ensure both realism and decision usability.

When selecting the optimal price research set-up, we always focus on one goal: respondents should apply the same decision rules they use in reality. This requirement must be met to generate valid insights.

Gabor-Granger

In short: direct price question using a few predefined price points – fast, clear, and easy to communicate.

When to use

When you already have plausible price points in mind
When customers only have approximate price knowledge (e.g., market novelty, low involvement decision situation)

What you get

Take rate per price point (and a simple acceptance/demand logic derived from it)
A rough indication of “premium potential” vs. “drop-off risk” within the tested corridor

Strengths

Very easy to implement and easy to explain internally
Low cognitive load for respondents

What to watch out for

Typically creates a “negotiation situation” that is not always realistic
Only a few price points can be tested cleanly – choose them deliberately and, if needed, combine with more realistic designs

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Monadic Test Design

In short: split-sample test: the same offer, only the price varies – significantly more realistic than a negotiation situation. If required, additional offer attributes can be varied simultaneously; differences in evaluation are then attributable to the overall offer.

When to use

When you want to test a concrete product/service bundle in a realistic way
When you want to compare concrete price points in an A/B logic

What you get

Take-rate curve across multiple price points
Comparison of additional KPIs (e.g., attractiveness/understanding/purchase likelihood)

Strengths

High realism through a clear ceteris-paribus logic (as long as only the price varies)
Very suitable when pricing is truly the focus and effects need to be cleanly attributed

What to watch out for

Requires sufficient sample size per monad
Discipline in KPI selection: define upfront which metrics are decision-relevant (otherwise it quickly becomes “nice to know”)

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Price Sensitivity Measurement (PSM / Van Westendorp)

In short: identifies accepted price ranges via up to four price questions – no need to set specific price points upfront.

When to use

When you need an initial indicative price range (early-stage pricing)
When you want to identify price thresholds

What you get

Accepted price range and reference points (“too cheap”/“too expensive”, etc.)
In adapted form, also indications of room for price increases

Strengths

Low requirements on the company side (no fixed price points needed)
Methodologically relatively simple and easy to communicate internally

What to watch out for

Without adjustments, respondents often estimate prices too low (price potential is not fully leveraged)
With weak price knowledge, “don’t know” answers can increase – consider combining with measures that improve price knowledge
Workaround: anchor with market or target prices

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Conjoint

In short: models trade-offs between performance attributes and price – ideal for packaging, portfolio decisions, and scenario simulations.

When to use

When multiple offer attributes need to be optimized simultaneously (not just “the one right price”)
When you want to back portfolio/bundle decisions with simulations

What you get

Utility values / relative preferences per attribute and price
Simulations: how do choice shares/preferences change with differently configured packages/prices?

Strengths

Very strong for “what-if” decisions in complex bundles
Provides a structured foundation for portfolio and packaging decisions

What to watch out for

and how we pragmatically “patch” it:
Classic conjoint studies have typical realism gaps:

Minimum requirements (not everything can be compensated by a lower price)
Price thresholds (thresholds may exist between tested price points that classic conjoint does not capture)
Unrealistic price focus due to the tabular presentation of product bundles

To mitigate these points, complementary information should be collected in the framing questionnaire and considered in simulations, for example:

Non-compensatory attributes (no-go’s)
Price thresholds (e.g., via PSM)
Measurement of price interest to calibrate the price effect

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Preisarchitekten Bild: Entscheidungsprozess

Understanding decision processes

Regardless of the chosen price research method, a fundamental understanding of your customers’ decision processes is critical. This includes answering questions such as (selection):

What are drivers and barriers in the decision process?
How relevant is price relative to other attributes such as own and/or third-party experience with the provider, service elements, brand perception, etc.?
What is customers’ price motivation (hunting for the best deal; avoiding unpleasant surprises; desire to treat themselves, etc.)?
How good is customers’ price knowledge?

Based on the responses to these questions, a differentiated profile of your customers emerges. With this customer profile in mind, we interpret the results of the pricing research methods and derive practical recommendations for price setting and for enforcing prices in the market.

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