Choice, pricing and concept testing measures how people value competing features, prices and propositions. Customers often rate several ideas positively when each appears alone, which gives the business little basis for choosing between them. Marketways recreates realistic trade-offs and estimates the strength and uncertainty of preference across relevant groups. The client can refine an offer before committing to launch, design or investment.
The decision this method supports
We use Choice, Pricing & Concept Testing to help clients answer: Which offer or combination of attributes does the defined audience prefer, and what trade-offs drive the choice?
How the method works
Choice and concept methods ask people to compare defined alternatives rather than rate every idea positively in isolation. Pricing methods examine willingness to pay and the trade-offs customers make between price and other attributes. The design should resemble the decision customers would face in the market.
A business example
A subscription business may test combinations of price, service speed and support access. Conjoint analysis can estimate the relative importance of each feature and simulate how different offers may compete.
How the client uses the result
Choice and pricing research helps leaders improve an offer before a full launch. It shows which features customers value, which trade-offs they make and how price changes the appeal of competing propositions.
What we deliver
We produce market estimates, customer groups, preference evidence, experience measures or behavioural patterns. A manager should be able to connect the result to an offer, channel, investment or operating decision.
Limits and complementary methods
Stated choices improve offer design but may not reproduce real purchase behaviour when money, availability and competitors enter the decision.
Selected methods and techniques
We select from these established methods according to the decision, evidence and operating conditions.
- Discrete-choice / conjoint analysis: Present respondents with deliberately varied combinations of features and estimate the trade-offs revealed by their choices, rankings or ratings. Specify the target population, attributes, levels, experimental design, task format and statistical model; ensure alternatives are plausible; and test whether the model predicts held-out study choices. Conjoint analysis is the broader family, while a discrete-choice experiment uses repeated choices among alternatives and usually estimates choice probabilities. The resulting utilities and preference shares are conditional on the study design. They are not observed willingness to pay, market share or sales without further evidence about awareness, availability, competition and actual behaviour.
- Discrete-choice experiments: A stated-preference experiment that presents designed sets of mutually exclusive alternatives and asks respondents to choose one, sometimes including a no-choice option. The design and fitted choice model estimate how attributes affect relative choice. Product-focused choice-based conjoint is one common application; the method does not observe real-market purchasing.
- Gabor-Granger price testing: Estimate stated purchase likelihood at different prices by asking respondents structured price-acceptance questions.
- Kano analysis: Ask how people would feel both with and without a feature to understand its effect on satisfaction. This helps distinguish essentials, features where more is better and unexpected extras that delight.
- Latent-class / segment preference analysis: Find customer groups from observed preference patterns when no labels are supplied. Estimate those groups from their choices, allowing uncertainty about which group a person belongs to.
- MaxDiff / Best-Worst Scaling: Infer relative preference by asking respondents to select the best and worst items from designed sets; scores are not purchase probabilities.
- Monadic concept testing: Evaluate a single concept in isolation for each respondent or group, reducing direct comparison effects between concepts.
- Preference mapping: Relate people's liking or choices to a mapped space of product or sensory characteristics, showing which characteristics are associated with preference and how this differs by person or segment. Internal preference mapping derives the product space from preference ratings; external preference mapping relates preference to separately measured product attributes. The map shows association rather than cause.
- Price-sensitivity research: Investigate how a defined population responds to price for a clearly specified offer, using observed transactions, experiments or stated-response methods such as Gabor-Granger, Van Westendorp or conjoint analysis. Record the price unit, offer, context, alternatives, sample and the quantity each method actually estimates; report variation across customer groups and uncertainty. Stated acceptance, perceived acceptable price, willingness to pay and observed purchasing are different measures. Selecting a profitable price also requires demand, costs, capacity, strategy and possible competitor response, so a price-sensitivity result is not automatically the recommended price.
- Sensory testing: Use controlled presentation of physical samples to measure whether people can detect a sensory difference, how trained assessors describe attributes, or how target consumers rate liking. The protocol must match the question and control preparation, coding and presentation order; trained-panel descriptions and consumer preference are different evidence.
- Sequential monadic testing: Evaluate several concepts individually in sequence, controlling order effects so ratings are not simply a direct ranking exercise.
- TURF analysis: Find combinations of offerings that appeal to the largest number of distinct people, without counting someone twice because they like several items. TURF stands for Total Unduplicated Reach and Frequency; the estimate depends on the responses used.
Parent method family
Market, Customer & Behavioural Analytics explains how this method connects to adjacent methods and relevant services.
Related service families
These service families contain business questions supported by this method. Service pages link to the wider method family so readers can understand the complete analytical approach.