We size the groups in your market, show which option people would choose and at what price, and say how far each answer can be trusted.
We group your market by need and behavior, size each group, and find it again in later surveys, or in your customer records where those have the right variables.
People choose between whole products, screen after screen. One choice doesn’t say why; a model of many estimates what each feature and each step in price is worth.
Picking the most and least important from small sets, again and again, sorts a long list. TURF then finds the set that would reach the most people.
When the decision involves features, tiers or competing offers, a choice model can help. For other price questions, a direct method or a live test may be more useful.
When the question is why people choose as they do, start with qualitative research. When your own data can size the opportunity, add data science and analytics.
We read the size of a difference, the uncertainty around it and the consequences of acting on it. A significance marker helps interpret the evidence; it doesn’t make the decision for you.
A letter: the gap meets the test’s threshold under its assumptions. Whether it matters commercially is a separate question.
No letter: the gap doesn’t meet that threshold. Read the estimate, its uncertainty and the other evidence before deciding what to do.
Too few people: the estimate is imprecise, however big the gap looks. We make that uncertainty visible.
A drawing: one page of a table printout comparing five groups of people, A to E, on five measures, with a base row showing how many people stand behind each group. Printed letters mark the differences that meet the test’s threshold, and a pen rings one of them. A gap with no letter is underlined in pen with a question mark. Group E, with very few people behind it, is boxed and hatched out in pen, although its bars are the longest on the page.
Quant measures what people choose under the conditions studied. It doesn’t predict exactly what people will do, so each answer comes with its limits.
Choosing which home-heating customers to prioritize
For a home services company in Europe, we built and sized a segmentation, then tested versions of its existing service proposition. Each person saw one version, allocated at random, and interest was read as conditional on buying a new heating system.
The segments went into use: the client’s media agency planned around the priority segment, and the company read its next proposition test through them.
Choosing which personal care buyers to target in each market
For a personal care company, we surveyed category buyers in four European markets, at a scale chosen so that smaller groups could still be read within every market.
The central and local teams came away with one segmentation they could all use.
Deciding what goes into a consumer technology roadmap
For a consumer technology company, engineering and marketing agreed before fieldwork began what evidence they would trust. A conjoint model and simulator then tested portfolio options, every output a preference modeled under the conditions tested, and never a market forecast.
Engineering and marketing had one shared set of evidence to plan the roadmap from.
EXPLOR Award winner, Insights Association, for a segmentation and market model, voted by the Corporate Researchers Conference.
Bring the decision and what you already know. We’ll suggest the method that would answer it, and say how sure the answer could be.
A partner or director will reply.