Talk to Us

Conjoint and choice modeling to shape the range, the features and the price.

See which combinations of features, range and price people would choose, test your options on a simulator, and sort a long list with MaxDiff and TURF.

View product and pricing case studies

What people trade.

People choose between whole products, screen after screen, as in a store. The drawing shows how one screen and many become a simulator.

An illustration, not a client project.

Say your team makes electric bikes. This is one screen of many, as a buyer sees it.

If these were the only bikes on offer, which would you buy?

  • Option A

    Brand: Your brand
    Frame: Standard
    Battery: 80 km
    Price: $2,400

  • Option B

    Brand: Competitor
    Frame: Standard
    Battery: 80 km
    Price: $1,900

  • Option C, chosen

    Brand: Another competitor
    Frame: Folding
    Battery: 30 km
    Price: $1,400

I wouldn’t buy any of these

This buyer chose the folding bike at the lowest price. One screen can’t say which feature decided it.

Across many screens and many buyers, a model estimates what each feature and each step in price is worth. The simulator is built on it.

A sketch of the simulator your team receives

  • Add or drop a model (on in this scenario)
  • Move a price
  • Change a feature
  • Add a competitor’s model

Add a folding model at a middle price, beside today’s range.

Where its buyers would come from

  • Most from competitors’ models

  • Some from your own range

  • A few from people who chose none before

Here the new model adds to the range more than it moves buyers around within it.

Every reading assumes every buyer knows every model and can find it, at prices inside the range tested. It is never a sales forecast.

Two choice models, each built around its decision.

A range from a blank page

Deciding what the next accessories range should be

A consumer electronics company wanted to plan its next accessories range, and a way to get there from the range it had.

Buyers who use or would consider products like these chose repeatedly between products described by brand, features and price. The simulator first reproduced the market as it stood; then ranges were built from a blank page.

Four years later, the client asked us to rerun the portfolio model on its newer range.

A future product, read against the flagship

Pricing and positioning a future product for Tonal

Tonal had one product, a flagship home strength trainer, and wanted to see how a future product would fit beside it.

After a qualitative stage, prospective buyers of home strength equipment traded off brand, price and features alongside the competitive set, with the flagship alone as the baseline. Our report answered every one of the brief’s questions on a single page.

Tonal’s customer insights director has said the report was shared with its executive team and its board.

“Adam and his team were the ultimate professionals.”

Customer Insights Director, Tonal

MaxDiff and TURF, when the list is long.

MaxDiff asks people to pick the most and least important from small sets, again and again, so a long list sorts itself by what people would give up. TURF then shows which combination reaches the most people.

Choosing which snack ideas to fund

For a snack company, we put new ideas and current products through the same screen with category buyers. MaxDiff made buyers choose between ideas, and TURF estimated the new reach different combinations could add to the core range.

The brand narrowed its innovation investment to fewer territories, with a reason recorded for each one it parked.

When a choice model helps, and who builds it.

When is a choice model the right tool?

When the decision is about combinations: which features go together, how many products to offer and at what prices, beside the products people could buy instead. For a workable price range for something new, a direct pricing method can be enough.

Pricing research

How many people does a choice model need?

Enough to read the smallest group the decision depends on, such as each price band or each priority group of customers.

Can our team keep using the simulator?

Yes, within the conditions tested. Your team receives the model and the simulator to try new options inside the tested ranges, and when the market moves on, the model can be rerun with new products and prices.

Who builds the model and the simulator?

Our own data scientists and statisticians design the choice exercise and build the model and the simulator, in-house. The partner or director leading your work reads the results with your team and works through what they mean for the range.

The partners

Weighing a range, its features or its price?

Bring the options your team is weighing, with as much or as little detail as you like. We’ll say whether a choice model would settle it, and what the simulator would need to compare.

Talk to us hello@antedote.com

A partner or director will reply.