Conjoint & Discrete Choice

MMR Strategy’s Conjoint and Discrete Choice methodologies are powerful tools that help clients develop the optimal mix of features and prices for products and concepts.

 

These methods help managers decide which features to include in a product or service, or how much of a given feature to include. For example, product development for a vacuum might need to trade off power, weight, and price, while product development for an insurance product might trade off non-tangible attributes such as different types of coverage.

 

Conjoint and discrete choice studies provide a common measuring system that allows managers to compare different types of features, different levels of features, or even different price points. These studies are based in methodologies that present respondents with different bundles or combinations of features, and ask the respondents to indicate their preference for each bundle. Depending on the specific methodology used, respondents may be asked to indicate which features they prefer, their likelihood to purchase those features, and the price they might pay.

 

Our experience includes traditional conjoint techniques as well as Maximum Difference or MaxDiff techniques. We can combine these techniques with methods such as TURF, that measure the reach of different combinations of features, or Latent Class Analysis, which segments consumers according to their interest in different types of features. We can even predict the market share that a new product is likely to gain after rollout. All these outputs can be combined in a market simulation analysis that allows the client to compare different combinations of product features, prices, and other elements.

 

Case Study

Our experience with conjoint and discrete choice includes a wide variety of industry and products, including applications such as advertising messaging, product design, brand equity evaluation, pricing, competitive analysis, and market segmentation.
 
MMR Strategy helped a financial products provider seek the best message for its advertising campaign.

Problem Analysis Results
  • A financial services company in a competitive marketplace wants to understand which product features to launch in direct response advertising.
  • Online interviews were conducted with household financial decision makers.
  • Respondents were shown groups of four features and asked to select their most and least preferred features.
  • Max Diff scaling was used to rank order features from most to least preferred.
  • The research identified the three features with highest consumer preference that should be used in advertising.

 

Contact us to learn more.