In recent years, discrete-choice analysis has emerged as a powerful tool in the market-research arsenal. Based on consumers' hypothetical or real-world choices, discrete-choice models help guide product managers' critical tasks.
- Positioning products in a competitive marketplace
- Developing strategic and tactical pricing strategies
- Sizing markets for new products
- Determining the optimal mix of product features
- Isolating market segments based on product preferences
- Estimating brand values among competing products
The Theory behind Discrete-Choice Models
- All transactions involve choice
By looking at choices people make, we can understand how they trade off elements of the marketing mix:
How Does Discrete-Choice Modeling Work?
We decompose customer choices into systematic ( i.e. , observable and predictable) and non-systematic ( i.e., unobservable or random) components, then we model the systematic components. In essence, we are modeling the probabilities that consumers with given profiles buy or don't buy products with certain configurations in a competitive marketplace.
Deconstruct Competing Products or Services
- Include features and pricing from competitive products
- Break the product or service into attributes, including brand and price
Design a Discrete-Choice Questionnaire
- Generate choice sets by varying attributes according to a formal experimental design
- Create shopping exercises for respondents
Conduct a Survey
- Collect survey data
- Estimate a statistical model
- Build the simulator
For more details, see our White paper on Discrete-Choice Models