In marketing research, discrete choice is a statistical technique used to learn about how people make decisions and why. It is an important part of conjoint analysis and is often used to model how people choose between a set of alternatives, such as brands, goods, or services, where each alternative is described by a set of attributes.
Why do Organizations use Discrete Choice?
- Simulates Real-World Decisions
- Discrete choice mimics actual consumer behavior by asking people to choose between realistic product or service alternatives rather than rating or ranking them.
- Reveals Trade-offs Consumers Make
- Helps identify which attributes matter most (e.g., price vs. quality) and how much people are willing to give up in one area to gain in another.
- Estimates Willingness to Pay (WTP)
- Quantifies how much value customers place on individual features, enabling better pricing strategies.
- Supports Product Design and Optimization
- Businesses can design or tweak products based on combinations of attributes that drive the most value or preference.
- Market Share Simulation
- Predicts how new products or changes to existing products might perform in the market, including competitive responses.
- Segments the Market by Preferences
- Identifies different consumer segments based on their choice patterns, enabling targeted marketing strategies.
- Improves ROI of Marketing Decisions
- Offers data-driven insights to guide product launches, pricing, positioning, and promotional strategies.
A Discrete Choice Case Study
Example: Brand X wants to design a new beauty product and price it competitively against top competitors. They commission a discrete choice study to:
- Identify the most valued features
- Determine willingness to pay
- Forecast market share for different product concepts
- Define the Alternatives
You start by identifying the products or services consumers will choose from. Each option is a bundle of features (called attributes) with specific levels.
Example: Beauty Product
- Brand: X, Y, Z
- Price: $69, $89, $109
- SPF: 15, 30, 45
- Design Choice Sets (Conjoint Design)
You create choice sets, where each set includes 2–5 hypothetical product profiles (alternatives), and respondents are asked to pick their favorite from each set.
Example choice set:
Which Option would you choose?
- Option A: Brand X, $69, SFP 30
- Option B: Brand Y, $89, SPF 45
- Option C: Brand Z, $69, SPF 15
- Collect Responses
You present several of these choice sets to each respondent (usually 8–12 sets) and record their selections.
- Estimate Utility Scores (Part-Worths)
Statistical models are used to estimate how much utility (value) each level of each attribute contributes to the overall preference.
- Common models:
- Multinomial Logit (MNL)
- Mixed Logit
- Hierarchical Bayes (HB)
Each model assumes respondents make decisions to maximize utility.
- Analyze Results
From the estimated utilities, you can:
- Calculate attribute importance
- Estimate willingness to pay (WTP)
- Run market simulations to see how changes in product design or pricing might affect market share
Example Output:
|
Attribute |
Level |
Utility Score |
|
Brand |
X |
+1.2 |
|
Y |
+0.8 |
|
|
Z |
+0.4 |
|
|
Price |
$69 |
+1.0 |
|
$89 |
+0.5 |
|
|
$109 |
0.0 |
Higher utility = higher preference
Business Growth Strategies using Discrete Choice
Once the utilities are known, businesses can:
- Optimize product design
- Forecast market demand
- Segment customers
- Set competitive pricing