Introduction to ChoiceBased Conjoint CBC Adapted from Sawtooth

Introduction to Choice-Based Conjoint (CBC) Adapted from Sawtooth Software, Inc.

Conjoint Methods: Card-Sort Method (CVA) Using a 100 -pt scale where 0 means definitely would NOT and 100 means definitely WOULD… How likely are you to purchase… Coke 6 -pack $1. 89 Your Answer: ______

Conjoint Methods: Pairwise Method (ACA or CVA) Which would you prefer? Coke 6 -pack $1. 89 Strongly Prefer Left 1 2 3 4 Pepsi 8 -pack $2. 29 5 Strongly Prefer Right 6 7 8 9

Choice-Based Conjoint Question

Comparing the Methods (cont. ): Traditional Card Sort: – Respondent task is not as realistic as CBC – Ranking or ratings typically provide enough information to compute utilities (preferences) for each individual – Usually only compute Main Effects (no interactions)

Comparing the Methods (cont. ): Pairwise Presentation: – Respondent task is often not as realistic as CBC – Ratings typically provide enough information to compute utilities (preferences) for each individual – Usually only compute Main Effects (no interactions)

Comparing the Methods (cont. ): Choice-Based Conjoint Pros: – Making choices in CBC questions is similar to what buyers do in the marketplace – CBC can include a “None” option, so respondents who have no interest in purchasing can opt out of the question – Because we can analyze results by pooling respondent data, CBC permits measurement of Main Effects AND Interactions. More overall parameters can be estimated.

Comparing the Methods (cont. ): Choice-Based Conjoint Pros (cont. ): – Because we can pool respondent data, each respondent can answer as few as just 1 question – Respondents can answer at least up to 20 choice questions with high reliability – Randomized designs permit showing respondents all combinations of levels and are quite efficient – Particularly well suited to pricing studies

Comparing the Methods (cont. ): Choice-Based Conjoint Cons: – Choices are inefficient: they indicate only which product is preferred, but not by how much – Aggregate models assume respondent homogeneity, which may be inaccurate representation for a market (but Latent Class analysis and new developments in Bayesian estimation techniques help resolve this issue) – Usually requires larger sample sizes than with CVA or ACA

Comparing the Methods (cont. ): Choice-Based Conjoint Cons (cont. ): – Tasks are more complex, so respondents can process fewer attributes (CBC recommended <=6) – Complex tasks may encourage response simplification strategies

Comparing the Methods (cont. ): Analyzing the Data: – ACA: Ordinary Least Squares regression (OLS) or Hierarchical Bayes (HB) – CVA: OLS (ratings), Monotone regression (rankings) or or Hierarchical Bayes (HB) – CBC: Counting analysis, Multinomial Logit, Latent Class, ICE, or Hierarchical Bayes (HB)

Main Effects Versus Interactions Main Effects: - Isolating the effect (impact) of each attribute, holding everything else constant Assume two attributes: – BRAND: Coke, Pepsi, Store Brand – PRICE: $1. 50, $2. 00, $2. 50

Main Effects Versus Interactions (cont. ): Hypothetical Main Effects Utilities: Interpretation: Across all brands (holding brand constant), $1. 50 is worth 80 points, etc.

Main Effects Versus Interactions (cont. ): We can add the main effect utilities together and infer the preference for each brand at each price. But this assumes the same price function for each brand.

Main Effects Versus Interactions (cont. ): This may not be an accurate representation of how price changes affect preference for each brand. Perhaps price changes have a different impact depending on the brand. That would imply an interaction.

Main Effects Versus Interactions (cont. ): CBC counts the percent of times each brand/price combination is chosen. Each cell in the grid above is directly and independently measured (two-way interaction).

Main Effects Versus Interactions (cont. ): The Store Brand is more price sensitive to changes in price compared to Coke and Pepsi. Coke buyers are most loyal in the face of price changes.

Main Effects Versus Interactions (cont. ): There are many other kinds of interactions besides Brand x Price: Preference for color depends upon the car

Sawtooth Software’s CBC Systems • Windows- or Web-based computer-administered interviews or paper surveys • Capacity: 10 attributes with up to 15 levels each, up to 16 concepts per task • Experimental design produced automatically • Prohibitions between attribute levels can be specified • Fixed designs can be specified • Choice sets can include a “none” or “constant” option • Data analyzed automatically by counting or multinomial logit, optional modules for Latent Class and HB • Market simulator included

The CBC System: Advanced Modules • Paper and Pencil Module – Assists in creating and analyzing data for paper and pencil interviews • Latent Class Segmentation Module – Detects and models market segments – Helps relax the assumption of homogeneity, but still does not achieve individual-level data – Permits specification of linear terms, and respondent weighting • Hierarchical Bayes Analysis CBC/HB

Advanced Design Module • Advanced Design Module: – Support “brand-specific attribute” designs and estimation (some researchers refer to these as “true” discrete choice designs) – More than one “Constant Alternative” (None) option – Expanded number of attributes to accommodate brand-specific attribute designs (up to 30 attributes) – Ability to conduct/analyze partial-profile experiments

Why Latent Class, HB and ICE? • To reduce the Red Bus/Blue Bus (IIA) Problem, one must account for: – Substitution effects – Differential cross-elasticities – Differential self-elasticities

Aggregate Logit • Assume an aggregate logit solution where: – Utility (Train) = Utility (Red Bus) On any given day, difficult to predict which way any one respondent will travel to work. Resulting in the following aggregate shares: – Train 50%; Red Bus 50%

Aggregate Logit: • Assume we add another alternative where: – Utility (Train) = Utility (Red Bus) = Utility (Blue Bus) Again, difficult to predict which way any one respondent will travel to work. • Train 33. 3%; Red Bus 33. 3%; Blue Bus 33. 3% • Net Bus ridership increased from 50% to 66. 7% by offering a bus of a different color

Two-Group Latent Class Solution: • Left Half of Room – Strongly Prefer Buses • Right Half of Room – Strongly Prefer Trains In aggregate, it still appears that Utility (Bus) = Utility (Train)

Two-Group Latent Class Simulation: • Now offer both Red and Blue buses • Net Bus ridership still 50% (no Share Inflation) Capturing heterogeneity has resulted in differential substitution effects

Differential Cross-Elasticity under Latent Class • Now raise price of Blue Bus – Many Blue Bus riders shift to Red Buses – Train ridership unaffected Capturing heterogeneity has revealed differential cross-elasticity

Differential Elasticity under Latent Class • Assume: – Train riders = Not price sensitive – Bus riders = Very price sensitive

Differential Elasticity under Latent Class • If raise Train price – Few train riders shift to buses • If raise Red and Blue bus prices – Many bus riders shift to trains Capturing heterogeneity has captured differential elasticities

Conclusions • Capturing heterogeneity under Latent Class, HB or ICE – Reduces Red Bus/Blue Bus problem – Automatically accounts for differential substitution, elasticities and cross effects with simple main-effects models • If those effects are due to differences in preferences between people