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Samenvatting Conjoint Analysis
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Summary Conjoint analysisLecture 1
Why Conjoint Analysis?
In direct surveys, respondents might say they consider all attributes important
o Not informative
Conjoint enforces tradeoffs between attributes as in real purchase occasion
o All attributes evaluated at once
o Respondents evaluate “complete” products with both strong and weak attributes
Conjoint reduces problem of socially desirable answers
Conjoint adds realism
o In real-life consumers evaluate products, not isolated attributes (do they consciously
know which attributes matter?)
Conjoint analysis is straightforward
Why should we run hypothetical choice experiments if firms can increasingly make use of large
amounts of transactional purchase data?
1. Lack of experimental price variations required to learn consumers' preferences
2. Conjoint allows to measure consumer preferences for products or attribute levels not yet
introduced in the marketplace
3. Especially relevant for e.g., pricing of new product innovations
Field experiments are prominent alternatives for the goal of learning consumer preferences.
However, ...
1. Often, field experiments are difficult to conduct and not feasible in high-ticket product
categories (like cars, laptops, etc.)
2. Field experiments are limited to products already existing in the marketplace
Why is that Relevant?
Every year, many new products are introduced…
But very few succeed…
Economics
Evaluate transportation alternatives
Compare energy alternatives
Measure environmental impact
Law
Measure effects of litigation
Damage assessment
Identify boundaries between Firms
Evaluate punishment alternatives
Select jury members
Human Resources
Screen potential employees
, Design compensation packages
Select health care plans
Evaluate performance
Predict employee responses
Ranking-based conjoint:
o Choose the most-preferred product, then the second most-preferred product, … until the
least-preferred product
Rating-based conjoint:
o Give a score to each product in turn
Problems of Rating-Based Conjoint:
- Not realistic
o In real-life, we buy products rather than rating them
- Not clear whether spread in ratings is due to real preferences or due to response style
o E.g. small spread in example above, weak preferences or cautious answers?
- Implications for sales levels and market shares are not clear
o Sales and shares result from consumer choices, not ratings
o What would be the rating threshold?
Choice-based conjoint:
o Choice between different variants
Advantages of Choice-Based Conjoint
Tradeoffs are enforced even more
Realistic: the choice-setting mimics real-life
Accommodates no-choice option (”none of the offered alternatives is attractive”, “I
would stick to my current product”) ➔ sales proxy
Avoids the need of ad-hoc rules to predict market shares
No subjective scaling
Choice is cognitively less demanding than ratings (Louviere 1994)
Disadvantages of Choice-Based Conjoint
Hypothetical bias: Respondents' product choices (potentially at very large prices) might be
influenced by the experimental setting, i.e. with no consequences for actual purchase
behavior in the real world
Small individual level data: Respondents become fatigue if exposed to a large number of
choice tasks that are actually required if the experiment includes large numbers of attribute
levels
Bayesian methods and prior specifications: Bayesian statistical methods help as they
efficiently pool information across respondents (shrinkage). However, some analysts regard
the inclusion and specification of priors as subjective
- Aggregate level (same part-worths for all respondents)
o Assuming same preferences may give misleading results
o + High precision, as all respondents are combined
, o Method of analysis: LOGIT
- Segment level (different part-worths for different segments)
o + Realistic, as segments take into account different preferences
o + High precision if all respondents are used in one big analysis
o Method of analysis: LATENT CLASS ANALYSIS (LCA)
- Individual level (different part-worths for each respondent)
o + Realistic, as respondent have different preferences
o + High precision thanks to the joint estimation
o – How many strategies should the firm implement given the diversity of preferences?
o Method of analysis: HIERARCHICAL BAYES (HB)
What Can the Choice Simulator Do?
• Lets you predict which SKU respondents or segments of the population will choose (estimate
demand and market share)
• Lets you play “what-if” games to investigate the value of modifications to an existing product or
alternative
• Lets you investigate product line extensions
Lecture 2
Q: State two reasons why you nevertheless prefer conducting a conjoint experiment. Explain
1. Often, field experiments are difficult to conduct and not feasible in high-ticket product
categories (like cars, laptops, etc.). For example premium cars at low prices is not an option.
2. Field experiments are limited to products already existing in the marketplace, while a
conjoint analysis allows to analyze not yet existing product in the market place.
Q: Provide and explain three drawbacks of Rating-Based Conjoint (RBC) and explain how Choice-
Based Conjoint (CBC) addresses each of these drawbacks.
1. Not realistic, we buy and don’t rate products
2. Not clear whether the spread is due to real life preferences or due to response style
3. Implications for sales levels and market shares are not clear, at what rating do you buy?
CBC solves this:
1. Realistic choice setting, mimics real life
2. Accommodates no-choice option when their current product is better for example
3. No subjective scaling, customers express their choices by picking one of the alternatives
7 steps conjoint analysis:
Conjoint design:
1. Determine the type of study
a. E.g. rating or choice based
2. Identify the relevant attributes
a. Which and how many? Example
3. Specify the attribute levels
a. Which and how many? Example
4. Design questionnaire
a. Which product to include?
Conjoint analysis:
5. Collect data from respondents
a. Which channel, format and layout?
6. Estimate part-worths
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