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A Practical Guide To Conjoint Analysis
In a sentence
A concise practical guide that explains how conjoint analysis infers the value consumers place on individual product attributes by analyzing their ratings of or choices among realistically described products.
If you need to know what customers truly value in a product—and what tradeoffs they'll accept—this practical guide teaches you conjoint analysis, the marketing research technique that uncovers hidden attribute preferences without asking consumers to value features in isolation. Through a running sports-car example, it walks you step by step from constructing an experimental design of attributes and levels, through data collection with off-the-shelf software, to interpreting part-worth utilities. You'll learn three high-value applications: trade-off analysis (e.g., how much more you can charge if you add a sunroof), market-share forecasting using the multinomial logit model, and computing attribute importances as percentage decision weights. Written for managers and analysts, it demystifies the math just enough to make you a confident, accurate user of a powerful marketing decision aid.
The four lenses
- Science
- Statistics
- Systems
- Strategy
Tags
The model
A framework in which the researcher's experimental design (attributes and levels) and data-collection method drive the estimation of part-worth utilities, which in turn determine trade-off valuations, attribute importances, and predicted market share.
Experimental Design Qualitydesign lever
The structure of attributes and levels chosen for the study, including how tangible and concrete the levels are, how many levels are tested, and whether ranges span realistic alternatives—shaping the validity of all downstream estimates.
Data Collection Methoddesign lever
The procedure and instrument used to gather respondent ratings or choices among hypothetical product profiles, typically via PC or web-based conjoint survey software that generates profiles from the experimental design.
Consumer Attribute Preferencespsychological state
The underlying, often inarticulable, values consumers place on individual product attributes and levels, which conjoint analysis seeks to infer rather than elicit directly from respondents.
Estimated Part-Worth Utilitiesoutcome metric
The numerical attribute-level utilities estimated from respondent ratings or choices, scaled within each attribute to sum to zero, representing average consumer preference for each level and serving as the core conjoint output.
Trade-off Valuationoutcome metric
Computed estimates of how much consumers would give up on one attribute (e.g., price) to gain improvement in another (e.g., a sunroof), derived by additive utility arithmetic and interpolation across quantitative levels.
Predicted Market Shareoutcome metric
The forecasted share of choices a product will capture within a defined competitive set, computed by applying the multinomial logit model to the summed utilities of each competing product profile.
Attribute Importanceoutcome metric
The percentage decision weight assigned to an attribute, calculated as its within-attribute utility range divided by the sum of all attributes' ranges, indicating how much each attribute drives the overall choice process.
How they connect
- experimental design quality → influences estimated partworth utilities
- data collection method → influences estimated partworth utilities
- consumer attribute preferences → predicts estimated partworth utilities
- estimated partworth utilities → predicts tradeoff valuation
- estimated partworth utilities → predicts predicted market share
- estimated partworth utilities → predicts attribute importance
- experimental design quality → moderates attribute importance
- experimental design quality → moderates tradeoff valuation
The process
The book presents a playbook for conducting and applying conjoint analysis to understand customer preferences. The overall process begins by designing a study that defines a product in terms of its core attributes (e.g., price, brand) and specific levels for each attribute (e.g., $25,000, Toyota). This experimental design is then used to collect data by asking target consumers to rate or choose between different hypothetical product profiles. The core output of this analysis is a set of 'utility' scores, which quantify the value consumers place on each individual attribute level. Once these utility scores are established, the playbook branches into several practical applications. The first is trade-off analysis, which uses the utility scores to calculate consumers' willingness to exchange one feature for another, such as determining the price increase a new feature can support. The second application is market share forecasting, which uses a logit model to predict a product's performance against a known set of competitors. The final application is determining attribute importance, which calculates the percentage weight each attribute carries in a consumer's overall decision-making process. This sequence—from experimental design and data collection to the application of utility scores for trade-offs, forecasting, and importance weighting—forms a comprehensive method for making data-driven product design and marketing strategy decisions.
Conducting a Conjoint Analysis
To determine how consumers value the different attributes that make up a product and the tradeoffs they are willing to make among them.
When to use: When a manager needs to predict market share, determine willingness-to-pay, or quantify feature trade-offs for a new or existing product.
Step 1Design the experiment by defining product attributes and their levels.
Entry: The key features that define the product and its competitive space have been identified.
Exit: A complete list of all attributes and the specific levels for each attribute to be tested is finalized.
In: Product features under consideration · Out: Experimental design document listing all attributes and levels
Step 2Collect data from respondents.
Entry: The experimental design is complete and a sample of target consumers is available.
Exit: A sufficient amount of rating or choice data has been collected from respondents.
In: Experimental design, Target market respondents · Out: Respondent rating or choice data
Step 3Estimate and interpret attribute level utilities.
Entry: Respondent data collection is complete.
Exit: A table of estimated utilities for each attribute level is generated and its statistical significance is assessed.
In: Respondent rating or choice data · Out: Table of estimated attribute level utilities (part-worths), Statistical significance metrics (e.g., t-values)
Performing a Trade-Off Analysis
To determine what consumers would be willing to give up on one attribute to gain improvements in another, often used to calculate willingness-to-pay for a feature.
When to use: After a conjoint analysis has been run and attribute level utilities are available, to evaluate product design changes.
Step 1Calculate the total utility of a baseline product.
Entry: A table of attribute level utilities is available.
Exit: The total utility of the baseline product is calculated.
In: Attribute level utilities, Baseline product profile · Out: Total utility of baseline product
Step 2Calculate the utility change from a product modification.
Entry: Baseline product utility is known.
Exit: The net gain or loss in utility from the modification is quantified.
In: Baseline product utility, Modified product profile · Out: Utility change value
Step 3Determine the compensating change in a second attribute.
Entry: The utility change from the modification is known.
Exit: The equivalent value of the modification is expressed in terms of another attribute (e.g., willingness-to-pay).
In: Utility change value, Utility values for the second attribute · Out: The specific change in the second attribute that creates indifference (e.g., the price increase a feature can support)
Forecasting Market Share
To predict the market share of a product within a defined set of competing products.
When to use: When launching a new product or assessing the impact of a change to an existing product against known competitors.
Step 1Define the competitive set and product profiles.
Entry: A table of attribute level utilities is available and the competitive landscape is understood.
Exit: A complete list of product profiles for the entire competitive set is created.
In: Market intelligence on competitor offerings · Out: List of product profiles for the competitive set
Step 2Calculate the total utility for all products in the set.
Entry: All product profiles for the competitive set are defined.
Exit: A total utility score is calculated for every product in the set.
In: Attribute level utilities, List of product profiles for the competitive set · Out: A total utility score for each product
Step 3Apply the multinomial logit model to calculate market share.
Entry: Total utility scores for all products in the competitive set are available.
Exit: A predicted market share percentage is calculated for the target product.
In: All product utility scores · Out: Predicted market share percentage
Determining Attribute Importance
To quantify the relative importance (decision weight) of each attribute in the consumers' overall choice process.
When to use: After a conjoint analysis has been run, to understand which product features have the most influence on consumer choice.
Step 1Calculate the utility range for each attribute.
Entry: A table of attribute level utilities is available.
Exit: The utility range for each attribute is calculated.
In: Attribute level utilities · Out: Utility range for each attribute
Step 2Sum all attribute utility ranges.
Entry: The utility range for each attribute has been calculated.
Exit: The sum of all utility ranges is calculated.
In: Utility range for each attribute · Out: Total utility range across all attributes
Step 3Calculate the importance weight for each attribute.
Entry: The individual and total utility ranges are known.
Exit: An importance weight (percentage) is calculated for each attribute.
In: Individual attribute utility ranges, Total utility range · Out: Importance weight (percentage) for each attribute
The story
The reader A manager or marketing analyst who wants to understand what customers truly value in a product and predict how design choices affect preference and market share.
External problem
They cannot reliably learn how much each product attribute is worth to consumers or what tradeoffs customers will accept.
Internal problem
They feel uncertain and exposed making costly product-design and pricing decisions on guesswork or unreliable direct surveys.
Philosophical problem
Asking consumers to value features in isolation is fundamentally inaccurate; decisions deserve a method that reflects how people actually choose.
The plan
- Build an experimental design listing all attributes and their concrete, tangible levels.
- Collect data by having respondents rate or choose among software-generated product profiles.
- Estimate part-worth utilities for each attribute level from the responses.
- Apply the output to trade-off analysis, market-share forecasting via logit, and attribute-importance computation.
- Interpret results carefully given the scaling of utilities and the ranges tested in the design.
Success
- Confident, evidence-based product-design, feature, and pricing decisions.
- Accurate prediction of a new product's market share within a competitive set.
- Clear quantification of how much customers value each feature and the tradeoffs they'll accept.
- Better marketing segmentation through group- or individual-level utility estimates.
At stake
- Costly product and pricing decisions based on inaccurate direct-question surveys or intuition.
- Misreading statistical output and dropping or keeping the wrong attributes.
- Designing products customers don't value and losing share to competitors.
Chapter by chapter
ch01Chapter 1
Questions this book answers
- How much do consumers value each attribute and level of a product?
- What tradeoffs are customers willing to make among product features?
- How can a firm predict the market share of a proposed new product within a competitive set?
- Which product attributes matter most in consumers' choice processes?
- How should an experimental design of attributes and levels be constructed to yield valid, usable results?
Glossary
- Experimental Design Quality
- The degree to which the chosen attributes and their levels are tangible, appropriately numerous, and span realistic ranges so that the resulting conjoint estimates are valid and usable.
- Data Collection Method
- The instrument and procedure used to capture respondents' ratings of or choices among hypothetical product profiles generated from the experimental design.
- Consumer Attribute Preferences
- The underlying values consumers place on individual product attributes and levels, which are difficult to articulate directly but govern real choices.
- Estimated Part-Worth Utilities
- The numerical attribute-level utilities estimated from respondent data, scaled within each attribute to sum to zero, representing average preferences and forming the core conjoint output.
- Trade-off Valuation
- An estimate of how much consumers would sacrifice on one attribute to gain an improvement on another, expressed in the units of the traded attribute (e.g., dollars).
- Predicted Market Share
- The forecasted proportion of choices a product captures within a defined competitive set, based on the relative utilities of all competing product profiles.
- Attribute Importance
- The relative weight an attribute carries in the overall choice process, expressed as a percentage of total decision weight across all attributes.
Tools these methods power
Related in the literature
The measurement literature behind this signal — sourced, so you can defend it.
“A PRACTICAL GUIDE TO CONJOINT ANALYSIS Introduction Conjoint analysis is a marketing research technique designed to help managers determine the preferences of customers and potential customers. In particular, it seeks to determine how consumers value the different attributes…”
— A Practical Guide to Conjoint Analysismatch 71%
“for "Price" is about 27%, "Brand" about 15%, "Sunroof' about 10%, and "Upholstery" about 23%. The numbers provide a very intuitive metric for thinking about the importance of each attribute in the decision process. Final Thoughts Conjoint analysis has a broad array of possible…”
— A Practical Guide to Conjoint Analysismatch 61%
“new product design. The Anatomy of a Conjoint Analysis Literally, conjoint analysis means an analysis of features considered jointly. The idea is that, while it is difficult for consumers to tell us directly how much each feature of a product is worth to them, we can infer the…”
— A Practical Guide to Conjoint Analysismatch 59%
Resources: A Practical Guide to Conjoint Analysis