JoveWhizz's Choice Based Conjoint (CBC) methodology quantifies how consumers make trade-off decisions between product features, benefits, and price. By simulating real-world purchase decisions, CBC reveals the relative importance of each attribute and predicts preference share for any combination of features.
Choice Based Conjoint presents respondents with a series of choice tasks where they select their preferred option from competing product profiles. Each profile is defined by a combination of attributes (features, price, brand, etc.) at varying levels. By systematically varying these combinations across multiple choice tasks, we statistically estimate the utility (preference weight) that each attribute level contributes to overall choice.
CBC is the industry standard for conjoint analysis because it closely mirrors real purchase behaviour — consumers naturally make trade-offs between multiple product attributes when making decisions. Our methodology uses efficient experimental designs to maximise statistical precision while minimising respondent burden, typically using 10-15 choice tasks per respondent.
We begin each CBC study with qualitative research (in-depth interviews or focus groups) to identify the attributes and levels that matter most in the purchase decision. This foundational phase ensures the conjoint design reflects real market dynamics. We typically include 4-8 attributes with 2-5 levels each, balancing comprehensiveness with respondent cognitive load.
Data collection is conducted through online surveys optimised for mobile and desktop. Our choice tasks use clear visual presentation with attribute levels displayed consistently. We include holdout choice tasks — fixed choice questions not used in model estimation — to validate the predictive accuracy of the utility model against actual choices.
We estimate utilities using hierarchical Bayes (HB) models, which provide robust individual-level utility estimates even with relatively few choice tasks per respondent. These utilities are then used to calculate attribute importance scores, part-worth utilities for each level, and willingness-to-pay metrics for feature trade-offs against price.
Our market simulator enables clients to test any combination of attribute levels and predict share of preference across competing product configurations. We support "what-if" scenario analysis including competitive response modelling, line extension evaluation, and optimal product configuration identification. Results are delivered through interactive simulation tools for ongoing strategic use.
CBC is widely used for new product development, pricing strategy, feature prioritisation, brand equity valuation, product line optimisation, and market structure analysis. Our CBC studies support major investment decisions including product launches, repositioning strategies, pricing architecture changes, and portfolio rationalisation.
We have delivered CBC studies across consumer goods, technology, financial services, healthcare, automotive, and B2B sectors. Our methodology adapts to different decision contexts including initial purchase, upgrade decisions, subscription choices, and B2B procurement scenarios with multiple decision influencers.
CBC presents full product profiles and asks respondents to choose between them, mimicking real purchase decisions. Traditional conjoint methods (like full-profile or trade-off matrices) use different task formats. CBC is preferred because it produces more realistic and predictive results, particularly for products with many attributes.
Sample size depends on the number of attributes, levels, and segments to be analysed. For most CBC studies, 200-400 respondents per market provides robust aggregate-level estimates. Hierarchical Bayes models perform well even with smaller samples, but larger samples enable reliable segment-level analysis.
We recommend 4-8 attributes for most CBC studies to maintain respondent engagement and data quality. Studies with more attributes can be designed using approaches like adaptive CBC or partial-profile designs, though these increase complexity in both data collection and analysis.
Yes. CBC is highly effective for B2B research where purchase decisions involve multiple attributes and complex trade-offs. We adapt choice tasks to reflect B2B procurement dynamics including total cost of ownership, service levels, supplier relationships, and risk considerations.
Willingness to pay is calculated by comparing the utility of a feature change against the utility of a price change. The ratio of these utilities indicates the maximum price premium consumers would accept for a given feature improvement, providing direct input for pricing and feature investment decisions.
A standard CBC study takes 4-6 weeks from design to final reporting. The timeline includes qualitative pre-work (1 week), survey design and programming (1 week), data collection (1-2 weeks), and analysis and reporting (1-2 weeks).
Contact us to discuss how our Choice Based Conjoint methodology can help you optimise your product and pricing strategy.
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