Four Next Generation CBCA Tools to Understand How People Make Choices

By Steve Cohen

Mar 12, 2024

Marketing aims to influence the decision-making moment when a buyer selects your brand or item. This choice translates to adoption, increased revenue, loyalty, and growth. For decades, researchers have been looking for an unquestionable source of data-supported insights to determine what influences consumers’ choices and guides decisions. With this information, companies can set priorities for customer experience, product development, optimal pricing, and messaging, ensuring that choice equals sales and ultimately enabling businesses to measure ROI of marketing initiatives before engaging in them.

Since its inception, Choice-based Conjoint Analysis (CBCA) has proven invaluable for brands aiming to grasp consumer behavior. Initial developments in CBCA evolved into useful survey techniques focused on unraveling decision hierarchies and understanding how tradeoffs influence decision-making. Undoubtedly, the methods developed years ago were revolutionary for their time, but even the best marketing research methods have their shortcomings. What was previously thought of as the “gold standard for choice modeling,” has reached its limits without further refinements based on in-depth understanding of human behavior.

As a pioneer in the introduction, development, and commercialization of CBCA and related tools like MaxDiff and Menu-based CBCA, I can tell you that it is finally time for our industry to step up, to stop using outdated and simplistic models and methods, and to embrace superior solutions.

Advancements in the science of decision-making have spurred innovations that challenge conventional wisdom by incorporating more robust behavioral assumptions. These developments, primarily driven by academic research, remain largely unknown to most practicing researchers.

As longstanding pioneers in applying choice methodologies to marketing issues, in4mation insights (i4i) continues to leverage state-of-the-art techniques to tackle our clients’ evolving business challenges. Stemming from our extensive investigations in marketing science and behavioral economics, these innovations enhance our ability to forecast choice behavior and to drive superior business outcomes.

These new advancements in choice modeling, as elaborated below, greatly improve our comprehension of the factors influencing consumer choice. The insights derived from these innovations empower companies to better uncover optimal product attributes and pricing models that resonate with consumers, all while understanding the intricacies of decision-making within the confines of their everyday lives and budgets.

These are not minor adjustments – we are highlighting these advancements to fundamentally overhaul CBCA’s alignment with data, altering the direction and intensity of choice effects, yielding more precise predictions of consumer behavior and business results.

Our new advances in CBCA tools are:

1. Constrained Choices

An essential concept in decision making is that all choices are made under constraints. Primarily among these are having enough time and enough money, which are particularly pressing in today’s post-pandemic, inflationary climate.

An individual buyer’s budget offers a threshold beyond which they will not purchase an item, no matter its attractiveness. With so many news articles describing how consumers and businesses are tightening their belts and watching what they buy, understanding the role of a budget in choice is even more critical than it has been in the past.

However, while CBCA does not incorporate the buyer’s budget into its price sensitivity estimations and choice predictions, a new Budget-Constrained CBCA (BC-CBCA) does just that.

By employing budget constraints in CBCA, our i4i approach offers more realistic predictions of buyer behavior across product categories, from expensive durables to groceries purchased at supermarkets. BC-CBCA identifies segments with similar budgetary limits, thus enabling product designs and offerings tailored to different groups. Importantly, the underlying math of BC-CBCA guarantees a fit to the data that is as good as or even better than standard CBCA.

2. Do people pay attention to everything?

A foundational assumption of standard CBCA is that everyone processes all the information available to them and they make compensatory trade-offs. But what if they do not, and instead, they use simplifying rules to pay attention to just a subset of the information? Attribute Non-Attendance (ANA) uncovers who does and who does not pay attention to all the information. The result is that ANA enhances traditional CBCA by offering more accurate predictions, since it enables us to uncover the impact of the true salient attributes affecting choice. Our recent work has revealed that sometimes as little as 10% of consumers consider all attributes in their decision-making, strongly challenging the bedrock assumption of standard CBCA.

3. Asymmetric and Symmetric Choices

The standard statistical tool of CBCA is Multinomial Logit (MNL) which governs the math behind choice modeling. A less well-known aspect of MNL is its symmetric prediction of choice behavior. At a 50% likelihood of purchase, equal increases or decreases in product attractiveness (utility) will produce equal gains and losses in choice shares. The symmetric property can lead to unrealistic predictions, especially when the final predicted choice shares among alternatives varies widely — a scenario all too common in virtually all choice studies.

A new enhancement to standard MNL abandons the symmetry assumption in favor of asymmetry. At i4i, we have found that employing asymmetry in choice fits choice data better than symmetric MNL, more accurately reflecting real-world decision-making patterns, and yields vastly different choice share predictions.

4. Regret minimization (RM) versus Utility maximization (UM)

While conventional CBCA approaches assume that buyers aim to maximize the utility of their purchases, some opt for products that help them avoid losses. In contrast to Utility Maximization, Regret Minimization closely aligns with the behavioral economics principle that “losses loom larger than gains.” If we acknowledge that not all people are utility maximizers, it’s crucial to have choice models that can accommodate different decision-making strategies. Our experience at i4i has proven that a hybrid method, recognizing that some individuals prioritize maximizing utility while others focus on minimizing losses, produces superior results, rather than a monolithic approach where all individuals exclusively use UM to the detriment of RM or vice versa.

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We will be expanding on the details behind these four new developments in choice modeling in the coming weeks.

We are excited about the potential that these new cutting-edge enhancements to CBCA tools offer marketers, especially during these challenging times.

Whether these innovations address the gaps you have encountered with traditional CBCA or if you’re simply intrigued by their potential application in your business, please reach out to us at

Let’s explore how we can enhance your choice architecture to drive sales growth.