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The Key Design Problems Behind Failed Conjoint Studies

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Conjoint analysis is one of the most effective market research methods for understanding how customers make decisions. Businesses use it to evaluate pricing, product features, service options, packaging, and customer preferences. However, many studies fail to produce actionable insights because of avoidable design mistakes. The key design problems behind failed conjoint studies often begin long before the survey reaches respondents. Weak attribute selection, unrealistic scenarios, survey fatigue, and poor questionnaire structure can all reduce data quality and lead to unreliable conclusions. When these issues are ignored, businesses risk making expensive decisions based on inaccurate research. Understanding these design problems helps research teams build stronger conjoint studies that generate meaningful and decision-focused insights. Poor Attribute Selection One of the biggest key design problems behind failed conjoint studies is selecting the wrong attributes. Attributes are the...
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The Hybrid Analytics Model: When to Combine In-House Teams and Consultants

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Most companies treat analytics hiring as a binary decision: Build an in-house team Or hire external consultants But in reality, the most effective analytics strategies rarely rely on just one. They use a hybrid model —combining internal ownership with external expertise. The question is not which one is better , but when and how to use both together . What Is the Hybrid Analytics Model? The hybrid model blends: In-house teams → long-term ownership, context, and continuity Consultants → speed, specialization, and execution power Instead of choosing one path, companies layer capabilities strategically . This approach allows you to: Move faster without over-hiring Build internal knowledge while delivering results Stay flexible as business needs evolve When the Hybrid Model Makes the Most Sense 1. When You Need Results Quickly but Also Long-Term Stability Building an in-house analytics team takes time—hiring, onboarding, and aligning with business goals. Co...
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Location: United States
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What?! Causal Drivers from Cross-Sectional Survey Data. How? (LiNGAM)

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How often do we as researchers want to attribute causality to what we know is just correlational? It can be so easy to mix up correlation with causation! And in our industry we do driver analyses so often. The mix-up is easy: for example, ice cream sales and drownings both increase during summer, but it doesn’t mean that one causes the other. Correlation shows that variables change together. Causation explains which variable leads to the change in the other, and this is very important when deciding where to invest or which factors really have the effect. This distinction becomes especially important in advanced analytics approaches, where decisions depend on identifying true  drivers  rather than coincidental patterns. The Analytics Team has been finding causal drivers for over 7 years now applying cutting-edge validated methods. And we have built interactive maps and simulators around these leading insights. These take typical drivers to the next level of practical applicatio...
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Extending the Value of Attitudinal Segmentation Through Data Fusion and Lookalike Modeling

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  Most discussions around data fusion and lookalike modeling start with improving predictive accuracy—better features, better models, better performance. At AT, we approach the problem differently. Our work begins with attitudinal segmentation. These segments capture how people think, what motivates them, and why they behave the way they do. In practice, this type of segmentation delivers the highest strategic value for marketing, brand, and customer strategy. However, attitudinal segments are often difficult to apply directly. Attitudes alone are hard to target in media, hard to deploy in customer databases, and difficult to score in real time. This is where data fusion and lookalike modeling play a critical—but secondary—role. Rather than using data fusion to redefine segments, AT uses it to extend the value of an existing attitudinal segmentation . Lookalike modeling becomes a mechanism for activation: linking rich, attitudinal insight to demographic, transactional, or behaviora...
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What Is Conjoint Analysis and How Does It Support Pricing Decisions?

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  Pricing decisions are rarely straightforward. Customers evaluate products as bundles of features, benefits, and prices, making it difficult to understand what truly drives choice. Conjoint analysis was developed to address this challenge by revealing how people make trade-offs when selecting between options. Conjoint analysis is a survey-based research method used to measure how customers value different attributes of a product or service. Instead of asking respondents what they like in isolation, conjoint presents realistic choice scenarios where respondents select between alternative product profiles. Each profile varies across attributes such as features, levels, and price. How Conjoint Analysis Works In a conjoint study, respondents are shown a series of choice tasks. Each task includes two or more product options, each defined by a combination of attributes and price points. Respondents choose the option they would most likely purchase. Statistical models analyze these choic...
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Location: United States
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