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Neuromarketing at ThinkAlike Laboratorie...

Zhang, Zhihao;Klop...

Case

Neuromarketing at ThinkAlike Laboratories LLC: Asking the Brain, Not the Consumer

Zhang, Zhihao; Klopfenstein, Amy

M-1089 | Published July 31, 2026 | 12 Pages Case

Collection: Darden School of Business

Product Details

Set in June 2024, this case asks students to evaluate whether a firm should adopt a novel form of consumer data to solve a pressing business problem. Kerry Kinsington, marketing director for Learn Together Media (Learn Together), must decide whether to hire ThinkAlike Laboratories LLC (ThinkAlike), a neuromarketing company that uses a proprietary methodology called cross-brain correlation (CBC), which measures the degree to which viewers' brain waves synchronize while watching content, to measure audience engagement. Learn Together provides corporate e-learning courses, and several clients have complained that their employees are not paying attention to the course videos and are performing poorly on follow-up assessments. Kinsington has been unable to derive useful insights from traditional market research methods such as surveys and focus groups, and she must decide whether ThinkAlike’s neuroscience-based data can yield the information she needs. The case provides students with an opportunity to develop a generalizable framework for evaluating novel consumer data, particularly when the underlying science falls outside their own expertise. This framework has broad applicability to other emerging technologies and data sources that students will encounter in their careers. This case was originally taught to second-year MBA students toward the conclusion of "Consumer Insights in a Data-Driven World," a Darden course on how to solve marketing problems with consumer data. The case is designed for a module on emerging or frontier forms of consumer data, and shifts the challenge from the question “How do I analyze this data?” to a question that should come before it: “Should I collect this data at all?” The case works well as the first session in such a module because it introduces a structured approach for evaluating novel data that students can then apply to subsequent sessions on other emerging technologies (e.g., AI-generated insights, biometric data, and large-scale field experiments). The case is suitable for executive education programs on data-driven decision-making, MBA courses on marketing analytics or emerging technologies, and any course in which instructors wish to teach students who lack deep technical expertise in how to evaluate the promise of a new data source. It can also serve a different pedagogical function in more specialized courses on neuromarketing, consumer neuroscience, or neuroscience for business.

- Develop a generalizable framework for evaluating novel consumer data. Students learn to ask three questions of any unfamiliar data source: (1) What incremental insight does this data provide beyond existing methods? (2) Is the data valid, meaning does it actually measure what it claims to measure? (3) Is the insight worth the cost of obtaining it? This framework is the primary takeaway from the session. - Practice evaluating scientific claims without scientific expertise. Most business leaders will encounter technologies grounded in science they do not fully understand. Students learn practical strategies for vetting the credibility of a data source, including assessing different forms of evidence (e.g., peer-reviewed publications, team credentials, convergence with other measures, and predictive track records) without needing to become subject-matter experts themselves. - Understand neuromarketing as one instance of a broader phenomenon. The case uses neuromarketing as a concrete, vivid example of novel consumer-data technology. Students should leave the session not merely with an opinion about ThinkAlike, but with an appreciation for how the same evaluative logic applies to AI, virtual reality, biometric monitoring, and other technologies whose advocates promise transformative consumer insights. - Distinguish between data and insight. A recurring theme in the course is that data alone does not constitute insight. CBC produces data (brain-wave synchrony scores over time), but the insight (what to change about a video and why) requires interpretation, domain knowledge, and judgment. Students should grapple with the gap between what the technology measures and what the firm actually needs to know.