In Part 1 of this two-part article, we explored how we can move from behavioral insights to behavioral intelligence by understanding and reconciling complexity and distilling parsimonious explanations of behaviors. In this second part, we turn to other aspects of behavioral intelligence: the integration of data on an individual level for personalization, the integration of empirical findings to derive pooled knowledge, and the scaled application of behavioral insights.

We previously discussed the importance of integrating fragmented ideas by finding common drivers. The integration of information about a person, on the other hand, often requires the assimilation of heterogeneous data sources. Consider data held by financial services providers. Demographic information (captured when the customer is onboarded, for example) provides a baseline context such as age, income, education, or family status. Transactional data reflects actual financial choices and preferences (e.g., about spending or investing). Additional psychometric assessments can measure behavioral factors (e.g,. risk tolerance or long-term orientation). And digital interaction data (e.g., clicks, browsing paths, or engagement patterns) can capture where attention is directed in real time. When integrated under a coherent behavioral framework, these datasets can help personalization by providing a multidimensional view of decision-making that is richer than any single source.

Personalization can take the form of product recommendations, improvements to user experience, or interventions to increase financial wellness. For example, research on how people work with financial advisors I’ve been involved in divided people into maximizers (people who try to make the best possible decision) versus satisficers (who are happy with good-enough decisions). Our data showed that maximizers preferred to get more........

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