A financial services company partnered with CloudResearch to conduct a nationwide survey exploring how Americans view generational wealth and legacy through our AI-powered platform, Engage. The study with 3,500 census-matched respondents, included an oversample of Black Americans to ensure meaningful subgroup analysis. Results showed that most Americans believe wealth can be preserved through planning, but many face barriers such as short-term financial pressures and limited access to financial education. The research also uncovered widespread confusion about life insurance's role in building wealth, with 73% uncertain about its benefits beyond death coverage. By leveraging Engage's platform, the company obtained high-quality, representative data that highlighted both opportunities and challenges in helping families prepare for the future.

These insights directly informed the company's strategic initiatives. The findings emphasized the critical role of financial advisors, with nearly nine in ten Black Americans valuing professional guidance for legacy planning. The company is using this knowledge to adjust product positioning, strengthen advisor education, and refine marketing messages that demystify life insurance as a tool for intergenerational support. Engage can translate consumer attitudes into actionable strategies, bridging the gap between public perceptions and financial services innovation.

Engage is advancing consumer behavior research by enabling the discovery of hidden psychological mechanisms through AI-powered conversational methodology. University of Virginia researchers tested a new approach that uses conversational AI to both measure known mediating variables and discover entirely new ones. In this study examining how payment methods (cash versus credit cards) affect spending likelihood, participants engaged in naturalistic conversations about their thought processes, free from the constraints of predetermined survey measures.

The Engage chatbot interviewer enabled inductive discovery by allowing participants to detail their reasoning through open-ended responses and conversational interactions. The platform's AI coding technology automatically converted these text responses into quantitative measures, processing qualitative data at scale while preserving authentic participant insights. This methodology successfully replicated the established “pain of payment” mediating effect, while simultaneously revealing additional mediating variables that existing scales had never previously captured.