Hi there,

I’m writing again. I missed sharing insights with my past and present clients, as well as all my business friends, so…

Pattern Recognition is a short, occasional email from me. I plan to highlight an overlooked insight, a shift in customer behavior that I’ve been tracking, or a pattern in the marketing field that might matter more than it first seems. These insights are a little different from the long-form work I still write on The Findings Report. These are meant to be instantly actionable, drawn from my work at Conclusive and in the classroom, teaching at USC.

Below is a piece I just published about why so many marketing teams are flying blind and how custom, reproducible research designs can remove the guesswork and improve marketing effectiveness.

I hope we have a chance to connect soon!

Larry

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As confidence in the economy continues to wobble, many marketing leaders are cautious about how to invest in their customer pipeline. Is now the time to prioritize acquisition, development, or retention? In our recent work, we’ve found that existing insight frameworks offer little help in answering these questions. Top-of-funnel KPIs such as awareness are often irrelevant for established brands. Meanwhile, bottom-funnel metrics such as satisfaction or NPS are poor barometers for marketing mix decisions. They rarely demonstrate the cause-and-effect relationships that drive real outcomes like incremental sales, cross-selling, upselling, or reduced churn.

Much of the problem lies in overly templatized research design. Too many studies default to generic question sets and abstract metrics that aren’t anchored in the realities of the client’s customer journey. The best insights come from customized approaches that reflect a brand’s unique business model, growth stage, and customer dynamics. And critically, the most effective studies integrate multiple data sources, especially first-party data from CRM, transaction systems, and engagement platforms.

For example, in a recent project for a cloud-based SaaS company, we measured customer commitment using a validated psychometric scale and linked the survey responses to behavioral patterns in the company’s CRM system. This design allowed us to isolate which types of emotional and motivational commitment were most predictive of renewal, expansion, and advocacy. Had we focused solely on indirect metrics like satisfaction, we would have missed the nuances that revealed actionable levers in the client’s marketing mix. Instead, we delivered not only a diagnosis of what mattered but an interactive dashboard that allowed the client to simulate outcomes based on different investment choices.

As marketing leaders continue to face pressure to do more with less, those who take a rigorous, behaviorally anchored approach to insights will be better positioned to justify investment and outperform competitors. Traditional metrics such as satisfaction and NPS have a role to play, but the best research designs offer a more robust diagnostic model that links motivations, emotions, and perceptions to actual behavior.

At Conclusive, we advocate for open-source research design: methodologies that are transparent, purpose-built, and reproducible. There are no black boxes. Our work on customer commitment is a good example. It is grounded in a validated psychometric framework that was published in a peer-reviewed journal and pressure-tested across industries. But the end-result was a decision model the client could use with confidence because, while it had been developed by leaders in the research and academic community, it was tailored to the management’s specific needs, challenges, and business context.