Marketing Exec Pitches AI Prompts as Loyalty Program Design Tool, Bypassing Agency Fees

Marketing Exec Pitches AI Prompts as Loyalty Program Design Tool, Bypassing Agency Fees

A fractional marketing consultant published a framework Thursday for using generative AI to design customer loyalty programs, positioning the approach as a cost-effective alternative to hiring specialized agencies—a pitch that arrives as finance chiefs increasingly scrutinize marketing budgets.

Ed Poppe, founder of a fractional marketing consultancy, released the methodology on MarTech, arguing that AI tools like ChatGPT, Claude, and Gemini can help marketing teams "move from stuck to getting started faster" on loyalty initiatives without what he characterized as "six-figure agency budgets." The framework centers on using structured prompts to clarify brand positioning, segment customer opportunities, and prioritize loyalty program features.

The timing is notable. CFOs have spent the past year demanding clearer ROI metrics from marketing departments, and loyalty programs—which can require significant upfront investment in technology, rewards inventory, and customer acquisition—often face intense scrutiny during budget reviews. Poppe's argument is essentially that AI can compress the strategy phase enough to make the business case more palatable.

His methodology starts with what he calls a positioning clarity exercise, using AI to synthesize consumer behavior patterns before crafting loyalty program messaging. The example he walks through involves a coffee roaster, chosen deliberately because "coffee preferences are deeply personal"—if the framework works for a category with high individual variance, the logic goes, it should transfer to simpler product categories.

The prompt structure Poppe recommends asks AI models to first "synthesize well-established consumer patterns and frustrations in [product category] the way a CPG insights lead would analyze them," then use those insights to generate positioning statements. It's a two-step process designed to ground the AI's output in category-specific context rather than generic marketing language.

What's interesting here—and what will likely determine whether finance leaders actually buy in—is Poppe's framing around what AI can and cannot do. He explicitly states the tools "won't give you perfect answers" and describes the approach as "much less about being a prompt wizard or replacing a specialized agency" than about generating frameworks teams can pressure-test internally and refine with actual customers.

That's a more modest claim than much of the AI-in-marketing hype circulating right now, and it may be precisely why it could gain traction with CFOs. The pitch isn't "fire your agency and let ChatGPT run your loyalty program." It's "use AI to get to a testable hypothesis faster, then validate it the old-fashioned way."

The unanswered question, of course, is whether this actually works at scale. Poppe notes he's run the process through multiple AI models and "the outputs vary, but the framework works"—but there's no data in the piece on conversion rates, customer lifetime value improvements, or program ROI from companies that have used this approach.

For finance leaders evaluating marketing proposals, that's the gap that matters. A framework that compresses strategy development from weeks to days has obvious appeal, particularly if it reduces consulting fees. But loyalty programs live or die on execution, and no amount of clever prompting will fix a program with weak economics or misaligned incentives.

What Poppe has done is create a structured on-ramp for the "AI can help with this" conversation that marketing and finance teams are already having. Whether it leads to better loyalty programs or just faster bad ones will depend entirely on what happens after the prompts run.