Nita Patel, CMO of Lickly: “Marketing doesn’t have an information problem. It has a decision problem.”

Influencer marketing has become a sizeable industry, but the way brands choose who to work with has often remained surprisingly simple: look at follower counts, check engagement rates and decide which creator appears to offer the biggest reach.

For Nita Patel, Chief Marketing Officer at Lickly, that approach misses the more important question of who a brand is actually trying to influence. Lickly was built around an audience-first approach, looking beyond headline creator metrics to understand behaviours, communities and cultural signals before making decisions about which creators and strategies might be appropriate.

That thinking has since expanded beyond influencer marketing. Nita argues that marketers now have access to more data, signals and AI-generated recommendations than ever, but that abundance does not necessarily make the decisions themselves easier. The challenge is turning all that information into recommendations that marketers can understand, evaluate and ultimately defend.

Lickly describes this as “Decision Intelligence” – using AI, evidence and structured reasoning to help marketers determine not just what they could do, but what they should do next. Its proprietary M³VR™ methodology is designed to question, verify and validate recommendations across multiple models, signals and reasoning paths, with the evidence presented alongside the recommendation.

For Nita, however, the point is not to remove people from the decision-making process. As she puts it, marketers still bring context, experience and judgment, particularly when real budgets and brand reputation are at stake.

In this interview, Nita discusses why influencer marketing needs to look beyond follower counts, what prompted Lickly’s evolution into a broader marketing Decision Intelligence platform, and why the growing use of AI makes evidence and accountability more important rather than less. She also explains what the experience of “Building Lickly with Lickly” has revealed about how marketers actually want to use AI.

Influencer marketing has become a $46 billion industry, yet brands still often use follower counts and engagement rates as shortcuts for deciding which creators to work with. Why do you think the industry has been so slow to move toward more meaningful audience data?

Follower counts and engagement rates became shortcuts because they’re easy to see, easy to compare and easy to put into a report. But easy to measure doesn’t necessarily mean meaningful.

The limitation is that influencer marketing has historically started with the creator instead of the audience. Brands focused on which influencers to use before establishing who they were actually trying to influence.

Once you start with the audience, the decision becomes more informed. You’re looking at behaviors, communities, cultural signals and what actually moves people, then determining which creators and strategies are best aligned to reach them. Reach still matters, but it needs context. A large audience that isn’t relevant to the people you need to influence can be a very expensive audience.

Lickly started with an audience-first approach to influencer marketing, but you’ve said that work exposed a much bigger problem around marketing decision-making. What did you discover?

Marketers have access to an enormous amount of information, but turning all of that intelligence into a clear decision remains difficult.

Influencer marketing made that especially clear. Once we understood the audience, we still had to determine which creator to select, what campaign angle to pursue, what risks to consider, how the competitive landscape should influence the strategy and what performance we could reasonably expect before committing budget.

That realization went well beyond influencer marketing. Across marketing, we have more data, signals and AI-generated answers than ever, yet marketers still have to decide what to do next.

That’s the evolution behind Lickly. We’re an audience-first, AI-driven Decision Intelligence platform for marketing. The goal is to move from more intelligence to better decisions backed by evidence — marketing decisions you can defend.

Lickly now describes itself as an AI-driven Decision Intelligence platform for marketing. What does Decision Intelligence mean in practice, and how is it different from simply giving marketers more data or another AI-generated answer?

Decision Intelligence for marketing is the use of AI, evidence and structured reasoning to turn audience and market signals into recommendations marketers can evaluate, act on and defend.

AI has made it incredibly easy to generate recommendations. But when marketing dollars are involved, the reasoning and evidence behind those recommendations become just as important.

Every marketing decision is an investment. If I’m choosing an audience, approving a campaign, selecting a creator or allocating budget, I want to understand what the recommendation is, why it’s being recommended and what evidence supports it.

Decision Intelligence should help the marketer make the call while keeping human judgment at the center of the process. Marketers still bring context, experience and judgment. Technology should make that judgment better informed and more defensible.

As generative AI becomes embedded across marketing, marketers are increasingly being asked to trust AI-generated recommendations. How have you approached the problem of reasoning, evidence and human judgment differently?

I don’t think marketers should be asked to trust AI simply because it can produce a convincing answer.

Our approach is to show the receipts. Lickly’s proprietary M³VR™ methodology questions, verifies, reasons and validates across multiple models, signals and reasoning paths before delivering an evidence-backed recommendation.

The marketer sees both the recommendation and the evidence behind it. That means you can examine the reasoning, challenge assumptions and decide whether the evidence is strong enough to act on.

AI can handle more of the analysis and give marketers stronger evidence for the decisions they’re already accountable for. Human judgment remains essential, especially when real money and brand reputation are involved.

Lickly is using its own platform to help inform the marketing decisions behind the company’s launch. What has “Building Lickly with Lickly” taught you about how marketers actually want to use Decision Intelligence?

This has probably been the most valuable part of the experience for me because marketing Lickly also means using Decision Intelligence in my own work.

We’ve been building Lickly with Lickly — using our own platform to help inform audience, positioning, content and campaign decisions around the brand and launch. That puts me in exactly the same seat as the marketers we’re building for.

What I’ve learned is that technology is most useful when it helps me get to a stronger decision faster. Having a recommendation is valuable, but I also need to understand why it was made, examine the evidence behind it and combine that with what I know about the business.

That experience has reinforced my belief that the best use of AI in marketing is to strengthen human judgment.

Looking ahead, what changes when marketers can move from simply understanding what happened to having evidence-backed recommendations about what to do next?

I think accountability becomes a much bigger part of how we evaluate marketing technology.

For years, the industry has gotten very good at explaining what happened after the money was spent. The focus now should be on helping marketers make stronger decisions before and during that investment.

Marketing will never be perfectly predictable, and Decision Intelligence shouldn’t pretend that it will be. A defensible decision means you made the best call you could with the evidence available, understood the reasoning behind it and knew why you were putting money behind it, even if the outcome isn’t guaranteed.

As AI becomes more embedded in marketing, I think that standard becomes even more important. AI-generated answers are going to be everywhere. Everyone will have those. Marketers still have to know which recommendations make sense for their business and have the evidence to defend the decisions they make.

More great interviews

• Glen Ceniza, CTO at Sapiens: “We hold our agents to a higher standard than we hold people”
• Jeff Chancellor, CMO at Oversight: “In finance, the real advantage comes from the context around the model”
• Peter Marelas, Senior Director of Product Management at New Relic: “The organisations that get ahead will be the ones that did the boring work early”