Lane Boots

Chesley Roberts

Omnichannel Growth Catalyst

Chesley Roberts is VP of Marketing & Analytics at Lane Boots, where she leads data-driven retail strategy, predictive modeling and AI-informed customer insights across DTC and wholesale channels.

Lessons From Using Data to Anticipate Customer Behavior

The biggest lesson is that data is only useful when it's attached to a decision. We've built a lot of reporting infrastructure at Lane Boots over the past year, paid ads dashboards, wholesale rep performance by account, DTC vs. B2B YoY tracking, returns analysis through Loop, email engagement by week, and the work that moved the needle wasn't the dashboards themselves. It was the moment someone looked at a number and changed something because of it.

Additionally, the question you're not asking is usually the important one. The most useful thing data has done for us is surface questions we weren't asking, not confirm the ones we were. Nobody came to me asking about sizing consistency by colorway. That came out of going two levels deep into the return reasons by style.

How AI Is Reshaping Marketing Analytics

The most urgent shift we're navigating right now is the move from search-driven discovery to AI-driven discovery. We've seen a 20% dip in organic traffic year-over-year since February 2026, and we don't expect that to reverse. Shoppers are increasingly turning to AI tools for product recommendations before they ever land on a brand's website. That means the brands that win are the ones optimizing for how AI surfaces and recommends them, not just how Google indexes them. For us, that looks like auditing our LLMs.txt file, keeping our knowledge base current, and writing product descriptions, FAQs and content with conversational queries in mind. If you're not tracking agentic traffic and testing to improve AI recommendations, you're already operating with incomplete visibility into where your customers are actually making decisions.

The second shift is internal. AI has expanded what's possible for a lean marketing team. I'm using it to run reporting that would have taken days to build manually, to surface underperforming ad sets before they become costly, and to catch gaps in email flows that might have gone unnoticed for weeks. The goal isn't to replace judgment, it's to get better information faster so the decisions we're making are sharper.

“The marketers who thrive won’t be the ones who know the most about AI, they’ll be the ones who know how to combine it with sharp judgment and real business context.”

For retail leaders, I think the opportunity is significant, but it requires intention. The brands that will come out ahead are the ones investing in content infrastructure now, crafting messaging that speaks to how people actually ask questions, and using AI operationally to close gaps before they compound. Both are winnable. Neither happens by accident.

Evaluating Initiatives for Sustainable Online Revenue Growth

The first thing I look at is time to value, how long does it actually take to implement, and how quickly can the team start using it effectively? An initiative that takes six months to stand up before it generates a single dollar isn't the same as one that's contributing in week two. Speed of impact matters.

From there, everything comes back to ROI. That has to be provable, not projected, not estimated by a vendor, but measurable against your own numbers. If you can't draw a clear line between the initiative and revenue, it's hard to justify sustaining it.

I'm also deeply skeptical of two pricing structures that I think work against brands in the long run: tools that scale their fees with your revenue, and contracts that lock you in for years before you've validated performance. Both shift the risk entirely onto the brand.

Balancing Data-Driven Decisions With Customer Expectations

There are a ton so I will settle on my top three.

1. Trust. Consumers want hyper-personalized experiences, and in the same breath, they're increasingly wary of how their data is being used. That creates a real tension for retailers: the personalization that drives conversion depends on first-party data, but collecting that data requires a level of trust that hasn't fully been earned yet.

2. Human touch. This one is a balancing act that I don't think the industry has fully figured out yet. Over-automating customer interactions risks making people feel like they're talking to a machine when they actually need help, and that erodes trust fast. But under-investing in AI-driven engagement leaves you exposed. If you're not showing up with timely, relevant messaging, someone else will fill that space with something louder, even if it's cheaper or gimmickier. The challenge is using AI to enhance the experience without letting it flatten it.

3. Data silos. This is the one I think retailers underestimate the most. AI has made it easier than ever for every department to pull their own reports, which sounds like a good thing until you're in a room where two teams are making conflicting decisions based on numbers that don't match. When marketing is looking at one revenue figure and operations is looking at another, and neither team knows why they differ, you get siloed decision-making built on shaky foundations. The real risk isn't bad data, it's confidently wrong data. Getting everyone aligned on a single source of truth, and making sure people are tracking the right metrics for the decisions they're actually making, is still one of the hardest operational problems in retail.

The Future of Predictive Analytics in Retail

With AI, the gap between brands who are ready and brands who aren't is widening fast. Predictive analytics will increasingly move from reporting what happened to anticipating what's next, flagging a customer who's about to churn, identifying a product that's about to peak, or catching a campaign that's starting to underperform before it does real damage. The retailers who build infrastructure around that kind of proactive intelligence are going to run circles around the ones still pulling monthly reports.

My advice to marketing professionals is simple: stop waiting for your company to hand you an AI strategy and start building your own fluency now. Learn the tools. Ask better questions of your data. Understand where your information is coming from and whether it's actually reliable. The marketers who thrive in this environment won't be the ones who know the most about AI, they'll be the ones who know how to combine it with sharp judgment and real business context. That combination is still very human, and it's not going away.