There’s a wealth of customer knowledge sitting right in front of retailers, but most just don’t see it.
Brands are investing millions to build out their data infrastructure. Tracking every click, abandoned cart, cross-channel journey and more. But how’s that data actually being used? Typically, just to improve workflows. This looks like automating email sends. Streamlining inventory. Speeding up campaign launches. It’s all about operational efficiency.
Why? Because that’s the default for most businesses. It’s much easier to optimize what you’re already doing than to rethink how you’re doing it. So brands end up with faster processes that still produce generic experiences. This annoys customers, just more efficiently.
The better play, though, is using data to understand consumer behavior. Reading the signals in those clicks and abandoned carts. Seeing patterns in how customers move across channels. Doing all of this to turn those insights into 1:1 experiences that actually resonate.
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Shifting from operational efficiency to consumer understanding is how you turn data into actionable insight. The kind that improves personalization and drives down acquisition costs.
Here’s what that looks like.
The Productivity Trap
I’m not a gambler, but there’s a bet I feel comfortable making: If I walked into any retail marketing meeting, I’m sure I’d hear variations of the same wins being celebrated:
- “We automated our segmentation process.”
- “We reduced campaign launch time by XX%.”
- “Our reporting dashboards are now real-time.”
These are all good things. They’re making progress. But none of them are customer-facing.
Operational optimization without genuine insight creates generic experiences for everyone. Your team could send emails faster, but if those emails don’t resonate, you’re just annoying people at scale. You’ve built a well-oiled machine that produces mediocrity more efficiently than before.
You can’t stand out just by increasing the speed by which you process your customer data. It comes down to what you do with what you discover.
What Your Data is Actually Telling You
When you look at behavioral patterns, that’s when the story gets interesting. Your data doesn’t just track what happened. It reveals why your customers are doing what they’re doing, what they actually care about and what they’re most likely to do next.
Take timing. Most brands know when a purchase happens, but few dig into the context. Are certain segments shopping exclusively on mobile during their lunch break? That’s not a random experience; that’s a signal. It tells you they’re squeezing shopping into stolen moments, which means you’d better have mobile-friendly product pages — and a frictionless checkout experience.
Or look at cross-channel behavior. Customer browses your site, adds items to their cart, then bounces. Two days later, they’re clicking through your email but not buying anything. Three more days later, they finally convert — via SMS. Most brands would track it across several separate data points. But the brands that pay attention see each action (or non-action) as chapters in the same story, and use that pattern to inform their next move.
Intent signals are everywhere. You just need to know where to look. A customer who hovers on a product page for nearly a minute but leaves without adding to cart isn’t disinterested; they’re considering. Someone who returns to view the same item three or four times across different sessions isn’t a browser. They’re a savvy shopper comparing for the best deal, or they’re waiting for a reason to commit.
These emotional and motivational signals are right in front of you.
From Insight to Action
Vitamin and supplement brand OLLY gets this. “We want every text or email we send to feel like a personal message from us,” says Becca Tran, OLLY’s CRM and retention specialist.
And that’s not just a nice sentiment, it’s data strategy. When you understand what individual customers care about, you can make every touch point relevant to them.
OLLY found the story their data told about each of their customers. The brand reacted recently by creating an email series that sends subscribers personalized recommendations at a regular cadence based on their specific needs and behaviors. These automated emails took minimal effort to create and send, and generated over 40% of a typical month’s email revenue for the company.
This is where data transforms from a productivity tool into a revenue driver. When you have better insights, you have more efficient campaign spend. You’re not wasting budget on messages that won’t land. You’re getting less operational overhead. No more creating dozens of email variants when behavioral data tells you exactly which three will perform.
Seeing What’s Coming Next
If you leverage your data the right way, you’ll gain the power to see into the future. Because your data can anticipate what customers will want before they know they want it.
How do we know this? Behavioral patterns compound. Someone who buys a face cleanser in January and does so again in March is more than a repeat customer. They’re someone on a predictable cycle that you can proactively serve.
A spike in searches for cozy loungewear isn’t just noise. It’s an early signal of a trend you can capitalize on before competitors notice.
With data like this, you won’t have to mass-send generic and ill-timed emails, text messages or push notifications anymore. This leads to better experiences on both sides of the relationship: consumers stop being bombarded with irrelevant offers, and brands increase lifetime value and foster deeper loyalty.
But getting there requires shifting how you think about data. You have to stop looking at your data to answer the question, “What happened?” Instead, you should be asking, “What does this tell me about what’s going to happen?”
What’s Next for Brands
Reading your data tea leaves means understanding data as a tool for connection and relevance instead of just productivity. It means moving from operational optimization to genuine customer understanding and engagement.
Today, you’re at a fork in the road: you could either stay focused on efficiencies and keep things surface-level — vague and generic — or you could turn all that wonderful data into measurable advantage. The latter route offers the best of both worlds: your customers get relevant offers, faster service, fewer annoying messages and more trustworthy interactions. You get faster revenue, lower waste and clearer decisions.
Option B, right? I’m assuming you said yes. Great!
So, what’s next — for brands? You have to prioritize deep understanding over efficiency. Make sure you’re framing your thinking through the lens of your customers’ wants, needs, actions and predictability.
And to do that, you must invest in the infrastructure that connects customer behavior across channels. You also need to build teams that can translate those patterns into action. Experiment, experiment, experiment, and see whether your insights are driving incremental value. Be curious. Ask stupid questions. Answer them quickly.
Remember that data isn’t the end. Customer relationships are. Data shows you the way there. You just need to get — and stay — on that path.
Keri McGhee is Chief Marketing Officer at Attentive, where she leads strategies that help brands transform customer data into meaningful, revenue-driving experiences.
