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There\u2019s a wealth of customer knowledge sitting right in front of retailers, but most just don\u2019t see it.<\/p>\n
Brands are investing millions to build out their data infrastructure. Tracking every click, abandoned cart, cross-channel journey and more. But how\u2019s that data actually being used? Typically, just to improve workflows. This looks like automating email sends. Streamlining inventory. Speeding up campaign launches. It\u2019s all about operational efficiency.<\/p>\n
Why? Because that\u2019s the default for most businesses. It\u2019s much easier to optimize what you\u2019re already doing than to rethink how you\u2019re doing it. So brands end up with faster processes that still produce generic experiences. This annoys customers, just more efficiently.<\/p>\n
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.<\/p>\n
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<\/div>\n<\/div>\nShifting from operational efficiency to consumer understanding is how you turn data into actionable insight. The kind that improves personalization and drives down acquisition costs.<\/p>\n
Here\u2019s what that looks like.<\/p>\n
The Productivity Trap<\/strong><\/h3>\nI\u2019m not a gambler, but there\u2019s a bet I feel comfortable making: If I walked into any retail marketing meeting, I\u2019m sure I\u2019d hear variations of the same wins being celebrated:\u00a0<\/p>\n
<\/p>\n- \u201cWe automated our segmentation process.\u201d\u00a0<\/li>\n
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\n
\n
<\/p>\n
- \u201cWe reduced campaign launch time by XX%.\u201d\u00a0<\/li>\n
\n
\n
\n
<\/p>\n
- \u201cOur reporting dashboards are now real-time.\u201d<\/li>\n
\n<\/ul>\n
These are all good things. They\u2019re making progress. But none of them are customer-facing.<\/p>\n
Operational optimization without genuine insight creates generic experiences for everyone. Your team could send emails faster, but if those emails don\u2019t resonate, you\u2019re just annoying people at scale. You\u2019ve built a well-oiled machine that produces mediocrity more efficiently than before.<\/p>\n
You can\u2019t 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.<\/p>\n
What Your Data is Actually Telling You<\/strong><\/h3>\nWhen you look at behavioral patterns, that\u2019s when the story gets interesting. Your data doesn\u2019t just track what happened. It reveals why your customers are doing what they\u2019re doing, what they actually care about and what they\u2019re most likely to do next.<\/p>\n
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\u2019s not a random experience; that\u2019s a signal. It tells you they\u2019re squeezing shopping into stolen moments, which means you\u2019d better have mobile-friendly product pages \u2014 and a frictionless checkout experience.<\/p>\n
Or look at cross-channel behavior. Customer browses your site, adds items to their cart, then bounces. Two days later, they\u2019re clicking through your email but not buying anything. Three more days later, they finally convert \u2014 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.<\/p>\n
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\u2019t disinterested; they\u2019re considering. Someone who returns to view the same item three or four times across different sessions isn\u2019t a browser. They\u2019re a savvy shopper comparing for the best deal, or they\u2019re waiting for a reason to commit.<\/p>\n
These emotional and motivational signals are right in front of you.<\/p>\n
From Insight to Action<\/strong><\/h3>\nVitamin and supplement brand OLLY gets this. \u201cWe want every text or email we send to feel like a personal message from us,\u201d says Becca Tran, OLLY\u2019s CRM and retention specialist.<\/p>\n
And that\u2019s not just a nice sentiment, it\u2019s data strategy. When you understand what individual customers care about, you can make every touch point relevant to them.<\/p>\n
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\u2019s email revenue for the company.<\/p>\n
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\u2019re not wasting budget on messages that won\u2019t land. You\u2019re getting less operational overhead. No more creating dozens of email variants when behavioral data tells you exactly which three will perform.<\/p>\n
Seeing What\u2019s Coming Next<\/strong><\/h3>\nIf you leverage your data the right way, you\u2019ll gain the power to see into the future. Because your data can anticipate what customers will want before they know they want it.<\/p>\n
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\u2019re someone on a predictable cycle that you can proactively serve.\u00a0<\/p>\n
A spike in searches for cozy loungewear isn\u2019t just noise. It\u2019s an early signal of a trend you can capitalize on before competitors notice.<\/p>\n
With data like this, you won\u2019t 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.<\/p>\n
But getting there requires shifting how you think about data. You have to stop looking at your data to answer the question, \u201cWhat happened?\u201d Instead, you should be asking, \u201cWhat does this tell me about what\u2019s going to happen?\u201d<\/p>\n
What\u2019s Next for Brands<\/strong><\/h3>\nReading 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.<\/p>\n
Today, you\u2019re at a fork in the road: you could either stay focused on efficiencies and keep things surface-level \u2014 vague and generic \u2014 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.<\/p>\n
Option B, right? I\u2019m assuming you said yes. Great!\u00a0<\/p>\n
So, what\u2019s next \u2014 for brands? You have to prioritize deep understanding over efficiency. Make sure you\u2019re framing your thinking through the lens of your customers\u2019 wants, needs, actions and predictability.\u00a0<\/p>\n
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.<\/p>\n
Remember that data isn\u2019t the end. Customer relationships are. Data shows you the way there. You just need to get \u2014 and stay \u2014 on that path.<\/p>\n
\nKeri McGhee is Chief Marketing Officer at <\/em>Attentive<\/em>, where she leads strategies that help brands transform customer data into meaningful, revenue-driving experiences.<\/em><\/p>\n<\/p><\/div>\n
\n
\n
\n
<\/p>\n
\n
\n
\n
<\/p>\n
\n<\/ul>\n
These are all good things. They\u2019re making progress. But none of them are customer-facing.<\/p>\n
Operational optimization without genuine insight creates generic experiences for everyone. Your team could send emails faster, but if those emails don\u2019t resonate, you\u2019re just annoying people at scale. You\u2019ve built a well-oiled machine that produces mediocrity more efficiently than before.<\/p>\n
You can\u2019t 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.<\/p>\n
What Your Data is Actually Telling You<\/strong><\/h3>\nWhen you look at behavioral patterns, that\u2019s when the story gets interesting. Your data doesn\u2019t just track what happened. It reveals why your customers are doing what they\u2019re doing, what they actually care about and what they\u2019re most likely to do next.<\/p>\n
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\u2019s not a random experience; that\u2019s a signal. It tells you they\u2019re squeezing shopping into stolen moments, which means you\u2019d better have mobile-friendly product pages \u2014 and a frictionless checkout experience.<\/p>\n
Or look at cross-channel behavior. Customer browses your site, adds items to their cart, then bounces. Two days later, they\u2019re clicking through your email but not buying anything. Three more days later, they finally convert \u2014 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.<\/p>\n
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\u2019t disinterested; they\u2019re considering. Someone who returns to view the same item three or four times across different sessions isn\u2019t a browser. They\u2019re a savvy shopper comparing for the best deal, or they\u2019re waiting for a reason to commit.<\/p>\n
These emotional and motivational signals are right in front of you.<\/p>\n
From Insight to Action<\/strong><\/h3>\nVitamin and supplement brand OLLY gets this. \u201cWe want every text or email we send to feel like a personal message from us,\u201d says Becca Tran, OLLY\u2019s CRM and retention specialist.<\/p>\n
And that\u2019s not just a nice sentiment, it\u2019s data strategy. When you understand what individual customers care about, you can make every touch point relevant to them.<\/p>\n
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\u2019s email revenue for the company.<\/p>\n
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\u2019re not wasting budget on messages that won\u2019t land. You\u2019re getting less operational overhead. No more creating dozens of email variants when behavioral data tells you exactly which three will perform.<\/p>\n
Seeing What\u2019s Coming Next<\/strong><\/h3>\nIf you leverage your data the right way, you\u2019ll gain the power to see into the future. Because your data can anticipate what customers will want before they know they want it.<\/p>\n
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\u2019re someone on a predictable cycle that you can proactively serve.\u00a0<\/p>\n
A spike in searches for cozy loungewear isn\u2019t just noise. It\u2019s an early signal of a trend you can capitalize on before competitors notice.<\/p>\n
With data like this, you won\u2019t 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.<\/p>\n
But getting there requires shifting how you think about data. You have to stop looking at your data to answer the question, \u201cWhat happened?\u201d Instead, you should be asking, \u201cWhat does this tell me about what\u2019s going to happen?\u201d<\/p>\n
What\u2019s Next for Brands<\/strong><\/h3>\nReading 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.<\/p>\n
Today, you\u2019re at a fork in the road: you could either stay focused on efficiencies and keep things surface-level \u2014 vague and generic \u2014 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.<\/p>\n
Option B, right? I\u2019m assuming you said yes. Great!\u00a0<\/p>\n
So, what\u2019s next \u2014 for brands? You have to prioritize deep understanding over efficiency. Make sure you\u2019re framing your thinking through the lens of your customers\u2019 wants, needs, actions and predictability.\u00a0<\/p>\n
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.<\/p>\n
Remember that data isn\u2019t the end. Customer relationships are. Data shows you the way there. You just need to get \u2014 and stay \u2014 on that path.<\/p>\n
\nKeri McGhee is Chief Marketing Officer at <\/em>Attentive<\/em>, where she leads strategies that help brands transform customer data into meaningful, revenue-driving experiences.<\/em><\/p>\n<\/p><\/div>\n
Vitamin and supplement brand OLLY gets this. \u201cWe want every text or email we send to feel like a personal message from us,\u201d says Becca Tran, OLLY\u2019s CRM and retention specialist.<\/p>\n
And that\u2019s not just a nice sentiment, it\u2019s data strategy. When you understand what individual customers care about, you can make every touch point relevant to them.<\/p>\n
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\u2019s email revenue for the company.<\/p>\n
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\u2019re not wasting budget on messages that won\u2019t land. You\u2019re getting less operational overhead. No more creating dozens of email variants when behavioral data tells you exactly which three will perform.<\/p>\n
Seeing What\u2019s Coming Next<\/strong><\/h3>\nIf you leverage your data the right way, you\u2019ll gain the power to see into the future. Because your data can anticipate what customers will want before they know they want it.<\/p>\n
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\u2019re someone on a predictable cycle that you can proactively serve.\u00a0<\/p>\n
A spike in searches for cozy loungewear isn\u2019t just noise. It\u2019s an early signal of a trend you can capitalize on before competitors notice.<\/p>\n
With data like this, you won\u2019t 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.<\/p>\n
But getting there requires shifting how you think about data. You have to stop looking at your data to answer the question, \u201cWhat happened?\u201d Instead, you should be asking, \u201cWhat does this tell me about what\u2019s going to happen?\u201d<\/p>\n
What\u2019s Next for Brands<\/strong><\/h3>\nReading 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.<\/p>\n
Today, you\u2019re at a fork in the road: you could either stay focused on efficiencies and keep things surface-level \u2014 vague and generic \u2014 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.<\/p>\n
Option B, right? I\u2019m assuming you said yes. Great!\u00a0<\/p>\n
So, what\u2019s next \u2014 for brands? You have to prioritize deep understanding over efficiency. Make sure you\u2019re framing your thinking through the lens of your customers\u2019 wants, needs, actions and predictability.\u00a0<\/p>\n
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.<\/p>\n
Remember that data isn\u2019t the end. Customer relationships are. Data shows you the way there. You just need to get \u2014 and stay \u2014 on that path.<\/p>\n
\nKeri McGhee is Chief Marketing Officer at <\/em>Attentive<\/em>, where she leads strategies that help brands transform customer data into meaningful, revenue-driving experiences.<\/em><\/p>\n<\/p><\/div>\n
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.<\/p>\n
Today, you\u2019re at a fork in the road: you could either stay focused on efficiencies and keep things surface-level \u2014 vague and generic \u2014 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.<\/p>\n
Option B, right? I\u2019m assuming you said yes. Great!\u00a0<\/p>\n
So, what\u2019s next \u2014 for brands? You have to prioritize deep understanding over efficiency. Make sure you\u2019re framing your thinking through the lens of your customers\u2019 wants, needs, actions and predictability.\u00a0<\/p>\n
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.<\/p>\n
Remember that data isn\u2019t the end. Customer relationships are. Data shows you the way there. You just need to get \u2014 and stay \u2014 on that path.<\/p>\n
\n
Keri McGhee is Chief Marketing Officer at <\/em>Attentive<\/em>, where she leads strategies that help brands transform customer data into meaningful, revenue-driving experiences.<\/em><\/p>\n<\/p><\/div>\n