How To Use Analytics To Refine Ecommerce Customer Segments

If you’re segmenting based on engagement, you’re already behind. Everyone does 30/60/90 day engagement windows. It’s not advanced. It’s basic hygiene. Here’s the real segmentation play most marketers miss: Segment by intent signals, not just opens/clicks. Examples: • Viewed shipping/returns policy? ➝ Hit with reassurance focused CTA • Time on product page > 30 seconds? ➝ Trigger a cart based reminder • Opened 5+ product emails but never clicked? ➝ Try plain text emails with a customer story • AOV based segments - low priced vs high priced ➝ show them the right products • FAQ viewers ➝ Give them more trust • Recent abandon carts/checkouts ➝ Leverage their interests • Time since they opted in for a coupon ➝ Remind them about it • Time since last purchase ➝ Show them complimentary products The list goes on and on... THEN add your engagement for best deliverability Engagement ≠ intent. Intent = actual buying behavior. Stop treating every click the same. Treat the reason behind the click differently.

Why was this brand paying 42% more for customers they already had in reach? When I audited their account, the founder assumed pricing was the issue. CAC was climbing, margins were thin and they were ready to test discounts. But the numbers told a different story. The problem wasn’t price, it was structure. Here’s what we fixed: 1. Audience re-segmentation Instead of running broad cold, warm and customer buckets, we broke them into intent-based layers. Warm traffic was divided into “cart abandoners,” “repeat site visitors,” and “social engagers.” Each group got ads tailored to its stage of readiness instead of generic messaging. 2. Funnel sequencing Previously, retargeting was hitting cold leads too early, wasting spend on people who weren’t ready. We re-mapped the sequence: cold campaigns to spark awareness, mid-funnel ads to build education and trust and retargeting focused solely on proof and urgency for high-intent visitors. 3. Creative alignment All their ads looked the same, polished product features. We rebuilt the creative to fit funnel stages: problem/solution ads for cold traffic, story-driven testimonials for mid-funnel and offer reinforcement for retargeting. This way, buyers saw a journey, not repetition. The impact? → CAC dropped 42% in 60 days. → Average revenue per customer stayed intact. → Profitability grew without touching price or product. The real win wasn’t “better ads.” It was creating a system where every stage of the funnel worked together. ↪ Running e-commerce ads but still seeing CAC creep higher (even when top-line ROAS looks fine)? ↪ I’m offering a quick 15-minute funnel audit (link in comments) to uncover the 1–2 misalignments inflating your CAC and show you how to fix them.

My Favorite Analyses: the Recency-Frequency matrix. This simple yet powerful framework goes beyond traditional segmentation to provide actionable insights into customer behavior. By focusing on how recently and how often customers engage with your brand, you can tailor your strategies to maximize lifetime value. Why it works: - Recency: Customers who have purchased recently are more likely to purchase again. It's a strong indicator of engagement and future behavior. - Frequency: Customers who purchase more often demonstrate loyalty and satisfaction, leading to a higher customer value. Recency and Frequency are the most important indicators of customer value, exhibiting more correlation to CLV than Monetary Value which is the third component in traditional RFM analyses. The Recency-Frequency matrix helps you categorize your customers into segments based on behaviors instead of factors like demographics or psychographics that imply actions. The analysis reveals distinct customer segments that require unique marketing strategies, including your Champions, the customers who Need Attention, and those who have Already Churned. Implementing the Matrix: Depending on the size of your customer dataset, the Recency-Frequency matrix can be built in a spreadsheet or a more hefty tool like SQL or R. - Excel/Google Sheets: Use `MAXIFS`, `COUNT`, `PERCENTRANK`, and a pivot table to build the Recency-Frequency matrix, but watch out for row limits. - SQL: Leverage functions like `DATEDIFF` and `COUNT` to calculate metrics, and segment with `NTILE`. - R: The `RFM` package handles large datasets with ease, offering advanced segmentation and visualization. This approach isn’t just theory — it’s a data-backed method for ensuring your marketing dollars are spent where they’ll make the most impact. DM me if you'd like to learn more, including the marketing strategies that I most commonly recommend for each Recency-Frequency matrix customer segment. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling #MyFavoriteAnalyses #ROI #MROI