Your Best Customers Deserve Better Than Generic Emails
Sending the same message to your champions and your about-to-churn customers is costing you money. Customer segmentation tells you exactly who each customer is, what they're worth, and how to treat them differently.
The Customer Segments You Need to Know
RFM-based segmentation divides your customer base into actionable groups, each with a different strategy.
Your Best Customers
Bought recently, buy often, spend the most. These are your brand advocates and referral drivers.
→ Reward them, ask for reviews, offer early access
Consistent High-Value
Buy regularly but not as recently or with as high a ticket as champions. Solid retention base.
→ Upsell, cross-sell, loyalty programs
Slipping Away
Were good customers but haven't bought recently. Still reachable, but the window is closing.
→ Win-back campaigns, personalized offers
New or Inconsistent
Made one or two purchases but haven't developed a habit. Convert them or they'll drift.
→ Onboarding sequences, second-purchase offers
Haven't Bought in Months
High recency gap. Reactivation is expensive, worth attempting only for high historical spenders.
→ Reactivation campaigns for top spenders only
Segmentation Methods I Use
Matched to your data and business model, not a one-size-fits-all approach.
RFM Analysis (Recency, Frequency, Monetary)
The gold standard for transaction-based segmentation. Scores every customer on three dimensions to create actionable segments that map directly to marketing and retention strategies.
Customer Lifetime Value Segmentation
Predict each customer's total future value, not just their historical spend, and segment based on projected LTV. Focuses retention investment where return is highest.
Cohort Analysis
Group customers by acquisition month and track their behavior over time. Reveals whether newer customers behave better or worse than older ones, and identifies inflection points.
Behavioral Clustering (K-Means / DBSCAN)
For businesses with rich behavioral data, page views, product interactions, browsing patterns, ML clustering identifies segments you wouldn't find with rules-based approaches.
Related Projects
Real customer analytics work with documented business impact.
Retention Analysis · Power BI
Retail Revenue Decline Diagnosis
Cohort retention analysis identifying 85% customer retention failure behind 90% revenue collapse.
Demographic Analysis · Python
Returns by Age Group & Gender
Segmenting 6,789 returns by demographics and product category to identify intervention priorities.
Survey Analytics · Power BI
Banking Customer Satisfaction
Segmenting survey responses by demographics to reveal different churn drivers by customer group.
Know Your Customers
Which 20% of Your Customers Drive 80% of Your Revenue?
Most businesses can't answer that question. I'll answer it, and tell you exactly how to treat each segment differently to maximize retention and lifetime value. First call is free.
Book Your Free ConsultationRemote worldwide · adeyemi@adediranadeyemi.com