RFM segmentation groups your customers by three things your order data already knows: Recency (when they last bought), Frequency (how often they buy), and Monetary value (how much they’ve spent). Score each customer on all three, and a flat customer list turns into a handful of groups that deserve visibly different treatment — champions who’ve […]
Category Archives: Customer Segmentation
A dynamic segment is a rule, not a roster: “customers with 2+ orders whose last purchase is 90+ days old” recalculates itself continuously, so contacts flow in when they match and out when they stop matching. A static list is a snapshot that was accurate the day you built it and has been decaying since. […]
A segmentation audit answers four questions about your existing setup: coverage (what share of your contacts sits in no meaningful segment at all), overlap (whether the same customer is getting hit by multiple segments in the same week), staleness (which segments no longer match how your store actually works), and revenue (whether segmented sends genuinely […]
Split your customers on one condition — total orders equals 1 versus total orders greater than 1 — and make both segments dynamic, so a first-time buyer moves to the repeat segment the moment order two lands. That single split changes what each group receives: first-time buyers get onboarding and a deliberate push toward the […]
Segmenting customers by discount sensitivity means splitting your base into three groups based on order history: full-price buyers (few or none of their orders used a discount code), mixed buyers, and deal-driven buyers (most or all orders came with a code). Then you route your promotions accordingly — full-price buyers stop receiving sale blasts they […]
Segmenting customers by cart value means splitting your automations — abandoned cart flows above all — into value bands, so a €30 cart and a €300 cart stop receiving the identical recovery email. Set three bands anchored to your own numbers: low (well under your average order value), mid (around it, including carts just below […]
You can segment subscribers who’ve never bought using three things you already collect: where they signed up, what they’ve browsed since, and whether they open what you send. That’s enough to build three working segments — discount-motivated signups, category-interested browsers, and quiet subscribers who’ve gone dark — and to send each one a different path […]
To segment customers by time since last purchase, group them into recency bands — active, cooling, at risk, and inactive — with thresholds based on your store’s own repurchase cycle, not on someone else’s 30/60/90-day template. Pull your order data, find the typical gap between a customer’s orders, and set each band as a multiple […]
Segment by purchase frequency using two settings together: order count and a rolling time window. A solid default is four buckets — one order, two orders, three to four orders, five or more — counted over the last 12 months, not over all time. The window is the part stores get wrong: “number of orders” […]
Segment by country using the shipping address from actual orders, and by language using an explicit choice the customer made — a language picker on your signup form or the store language they browsed in. Don’t guess from IP address if better data exists. Build one segment per country (or country group) that materially differs […]