Overview

The brand had plenty of customer data (Shopify orders, subscriptions, email, support) but no clear way to segment or act on it. We mapped their stack, defined segments around RFM, layered in subscription status and engagement signals, and backfilled history so movement between segments could be measured. Segments were synced to marketing tools for targeted flows (win‑back, cross‑sell, VIP). This turned a generic retention program into a focused one, with clear before/after metrics.

The challenge

This Shopify cosmetics brand couldn’t get a clean view of customers, let alone act on it. They had subscribers and one‑time buyers, lots of email and support touchpoints, but no way to segment reliably or measure the impact of treating segments differently.

What we built

• RFM as the backbone

• Recency, Frequency, Monetary scoring tailored to their buying patterns and promo calendar.

• Real-world enrichments

• Subscription status and tenure merged with one‑time orders.

• Email engagement and customer service signals (complaints, refunds, NPS tags) folded into the score.

• Durable history and tracking

• Backfilled segment history to see movement over time (who’s improving, who’s decaying).

• Clear definitions documented in a metrics layer so marketing, CX, and finance use the same segments.

• Activation-ready

• Segments synced to marketing tools for targeted flows and offers (win‑back, cross‑sell, upsell, VIP care).

• Why This Works

• Marketing gets clear handles (e.g., “High‑value but cooling,” “At‑risk subscribers,” “Promising first‑timers”).

• You can measure lift by segment, not just channel—so budget goes where it returns.

• It plugs into Shopify and subscription tools without a rebuild.

What changed

• The team runs targeted campaigns per segment instead of blanket blasts.

• Movement between segments is visible, so you see if campaigns actually upgrade customers.

• CX prioritizes high‑value at‑risk customers with faster responses or better offers.

Results

• Subscriber LTV up 9% after segment‑driven campaigns on the existing base.

• Win‑back rates improved on “cooling” cohorts; fewer churned subscribers month‑over‑month.

• Better ROAS on retention spend as offers focused on segments with highest expected lift.

• Three result-focused KPIs

• LTV uplift on subscribers: +9% post‑segmentation rollout

• Time to second purchase: reduced for “cooling” customers after targeted flows

• Retention ROAS: higher on segment‑targeted campaigns vs prior blanket sends