Context
Cave Romane is a French online wine merchant specializing in Burgundy and Bordeaux wines as well as premium champagnes and spirits. The catalog has about 1,600 SKUs with highly variable average baskets: 65 euros for a casual buyer up to 2,800 euros for an enthusiast building their cellar. The WooCommerce store generates 480,000 euros in monthly revenue with 6,800 active customers.
Premium wine is marked by strong customer heterogeneity: most buy a few bottles per year for special occasions, while a minority (real enthusiasts) build cellars and spend several thousand euros per year.
The challenge
The owner intuitively knows a small portion of customers generates most revenue, without quantifying it precisely. Marketing campaigns (newsletters, Meta Ads) are identical for all customers, from small occasional buyers to big collectors.
This uniformity creates two problems. First, big customers receive the same generic promotions as everyone else when they deserve premium treatment (early access to primeurs, personalized selections, concierge service). Second, marketing budget is wasted chasing low-value occasional customers while VIPs, who don't need aggressive commercial outreach, are over-solicited.
Without reliable RFM segmentation, it's impossible to build a value-differentiated strategy.
The Fullmetrix solution
Cave Romane connects WooCommerce to Fullmetrix and activates the RFM engine. Analysis reveals that 8% of customers (the Champions segment) generate 54% of revenue, with an average basket 6.3x the average and 5.2 orders per year.
The team activates a fully differentiated VIP strategy. The 540 Champions get priority access to Burgundy and Bordeaux primeurs 48 hours before public release. A monthly personalized selection signed by the owner is emailed to them. A phone concierge service opens for orders above 800 euros. Meta Ads acquisition campaigns exclude Champions and target them only for exclusive events (private online tastings).
In parallel, Hibernating and Lost customers are re-engaged with a win-back -15% on their favorite region, driven by historical preferences detected by Fullmetrix.