Ingested 2+ years of URB's sales data into a custom data lake, built a dashboard to surface actionable patterns, then used the insights to redesign loyalty and run segmented, tiered email campaigns that turned $8K–$20K win-back months into $105K and $126K.
URB had real signal sitting inside their POS — but no dashboard, no audience model, no way to turn the data into campaigns. Email was running on hunches: blanket sends, flat discounts, the same offer to every store. The result was win-back months stuck in the $8K–$20K range.
We ingested URB's full POS history into a data lake, wrote queries to surface specific outputs, and built a custom dashboard so we could pull actionable insights on demand — for email targeting, promo amount, loyalty tier design, and audience segmentation.
Once the data lake was live, the dashboard became the brief for everything downstream — every email targeting decision, every promo amount, every loyalty rule.
Three things jumped out the moment we could actually look at the data:
1. Border stores behave differently than inland stores. Same brand, same products, completely different purchase patterns and price sensitivity. Treating them the same in email was leaving money on the table.
2. High-value cohorts were under-rewarded. The customers driving the biggest LTV weren't being recognized — just blended into the general blast list.
3. Loyalty tiers were arbitrary. Thresholds and multipliers had been set by intuition, not by what spend patterns and projected return actually justified.
Once the data dashboard was live, every email campaign got rebuilt from the ground up — targeting from real audience segments, offers calibrated to actual purchase behavior, tiers based on real redemption data.
In March + April we noticed in the data: a big discrepancy between inland stores and stores near the border. Different purchase behavior, different AOVs, different lapse patterns. So we split them, set different minimum-spend thresholds matched to each location's actual behavior, and tiered the discount by days lapsed. The result was a step-change in revenue.
Averaged across the three "before" campaigns and the new March + April segmented send.
Splitting inland vs. border, matching minimum-spend thresholds to actual local AOV, and tiering the discount by days lapsed turned a flat-line campaign into the single best-performing month URB had ever run.
The data showed cohorts of consistently high-spending customers we weren't recognizing. We targeted them with appreciation points (500pts at Ohio · 250pts at Monroe), then tagged on a bigger reward (500pts at Ohio · 750pts at Monroe) if they came back in the month and made a $50+ purchase. Both campaigns landed $13K–$15K in net revenue.
Built the URB RAF program inside AlpineIQ — automated tracking, fraud guardrails, and unique referral links per wallet. Launched it with a coordinated 360 campaign: in-store assets, online creative, email blast, and a permanent slot in the welcome series so every new customer gets exposed.
500 points to each side — $5 off for the referrer, $5 off for the friend — triggered when the friend's first purchase clears $10. Automated end-to-end through AlpineIQ.
The 360 launch hit four channels at once: in-store signage and budtender talk tracks, on-site banners, a dedicated email blast, and a permanent block in the welcome series so it's the first thing every new URB wallet holder sees.
Book a free 30-minute call. We'll walk through what's in your POS and what we'd build to actually act on it — no pitch deck, just a real conversation.