How a global convenience chain with 66,000+ stores used personalized recommendations to increase basket size by 27%.
66,000+
Stores
17
Countries
+27%
Basket Size
$60M
Value Generated
With over 66,000 stores across 17 countries, this convenience retail giant had lost 16% of daily customers per store over the past decade — a $3.1B revenue gap. Their mobile ordering app served millions of customers, but 25-60% of orders were repetitive purchases. Without personalization, customers kept buying the same items, missing opportunities for discovery and increased basket size.
We built a personalized recommendation engine that analyzed purchasing patterns across the mobile ordering app and the loyalty program. The system segmented users into cold, warm, and hot tiers: new users received popularity-based suggestions, returning users got recommendations based on similar customer profiles, and frequent buyers received hyper-personalized picks combining their purchase history with trending products in their area. Complementary product pairing ensured that each recommendation increased basket diversity.
Measurable impact across key metrics
Avg. Basket Size
Before
Baseline
After
+27%
Value Generated
Before
—
After
$60M
Customer Retention
Before
Baseline
After
+16%
“The recommendation engine generated $60M in value by helping customers discover products they didn't know they wanted.”
Let's discuss how RecoMind can help your business achieve similar results.
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