Coffee-chain store sales, drive-thru throughput, product mix, and loyalty
Industry
Quick-Service Coffee Chain
Company size
17 cafes across five metros, a 70,000-member loyalty app, and a 138-item menu
Modeled scale
About $14.3M gross and $13.3M net sales across 1.7 million transactions
Packaged scale
About 3.23 million rows across 11 Standard-tier tables
Daily rhythm
About 137 transactions per store per day at an average ticket near $7.85
Operating footprint
Cafes, drive-thru-only units, and kiosks across Coastal, Inland, and Northwest reporting regions
Customer mix
Members and guests buying espresso, coffee, tea, food, bakery, and retail beans across four channels
Marlcairn Coffee is a fictional regional quick-service coffee chain used for synthetic BI analysis. The company is not real. Its story is built around a maturing multi-unit cafe base where store sales, daypart and drive-thru behavior, product mix, loyalty engagement, and speed-of-service come together into one trustworthy analytics environment.
These are the operating questions this kit is optimized to answer first.
Move from a chain KPI down to store, day, daypart, channel, product, and ticket on one consistent sales model.
The AM Peak dominates traffic but runs the slowest service, 224 seconds against 155 in the Early AM, and keeps slowing.
Drive-thru share separates cleanly by store format, from 87 percent lane share to zero at the kiosks.
Beverages lead while limited-time offers climb from about 6 percent of units to nearly 15 through the fall and holiday peak.
Compare year two against year one for every cafe, from Hollowbrook up 28 percent to the Elmsworth kiosk down 9.
Compare redemption volume, discount cost, and incremental mix across thirty campaigns, each with its own window, rate, and channel scope.