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Success story · A national quick-service restaurant chain

Data-driven rewards optimization delivered higher loyalty growth at a lower promotional cost.

14%Increase in loyalty signups within targeted dayparts and markets
11%Lift in repeat visits among active members receiving tuned
8%Reduction in discount dollars per incremental visit

A national quick-service restaurant chain operates a large, multi-market QSR network with strong morning traffic, mobile order-ahead, and drive-thru behaviors. The brand already runs a successful loyalty program, but performance varies widely by market, daypart, and visit habit, which means national offers don’t always produce the best local or lifecycle outcomes.

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Retail & consumerMarketing & loyaltyAI & machine learning
Impact
14%Increase in loyalty signups within targeted dayparts and markets.
11%Lift in repeat visits among active members receiving tuned, behavior-based offers.
8%Reduction in discount dollars per incremental visit due to smarter offer selection.
01The Situation

The Situation

Business Problem

Loyalty was performing, but not at its full potential because offers were mostly broad-based and not consistently tuned to customer lifecycle or to actual visit patterns (commuter AM vs. Weekend PM). The objective was to use first-party transactions and loyalty data together with public, aggregated spend/rewards signals to identify where targeted incentives would convert best—and to activate those through the brand’s owned channels for both acquisition and frequency.

02The Challenge

The Challenge

Multiple data sources needed to be unified and simplified into signals that marketing and operations could actually use to target and measure offers. At the same time, the program needed consistent eligibility logic across app, mobile order-ahead, and in-store experiences, plus clean A/B testing and guardrails so that promotional spend went down while loyalty growth and repeat visits went up.

03The Solution

The Solution

We used AI to personalize rewards for new and existing members by combining first-party transactions and loyalty behavior with public, aggregated spend signals, then tested targeted offers to prove incremental lift.

How the system works SIGNALSREWARDS ENGINEOWNED CHANNELSTransactionsLoyalty dataSpend panels01Segments by lifecycle02Scores propensity to respond03Picks offer, depth, timingMargin capsFrequency capsGUARDRAILS · A/B TESTEDAPP · ORDER-AHEAD · IN-STOREOne eligibility logicacross every channelSPEND REALLOCATEDToward the cohorts andoffers that convertINCREMENTAL LIFT PROVENBY CLEAN A/B TESTS
  • 01Lifecycle segmentation and propensitiesLifecycle segmentation (new, routine, lapsing) plus propensities for conversion, morning-daypart frequency, basket lift, and offer fatigue.
  • 02Offer selection with guardrailsChooses the incentive type (points multiplier, bundle, $-off), depth, and timing by lifecycle and behavior; enforces margin and frequency caps.
  • 03Spend reallocationReallocates promotional spend toward the best-performing cohorts and offers to avoid over-discounting.

Impact

What changed, as reported.

The figures as printed in the firm's own case-study collateral — nothing rounded or annualised.

14%Increase in loyalty signups within targeted dayparts and markets.
11%Lift in repeat visits among active members receiving tuned, behavior-based offers.
8%Reduction in discount dollars per incremental visit due to smarter offer selection.

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Client background

A national quick-service restaurant chain.

A national quick-service restaurant chain operates a large, multi-market QSR network with strong morning traffic, mobile order-ahead, and drive-thru behaviors. The brand already runs a successful loyalty program, but performance varies widely by market, daypart, and visit habit, which means national offers don’t always produce the best local or lifecycle outcomes.

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