Too many LTOs. No way to know which ones actually worked.
The brand was running a regular cadence of limited time offers — a core part of the marketing calendar for any fast casual chain. But the LTO program had a fundamental problem: there was no reliable way to distinguish true incremental lift from margin cannibalization. A promotion that appeared successful in overall sales might simply be pulling forward purchases from guests who would have visited anyway, at a lower margin.
The cadence of offers also risked creating customer fatigue — conditioning guests to wait for a deal before visiting. Without a targeting layer, every offer went to the full database regardless of which guests were most likely to respond. The data to answer these questions existed in Bikky, Thanx, and the POS — but it had never been connected into a model that could inform LTO strategy.
The risk of continued broadcast: Too many undifferentiated LTOs leads to fatigue, margin erosion, and operational complexity — without the intelligence to know which to discontinue and which to double down on. AI/ML plus disciplined experimentation was the answer.
Five controlled experiments. Segment-specific targeting. Three campaign blueprints ready to scale.
Rower designed and executed five controlled experiments to validate Bikky’s predictive accuracy across segments, channels, and offer types — built for speed and minimal operational disruption while producing statistically valid results.
Performance benchmarking: Each experiment quantified promotional ROI lift from Bikky-powered targeted campaigns versus broadcast. Success was defined as demonstrating 1.5–3x ROI improvement with specific insights on which segments, offers, and channels drove performance.
Insight codification: Rower documented segment response patterns, preferred channels, optimal offer types, and daypart sensitivities — building the institutional knowledge to make every future LTO decision smarter than the last. Daypart sensitivity emerged as a stronger predictor of offer response than demographic segment — a finding that reshaped the brand’s entire LTO planning framework.
Campaign architecture: Three full-scale campaign blueprints were built covering menu integration, pricing considerations, operational requirements, and franchisee enablement — each ready for 60–90 day system-wide rollout without further engineering work.
LTO decisions backed by data. Every offer targeted. Every result measured.
- 1.5–3x ROI improvement demonstrated for Bikky-powered targeted campaigns versus broadcast — validating that segment-specific targeting dramatically outperforms one-size-fits-all offers
- $2.5–5M in LTO performance improvement identified from 5–10% effectiveness improvement via data-science-driven planning at optimal cadence
- Daypart sensitivity identified as the strongest predictor of offer response — reshaping the LTO planning framework from demographic-first to behavioral-first
- Three campaign blueprints delivered — menu-integrated, pricing-sensitive, franchisee-enabled — ready for immediate system-wide rollout
- Weak LTOs identified for discontinuation; high-performing offer types flagged for investment — the first time the brand could make these decisions based on controlled evidence rather than sales totals
Thanx reference architecture for LTO execution. Problem: too many LTOs → fatigue, margin erosion, ops complexity. Solution: AI/ML + disciplined experimentation → discontinue weak LTOs, double down on winners. Flow: Customer Touchpoints (Mobile App, Guest, Kiosk/QR) + Transact (Olo, Payment Processor/Tokenized Cards, POS/Qu+Aloha) → Thanx (Webhooks & Events, Offers & Rewards Catalog, SDK/API, Earn & Burn Engine, Member Profiles) → CDP (Identity Resolution → Customer 360/LTV → Audience Builder) → Engage (SMS, POS, Email, Apps, Websites, In-App Messaging) → BI/Dashboards.
The data stack was in place — Bikky, Thanx, and Olo already purchased. What was missing was the orchestration layer connecting them to execution across targeting, sign-ups, LTO performance, and AI segmentation.
The ROI logic: Increase loyal customer spend = highest-margin revenue. Automated sign-ups = lower CAC, larger loyalty base. Smarter LTOs = higher ROI per promo. Each lever is additive and compounds the others.
KR3 (Experimentation, 45 days): 5 controlled experiments validating Bikky accuracy across segments, channels, and offers. Performance benchmarking (1.5–3x ROI target), insight codification, and 3 campaign blueprints ready for 60–90 day rollout.
