Bakery chain member retention and holiday marketing: wake up dormant members
Client Profile
Pain Points
Coarse member tags
No preference tags means outreach misses the mark.
Time-consuming holiday campaigns
Every holiday needs copy, posters and pushes from scratch.
Too many dormant members
Members take coupons and never return, dragging repeat rates.
Live Demo
How We Built It
Member segmentation
Tag members by frequency, category and ticket size.
Auto holiday campaigns
Auto-generate holiday copy, posters and pushes.
Dormant-member reactivation
Differentiated offers based on why members went dormant.
Not betting on one AI — combining the right few
For a real business scenario, we don't lock in a single technical path up front. We evaluate candidate paths on the same benchmark, then design a hybrid so each path does what it's best at.
General LLM API
Public-cloud models such as GPT-4o / Qwen / DeepSeek
- ✓Out-of-the-box, minimal integration cost, cheap to start
- ✕Limited generalization to vertical domains, unstable field-level accuracy, separate compliance design
Industry fine-tuned model
Domain fine-tuning of open-source models on industry data
- ✓Grasps industry semantics, much higher accuracy, private deployment
- ✕Needs labeled data plus ongoing training and upkeep
RAG + rule engine
Retrieval-augmented knowledge plus business rule checks
- ✓Highly targeted, explainable, best latency and cost
- ✕Requires continuous data-loop investment
Fusion, not selection
What we deliver is not a single model but a fused pipeline of primary path + semantic correction + graceful fallback: routine cases are handled by the industry model in seconds; low-confidence fields are re-understood by an LLM; on service anomalies the system degrades to a general API and flags manual review — achieving accuracy, cost, latency and controllability at once.
An explainable, degradable, evolvable AI pipeline
We treat AI engineering as a pipeline, not a black box: every step has clear responsibilities, inputs/outputs and fallbacks. The five-step pipeline plus four-layer architecture below is the technical skeleton.
Ingestion & access layer
Multi-channel access with unified auth, idempotency and masking
AI inference layer
Multi-path inference plus a confidence router switching by features
Business orchestration layer
Connecting results to real business flows with rule checks and closure
Data loop layer
Every online error flows back; models iterate monthly
Tech Stack
Delivery Process
Member audit
Cleaned member data and built the tag system.
Marketing automation
Built automated outreach flows.
Holiday campaign
Piloted the first holiday campaign.
Review
Compared repeat data before and after.
Routine ops
Monthly iteration of member operations.
Before vs After
“Holiday campaigns now generate and push themselves, and dormant members are slowly coming back.”
Where It Fits
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