01
Lapsed customer win-back
Each lapsed member gets a unique offer sized by their predicted reactivation propensity.
Generate millions of unique single-use coupons matched to each customer's behaviour, churn risk and category affinity. Fraud-proof, attributable, AI-optimised.
Section 01
The thesis
The problem
Bulk coupon codes leak instantly. The same "FLAT20" is on every cashback site within hours, eroding margins and breaking the link between campaign and revenue. Worse: every shopper gets the same offer regardless of whether they need it.
The Fundle approach
Fundle generates a unique, single-use, member-bound coupon for every targeted customer. The offer is AI-chosen — category affinity, recency, churn risk, predicted lift — and validates instantly at POS or web checkout. Leakage drops to zero; attribution becomes exact.
Section 02
The capabilities
Unique-code generation at scale (1M+ codes / campaign)
Member-binding: validates only against the issued customer
AI offer selection: best discount/reward per member, capped by margin rules
Multi-channel delivery: WhatsApp, SMS, Email, App, printed receipt QR
POS validation: real-time check at 50+ Indian POS systems
Fraud controls: velocity limits, IP fingerprinting, duplicate-attempt alerts
Real-time redemption analytics: per offer, per segment, per channel
In production
Use cases
01
Each lapsed member gets a unique offer sized by their predicted reactivation propensity.
02
Fashion buyers get unique footwear coupons calibrated to their basket history.
03
Every member gets a unique mix of tenant offers; coalition burn settled in real time.
Questions
Most asked
Pre-built connectors for Pine Labs, Ezetap, Mosambee, Innoviti, Ginesys, LS Retail, GOFRUGAL and Wondersoft. New POS connectors typically ship in 2-3 weeks.
Offer eligibility is capped by margin rules per SKU/category, and the AI optimises for incremental margin lift, not just redemption rate.
Next step
A 30-minute working session with a Fundle loyalty strategist and a solutions engineer — tailored to your brand or mall.
Hi 👋 I'm Abhinav
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