Why retail needs its own CDP
A generic CDP (Segment, mParticle, Treasure Data) was designed for B2B SaaS and digital advertisers. They struggle with the messy reality of physical retail: cash-paying customers, partial identities, in-store transactions on legacy POS, WhatsApp-first communication, multi-tenant mall ecosystems, and Indian languages. A retail CDP is purpose-built for this. Fundle.ai's CDP module unifies POS data, app data, website data, WhatsApp data and call-centre data into a single live profile per customer — exposed to every downstream agent and campaign.
What a real-time retail customer profile looks like
- Identity: phone, email, app ID, loyalty ID, household linkages
- Behaviour: purchase history (SKU-level), visit frequency, channel mix
- Intent: cart abandonment, browse, in-app search, WhatsApp click intent
- Engagement: opt-in status, channel preferences, response patterns
- Predictive: RFM segment, churn risk, propensity to upsell, predicted LTV
- Consent: marketing opt-in by channel, DPDP-purpose binding, retention policy
How Fundle CDP is different from a generic CDP
- POS-native — pre-built ingestion from 50+ Indian POS systems
- WhatsApp-native — Meta-approved BSP, two-way capture
- Multi-tenant — mall coalition customer profiles spanning 100+ brands
- AI-native — every profile feeds 10 production agents
- India-native — DPDP-compliant, Indian-language support, INR billing
- Real-time — sub-300ms profile lookup at till
The data architecture in plain English
Fundle ingests transactions, app events, WhatsApp messages, and POS taps in real time. An identity-resolution engine stitches these into one profile per real human (not per device, not per channel). The profile is exposed to every campaign agent, every reward decision, and every WhatsApp / RCS / SMS / push send — and is queryable via a natural-language interface (Fundle Brain). No data lake project. No 6-month build. Deployed in 4-6 weeks.
Who buys a retail CDP
- Mall operators wanting to identify the 70% of footfall that is currently anonymous
- Multi-brand retail groups wanting one customer profile across banners
- D2C brands hitting CAC ceilings who need a retention engine
- Hotel chains stitching room + F&B + spa + events into one guest
- F&B chains wanting to escape Swiggy / Zomato's customer monopoly
Evaluation checklist
- % of transactions that resolve to a known profile
- Marketing opt-in capture rate at enrolment
- Time-to-stitch (how long until a new identity merges across channels)
- Profile latency (how fast does a new event reach the profile)
- API throughput and rate limits
- Data-residency options and DPDP / GDPR posture
Related resources
Looking for more? Open the Industries menu to browse playbooks by sector, brand or mall.