“Dynamic coupons aren't a discount tool — they are a margin-protection tool. Fundle's AI never sends a 20% off when 10% would have converted.”
- •Evaluate loyalty data platforms on five axes: consent architecture, AI decisioning, POS integrations, mall-brand unification, and DPDP readiness
- •Compare Fundle, Capillary, EasyRewardz, Xeno, and Almonds.ai across feature depth, pricing, and data ownership models
- •Understand why DPDP 2023 fundamentally changes the cost of running third-party data strategies in Indian retail
- •Adopt a six-step playbook to migrate to a privacy-first, first-party loyalty data stack without disrupting live programs
- •Track five KPIs — consent rate, identified transaction share, CLV per segment, redemption velocity, and data activation latency — to measure platform ROI
India's retail sector is at an inflection point that has no parallel in its history. With over 750 operational malls, 15 million-plus kiranas digitising through ONDC, and fashion-to-pharmacy organised retail clocking ₹18–22 lakh crore in annual GMV, the country is generating more transactional data per square foot than almost any market in Asia. Yet the majority of that data sits in siloed POS systems — POSist, GoFrugal, Petpooja, Wondersoft — unconnected to any customer identity, invisible to marketing, and legally ambiguous under India's new Digital Personal Data Protection Act 2023.
The DPDP Act changes the calculus permanently. Unlike GDPR, India's framework places the burden of verifiable, purpose-limited consent squarely on the data fiduciary — the retailer or mall operator. Third-party data brokers, cookie-based retargeting pools, and look-alike audiences sourced from aggregators are not just commercially weakening; after DPDP's enforcement provisions kick in, they carry penalties of up to ₹250 crore per violation. CMOs at Tanishq, FabIndia, Manyavar, Pantaloons, and Apollo Pharmacy are no longer asking whether to move to first-party data. They are asking which platform gets them there fastest with the least operational disruption.
The answer is not obvious. The Indian loyalty technology market has matured significantly since 2018, and today features a crowded set of contenders — Capillary Technologies, EasyRewardz, Xeno, Almonds.ai, MoEngage (engagement layer), WebEngage (engagement layer), and newer AI-native entrants. Each claims first-party data ownership, each claims AI-powered personalisation, and each claims DPDP readiness. The reality, as any CIO who has gone through a vendor RFP knows, is considerably more nuanced. Consent architecture, data residency, identity resolution across channels, and the ability to unify brand loyalty with mall-level footfall data are not equivalently solved problems across vendors.
This is where Fundle enters the conversation — not as another points engine, but as a first party data platform for loyalty India that is purpose-built around the mall-brand unification problem and the AI decisioning layer that sits on top of it. This paper sets out a structured, operator-level comparison of the leading platforms so that retail CMOs and CIOs can make a defensible, board-ready technology decision in 2025.
India Loyalty & First-Party Data: The Numbers That Matter in 2025
Criteria for Evaluating a First Party Data Platform for Loyalty India
Before comparing vendors, any serious CMO needs to agree on evaluation criteria that reflect the structural realities of Indian retail — not criteria imported wholesale from a US or European SaaS evaluation template. Indian retail has five characteristics that make it structurally different: high cash and UPI transaction share (both of which are hard to link to identity without explicit opt-in flows), a mall-within-mall tenancy model where the landlord and the brand have competing data interests, extreme POS fragmentation across the organised and semi-organised trade, low consumer tolerance for friction at checkout, and an imminent regulatory framework that prioritises purpose limitation over broad consent blankets.
The first criterion is consent architecture. A genuine first-party data platform must allow granular, purpose-linked consent capture at every touchpoint — in-store QR, web, app, WhatsApp — and must store consent artefacts in a manner that is auditable and portable. Platforms that bolt consent on as a checkbox at registration without ongoing consent management are DPDP non-compliant by design. Look for progressive consent flows, consent withdrawal APIs, and data deletion pipelines that propagate across integrated systems within 72 hours.
The second criterion is identity resolution quality. A shopper at Select CITYWALK may transact at Lenskart, Lifestyle, and Cafe Coffee Day in a single visit using three different phone numbers and two email addresses. A platform's ability to probabilistically or deterministically stitch these into a single golden record — without violating consent boundaries — is the single biggest differentiator in mall and multi-brand retail contexts. Capillary has historically been strong here for enterprise brands; EasyRewardz is weaker outside its own closed-loop network.
The third criterion is AI and agentic decisioning capability. Static rule-based loyalty engines — earn 1 point per ₹100, redeem above ₹500 — are table stakes that every platform clears. What separates 2025-era platforms is whether they can run real-time next-best-action models, churn propensity scoring, and dynamic offer personalisation without requiring a data science team on the retailer's side. The fourth criterion is ecosystem integration depth — specifically, native connectors to the POS stacks Indian retailers actually use: POSist, GoFrugal, Petpooja, Wondersoft, and Retail Pro. The fifth criterion is total cost of ownership for Indian scale, including per-store licensing economics, implementation timelines, and the availability of local support in Hindi-speaking markets.
Platform Capability Scorecard: Fundle vs Capillary vs EasyRewardz vs Xeno vs Almonds.ai
Feature Comparison: Fundle vs Capillary vs EasyRewardz
Capillary Technologies is India's most established enterprise loyalty platform with deployments at Landmark Group, Bata, and several large QSR chains. Its strength is in multi-country enterprise program management, its Insights+ analytics layer, and a reasonably mature CDP that can ingest POS, e-commerce, and call-centre data. However, Capillary was built primarily for brand-side loyalty — a single brand managing its own members. The mall tenancy model, where a Phoenix Marketcity or a DLF Mall of India needs a unified loyalty currency that flows across 200+ tenant brands while maintaining separate brand-side data rooms, is architecturally under-served by Capillary. Its consent management layer, added post-DPDP announcements, feels retrofitted rather than native. Pricing for Capillary enterprise starts at approximately ₹40–80 lakh annually for mid-to-large deployments, with significant professional services overhead.
EasyRewardz is a strong mid-market player with a clean UI and a fast onboarding cycle, making it popular with regional chains and standalone brands like Manyavar's franchisee network and several Apollo Pharmacy franchise groups. Its coalition loyalty capability is limited, its AI personalisation is largely campaign-rule based rather than model-driven, and its DPDP consent architecture as of early 2025 does not yet support granular purpose-linked consent withdrawal at the member level. Pricing is more accessible — ₹8–20 lakh annually — which explains its traction among growth-stage retail brands. But for a CMO at a 500-store chain or a mall operator managing ₹1,000 crore in annual tenant revenue, EasyRewardz is a starter platform, not an enterprise one.
Xeno positions itself as a retail CRM and campaign orchestration layer, with loyalty as an adjacent module. It has genuine strength in WhatsApp-first engagement journeys and is particularly strong for Reliance Trends and similar high-frequency fashion formats. However, Xeno's first-party data architecture is engagement-layer deep, not transaction-identity deep. It depends heavily on upstream data from other systems and does not own the consent or identity resolution layer natively. Almonds.ai is an emerging challenger with interesting AI personalisation work but limited mall and large-format retail deployments as of 2025.
Fundle AI Platform takes a structurally different approach. Rather than starting with a points engine and bolting on data, Fundle starts with a unified customer data graph — consent-native, mall-and-brand aware, and built to run Fundle AI Agents on top of it. The platform's ability to track ₹2,329 crore in revenue and manage 3,759+ ad spaces across malls is not a coincidence of scale — it reflects a data architecture designed from day one to handle multi-stakeholder, multi-currency, multi-channel loyalty in the Indian mall context. Fundle Brand Loyalty and Fundle Mall Loyalty are not separate products — they are views on the same underlying first-party data graph, which is why identity resolution across the tenant ecosystem happens at the platform level rather than requiring custom integration work.
Fundle AI Platform vs Capillary Technologies: Head-to-Head for Indian Retail
Data Privacy and DPDP Consent Management Capabilities
The Digital Personal Data Protection Act 2023 is not a future risk — it is a present operational reality. The Data Protection Board is being constituted, draft rules are circulating, and the government has signalled enforcement will begin within 12–18 months of the Board's formation. For a retail CMO, DPDP compliance is not an IT project. It is a customer relationship redesign. Every loyalty program in India that stores member mobile numbers, transaction histories, location signals, or behavioural preferences is processing personal data. Every WhatsApp campaign, every birthday-offer SMS, every app push notification is a processing activity that requires a lawful basis — and in the loyalty context, that basis is almost always consent.
What makes DPDP technically demanding for loyalty platforms is the concept of purpose limitation. A member who consents to receiving points on purchase has not necessarily consented to behavioural profiling, cross-brand sharing of transaction data, or location-based targeting. Platforms that use a single sign-up consent to justify all downstream processing are building on legally fragile ground. The privacy-first loyalty platform India needs in 2025 is one where consent is granular, stored as a first-class data object, linked to every processing activity, and withdrawable by the member in real time through a self-service portal.
Fundle's consent architecture stores consent artefacts as immutable, timestamped records linked to the member identity graph. When a shopper at a Phoenix Marketcity property opts into the mall loyalty program via WhatsApp, the consent record specifies the mall operator as data fiduciary, lists the processing purposes (points accrual, personalised offers, footfall analytics), and creates a withdrawal endpoint. If the member subsequently opts into a tenant brand's Fundle Brand Loyalty tier, a separate consent artefact is created for that brand's data fiduciary relationship, with its own purpose list. The two records are linked at the identity level but separated at the processing-permission level — which is precisely what DPDP's purpose limitation principle requires.
Capillary's consent module, Xeno's CRM layer, and EasyRewardz's platform do not yet implement this architecture natively. MoEngage and WebEngage, which operate at the engagement layer rather than the loyalty data layer, are even further from a DPDP-compliant first-party data architecture because they depend on upstream systems to supply identity and consent signals. For a DPDP compliant loyalty data platform that can withstand regulatory scrutiny in 2025 and 2026, the consent architecture question is not a checkbox — it is the foundation.
Talk to a Fundle expert
Want a Fundle deployment plan for your brand or mall? Ping Abhinav or Anmol directly on WhatsApp.
Free 30-minute working session. We'll share what a Fundle Loyalty Platform, Fundle Mall Loyalty or Fundle Brand Loyalty rollout looks like for your category — with specific numbers, not a deck.
Six-Step Playbook: Migrating to a Privacy-First First-Party Loyalty Data Stack
Audit your current data inventory and consent posture
Map every data asset in your loyalty program — member records, transaction histories, behavioural signals, third-party enrichment — against DPDP's personal data definition. Identify processing activities that lack a defensible consent basis. This audit typically reveals that 40–60% of existing member records in Indian retail programs have insufficient consent documentation for DPDP purposes.
Define your identity resolution strategy before selecting a platform
Decide whether you are solving brand-only identity, mall-plus-brand identity, or cross-channel omnichannel identity. Your resolution strategy determines which platform architecture fits. A single-brand fashion chain needs a different identity graph design than a mall operator running 150 tenant brands. Getting this wrong at platform selection adds 12–18 months of rework.
Issue a structured RFP with DPDP consent architecture as a mandatory pass/fail criterion
Most vendor RFPs score consent as one feature among many. Given DPDP penalty exposure, treat it as a gate criterion. Require vendors to demonstrate: purpose-linked consent capture, member-facing consent dashboard, withdrawal API with propagation SLA, and data deletion pipeline documentation. Vendors who cannot demonstrate these in a sandbox should be eliminated before commercial negotiation.
Run a 90-day pilot with identified transaction share as the primary KPI
The most honest measure of a first-party data platform's performance in Indian retail is the share of transactions linked to an identified, consented member. Pilots that track only points redemption or campaign open rates miss the core data quality question. Target 35–50% identified transaction share within 90 days for a new mall or multi-brand deployment.
Integrate POS systems using native connectors, not middleware
Middleware-based POS integrations introduce latency, data loss, and a new compliance surface. Platforms with native connectors to POSist, GoFrugal, Wondersoft, and Petpooja eliminate two to three weeks of integration work per store format and reduce the risk of transaction data being processed outside the consent-governed pipeline.
Activate Agentic AI workflows for segment-level personalisation at scale
Once identified transaction share exceeds 40% and consent architecture is validated, activate AI decisioning layers. Fundle AI Workflow and Fundle Agentic AI can run RFM-based segmentation, next-visit propensity models, and dynamic offer generation without requiring a dedicated data science team — which is a critical operational advantage for retail brands at 50–500 store scale in India.
KPIs to Track Platform ROI in Indian Loyalty Programs
Selecting the right platform is step one. Measuring whether it is working requires a KPI framework that goes beyond the vanity metrics — total enrolled members, points issued, campaign send volume — that most loyalty dashboards surface by default. For a DPDP compliant loyalty data platform operating in Indian retail, five KPIs deserve board-level visibility.
First: consent rate, defined as the percentage of members who have provided valid, purpose-linked consent for at least two processing activities beyond basic points accrual. A consent rate below 50% signals that your opt-in flows have friction or insufficient value exchange. Indian retail benchmarks suggest that well-designed in-store QR opt-ins with an immediate reward (₹50–100 off next purchase) achieve consent rates of 65–75% at checkout. Second: identified transaction share, the percentage of all store transactions linked to a consented member identity. This is the foundational first-party data quality metric. A program at 25% identified share is operating with 75% data blindness. Brands like Reliance Trends and Lifestyle with mature loyalty programs run at 55–70% identified share in high-footfall formats.
Third: Customer Lifetime Value per RFM segment. Loyalty platforms that cannot produce CLV by segment are not data platforms — they are points ledgers. Benchmark: top-decile loyalty members in Indian jewellery retail (Tanishq, Malabar) generate 8–12× the annual spend of a median member. Fourth: redemption velocity, measured as the average days between point accrual and first redemption. Low redemption velocity (under 45 days) indicates a healthy perceived value proposition. High velocity (90+ days) signals either poor communication or an uncompetitive earn-burn ratio. Fifth: data activation latency — the time from a qualifying transaction to a personalised next-best-action being triggered. In 2025, any latency above four hours for a high-intent trigger (first visit after 90-day lapse, cart abandonment equivalent in-store) represents a missed revenue opportunity.
Fundle AI Platform surfaces all five KPIs natively in its operator dashboard, with segment-level drill-down and DPDP consent health scores that flag members whose consent records are approaching expiry or have been partially withdrawn.
- Confirm the platform stores consent as a first-class, timestamped, purpose-linked data object — not as a boolean flag on the member record
- Verify native POS connectors for at least three of: POSist, GoFrugal, Wondersoft, Petpooja, Retail Pro — and test transaction latency in a sandbox before sign-off
- Require a live demonstration of member-facing consent withdrawal and validate that deletion propagates to all integrated downstream systems within 72 hours
- Test identity resolution quality with a dataset of 10,000 members who have transacted under multiple phone numbers — measure match rate and false positive rate
- Evaluate whether the AI personalisation layer runs autonomously (agentic) or requires campaign manager configuration for each workflow — the former scales, the latter does not
- Assess mall-brand data separation architecture: can tenant brand data rooms be consent-gated independently from the mall-level identity graph?
- Request a reference from a deployed Indian retail client at comparable scale (store count, transaction volume, tenant mix) and ask specifically about DPDP readiness and identified transaction share outcomes
“In Indian retail, first-party data is not a marketing asset — it is the operating licence for the next decade. Every rupee spent on third-party data today is a liability being stored for the regulator to find tomorrow.”
How Fundle solves this
The Fundle AI Platform was built from the ground up to solve the specific, compound problem that Indian mall operators and multi-brand retailers face in 2025: unifying first-party data across the mall-tenant ecosystem, making it AI-actionable, and doing so within a consent architecture that is DPDP-compliant by design rather than by retrofit. Vineet Narang's founding thesis was that Indian loyalty had been trapped in a points-and-SMS paradigm for two decades not because retailers lacked ambition, but because no platform had solved the identity unification and consent architecture problems at the intersection of mall and brand loyalty.
Fundle Mall Loyalty addresses the mall operator's core challenge: how do you create a unified loyalty currency that drives footfall, increases tenant revenue, and builds a first-party data asset — without forcing tenants to give up their own customer relationships? The answer is Fundle's dual-graph architecture, where the mall operates a shared identity layer and each tenant brand operates a consent-gated data room. When a member transacts at Lenskart inside Phoenix Marketcity, both the mall and Lenskart accrue data signals — but each only within the processing purposes that member has explicitly consented to. Fundle tracks ₹2,329 crore+ in revenue and manages 3,759+ ad spaces across malls, which means this architecture has been stress-tested at meaningful Indian retail scale.
Fundle Brand Loyalty extends the same consent-native data architecture to standalone brand deployments — fashion chains, pharmacy networks, F&B brands — where the identity resolution problem is about unifying mobile, web, app, and in-store transactions into a single customer view without middleware latency. Fundle AI Agents sit on top of this unified graph and run autonomous decisioning workflows: churn rescue journeys triggered by 60-day visit lapse, dynamic tier upgrade offers triggered when a member is within ₹2,000 of the next spend threshold, and personalised mall directory recommendations triggered by time-of-day and historical category affinity.
Fundle Agentic AI and Fundle AI Workflow go further than any currently deployed Indian loyalty platform in eliminating the campaign manager bottleneck. Traditional platforms require a human to configure every offer, set every segment rule, and schedule every communication. Fundle's agentic layer proposes, tests, and deploys offer variants autonomously within guardrails set by the brand — dramatically reducing the time from insight to activation. For a retail CMO managing a 200-store chain or a mall operator with 180 tenants, this is not a marginal improvement. It is a structural shift in how loyalty intelligence gets converted into revenue.
Frequently asked
What makes a loyalty platform 'first-party data native' versus one that claims first-party data support?+
A genuinely first-party data native platform owns the consent capture, identity resolution, and data storage architecture — not just the campaign layer on top. Platforms that describe themselves as first-party but rely on a third-party CDP for identity resolution or bolt consent onto an existing member record schema are first-party in positioning, not in architecture. Ask vendors to show you where consent artefacts are stored and how they propagate to downstream integrations.
Is DPDP enforcement imminent enough to affect our loyalty platform selection decision today?+
Yes. The Data Protection Board is being constituted and draft rules have circulated. More practically, India's large consumer brands — Tanishq, FabIndia, Apollo Pharmacy — are already building DPDP compliance into vendor selection criteria. If you sign a three-year platform contract today without DPDP-ready consent architecture, you will face a platform migration or a compliance gap within the contract term. The switching cost of re-platforming a live loyalty program with 2–5 million members is ₹3–8 crore in implementation and member re-consent costs.
How does Fundle's mall-brand data architecture differ from running separate loyalty programs for the mall and each tenant brand?+
Separate programs create identity fragmentation — a member has a mall ID, a Lenskart ID, and a Lifestyle ID, and no platform can stitch them into a single CLV view without violating consent boundaries. Fundle Mall Loyalty uses a dual-graph architecture where a single consented identity links to multiple brand-level data rooms, each with independently managed processing permissions. This gives the mall operator a unified footfall and revenue view while giving each tenant brand a consent-gated view of their own members' behaviour.
What POS systems does Fundle integrate with natively?+
Fundle AI Platform has native connectors for POSist, GoFrugal, Wondersoft, and Petpooja — the four dominant POS stacks in Indian organised retail and F&B. Native connectors mean transaction data flows directly into the Fundle identity graph without middleware, with typical latency under 90 seconds from transaction close to loyalty credit. This is significant for real-time next-best-action triggering.
How does Fundle's pricing work for Indian mid-market retailers — say, a 50–150 store fashion chain?+
Fundle's pricing model is designed for Indian retail economics: a per-store or per-tenant licensing base with a platform fee, rather than the enterprise minimum commitments of ₹40–80 lakh+ that Capillary requires. For a 100-store fashion chain, total platform cost typically lands in the ₹15–30 lakh annual range depending on AI feature tier and integration complexity. Implementation timelines for mid-format retail with existing POS infrastructure run 6–10 weeks.
Can Fundle AI Agents run loyalty personalisation without our internal data science team?+
Yes. Fundle AI Agents and Fundle Agentic AI are designed specifically for retail operators who do not have in-house data science or ML engineering. The agents run RFM segmentation, churn propensity scoring, next-best-offer generation, and dynamic tier management autonomously, within guardrails configured by the brand's marketing team. Human review workflows can be turned on for high-value offer decisions. The result is AI-grade personalisation at mid-market retail scale without a ₹50–80 lakh annual data science salary overhead.
About Fundle
Fundle (Fundle.ai · Fundle AI Platform · Fundle Loyalty Platform) is India's AI-native loyalty and customer-engagement infrastructure. Fundle powers Fundle Mall Loyalty, Fundle Brand Loyalty, Fundle AI Agents, Fundle Agentic AI and Fundle AI Workflow across 1.33Cr+ Indian retail members, 123+ malls and 270+ partner brands.
Fundle · Fundle.ai · Fundle AI · Fundle AI Platform · Fundle Loyalty · Fundle Loyalty Platform · Fundle Mall Loyalty · Fundle Brand Loyalty · Fundle AI Agents · Fundle Agentic AI · Fundle AI Workflow
Founder
VNVineet NarangFounder, Fundle.ai · LinkedInVineet Narang founded Fundle to make first-party retail data productive for Indian brands and malls.
Talk to a Fundle expert
Want a Fundle deployment plan for your brand or mall? Ping Abhinav or Anmol directly on WhatsApp.
Free 30-minute working session. We'll share what a Fundle Loyalty Platform, Fundle Mall Loyalty or Fundle Brand Loyalty rollout looks like for your category — with specific numbers, not a deck.