“We didn't build Fundle to sell software. We built it to make first-party data productive — every campaign, every store, every shopper, every day.”
- •Understand what the Digital Personal Data Protection Act 2023 demands from loyalty programmes collecting customer consent
- •Evaluate platforms on consent granularity, withdrawal mechanics, and audit trails — not just points and rewards
- •Prioritise deep POS integration: a platform that cannot talk to your billing system cannot capture consent at the moment of transaction
- •Stress-test data security architecture including role-based access, encryption, and breach notification SLAs
- •See why Fundle AI Platform was architected from day one around consent-first data flows, not retrofitted compliance
India's Digital Personal Data Protection Act 2023 is not a future concern. It is a present operational reality. The rules governing how retailers collect, store, process, and delete customer data are being finalised, and early signals from the Ministry of Electronics and Information Technology make clear that consent will be the load-bearing pillar of the entire framework. For a retail CMO or CIO running a loyalty programme across Phoenix Marketcity, Select CITYWALK, or a network of Reliance Trends or Lifestyle stores, this is not an abstract legal problem — it is a platform architecture problem.
Most loyalty platforms in India were not built with consent based loyalty data management at their core. They were built to push points, automate offers, and drive repeat visits. Consent was an afterthought — a pre-ticked checkbox on a paper enrolment form or a buried clause in a 4,000-word app terms document. That era is ending. The DPDP Act creates explicit obligations around informed, specific, free, and revocable consent. It introduces the concept of the Data Principal — your customer — who has enforceable rights to access, correct, and erase their data. Violations carry penalties of up to ₹250 crore per instance. That number concentrates the mind.
The Indian retail loyalty market is enormous and growing fast. There are an estimated 600-plus organised mall properties across Tier 1 and Tier 2 cities. India's organised retail sector crossed ₹12 lakh crore in FY24. Loyalty programmes that capture first-party data from even a fraction of those footfalls represent some of the richest consumer datasets in the world. But that richness becomes a liability overnight if the consent architecture underneath it is not sound. A single regulatory audit, a single data breach, or a single viral consumer complaint about unsolicited marketing can unravel years of brand equity — ask any CMO who has managed a WhatsApp blast gone wrong.
This is precisely why Fundle was designed differently. Rather than bolt on a consent layer over an existing points engine, the Fundle AI Platform treats consent as the foundational data contract between the retailer and the customer. Every downstream workflow — segmentation, personalisation, AI-driven offer generation, cross-brand data sharing in a mall environment — is gated by that consent state. This article is a practical buying guide for Indian retail CMOs and CIOs who need to evaluate loyalty platforms not just on features and commercial terms, but on their structural readiness for a consent-first, DPDP-compliant operating environment.
India Loyalty & Data Privacy: The Numbers That Matter
Assessing Consent Collection and Management Features
The first and most critical dimension to evaluate in any consent based loyalty data management platform is the consent collection architecture itself. This goes well beyond a single opt-in checkbox. Under the DPDP Act, consent must be specific to purpose — a customer who agrees to receive promotional SMS cannot be assumed to have consented to their purchase data being shared with a third-party analytics vendor or a co-tenanting brand in a mall. Platforms that treat consent as a binary on/off switch will fail this test comprehensively.
Look for granular purpose-based consent flows. A mature platform should allow a retailer like FabIndia or Manyavar to define distinct consent buckets: transactional notifications, personalised marketing, third-party data sharing, location-based offers, and profiling for AI recommendations. Each bucket should have its own consent timestamp, collection channel (app, web, in-store kiosk, WhatsApp), and version of the privacy notice the customer saw when they agreed. This creates a defensible audit trail if a Data Protection Authority inquiry ever lands.
Consent withdrawal is equally important and frequently neglected. The DPDP Act gives every Data Principal the unqualified right to withdraw consent at any time. The withdrawal must be as easy as the original consent — a principle that immediately disqualifies platforms where opting out requires a customer service call or a 7-day email thread. Evaluate whether the platform's withdrawal flow is self-serve, real-time, and propagated immediately downstream to all connected systems — your CRM, your campaign engine, your CDP, and your POS. Platforms that batch-process consent withdrawals overnight create a compliance window that regulators will find unacceptable.
Finally, examine the consent versioning capability. When you update your privacy notice — which you will, multiple times, as the DPDP rules are notified — the platform must be able to re-request consent from affected customers for new or changed processing purposes, without invalidating existing valid consents. Legacy platforms from vendors like EasyRewardz or older deployments of Capillary were not architected for this level of consent lifecycle management. Newer AI-native platforms are building it in from the ground up, and that architectural difference matters enormously when the regulatory clock is ticking.
The Consent Lifecycle in a DPDP-Compliant Loyalty Programme
Integration with Retail POS and CRM Systems
Consent captured at enrolment means nothing if it cannot be enforced at the point of transaction. The POS is where data is actually collected — SKU-level purchase data, basket size, payment method, store location, timestamp. If the loyalty platform cannot communicate with the POS in real time, consent enforcement becomes theoretical. You have no mechanism to prevent the POS from logging data for a customer who has withdrawn consent, and no way to attach the correct consent state to each transaction record.
This is a harder technical problem than it sounds. India's retail ecosystem runs on a fragmented POS landscape. A mid-sized mall like a Phoenix Marketcity property might have tenants running Petpooja, POSist, GoFrugal, Wondersoft, and proprietary enterprise billing systems all under the same roof. A national apparel chain like Pantaloons or Lifestyle will have its own ERP-integrated POS that has been customised over a decade. Any loyalty platform claiming DPDP readiness must demonstrate native, tested integration with this heterogeneous landscape — not a promise of future API availability.
Fundle integrates with 50+ POS connectors to manage consent seamlessly. This is not a marketing claim — it is an operational architecture decision. Each connector is built to pass the customer's consent state as a first-class field in the transaction payload, so that every data record written to the Fundle AI Platform carries an immutable consent provenance tag. Downstream processes — whether that is an AI segmentation model, a Fundle AI Workflow automation, or a mall-level cross-brand analytics report — can only touch data that carries valid, current consent for that specific processing purpose.
CRM integration deserves equal scrutiny. Most enterprise retailers in India run either Salesforce, Microsoft Dynamics, or a homegrown CRM. The loyalty platform must be able to sync consent state changes bidirectionally and in near real time. If a customer withdraws marketing consent at a store kiosk in Bengaluru, that withdrawal must be reflected in the CRM before the next scheduled campaign run — whether that campaign fires in 10 minutes or 10 hours. Any platform that cannot guarantee this propagation SLA is a compliance risk, not a solution.
Legacy Loyalty Platforms vs. Consent-Native Architecture
Data Security and Access Controls
Consent governance and data security are two sides of the same compliance coin. A platform can have flawless consent collection and still expose customer data through inadequate access controls, weak encryption, or misconfigured API permissions. For a retail CMO or CIO, the security architecture of a loyalty data platform is not the IT team's problem alone — it is a board-level reputational risk.
Start with encryption standards. Customer data at rest should be encrypted using AES-256 or equivalent. Data in transit should use TLS 1.2 or higher. These are table stakes. More important is key management: who holds the encryption keys, and can the platform operator access your customer data unilaterally? Platforms that commingle tenant data in a shared schema with shared encryption keys create a risk surface that no enterprise retailer should accept. Look for tenant-isolated data architecture with separate encryption key hierarchies per client.
Role-based access control is the next layer. A loyalty platform serving a mall operator like DLF Mall of India will have multiple user types: mall marketing managers, individual brand managers for tenants like Tanishq or Lenskart, analytics teams, and campaign executives. Each role should have precisely scoped data access — a Tanishq brand manager should never be able to see transaction data from a Lenskart customer who has not consented to cross-brand data sharing. Platforms that offer only coarse-grained admin or read-only roles are not fit for a multi-tenant retail environment.
Breach notification readiness is a dimension that most platform evaluations ignore until it is too late. The DPDP Act and its forthcoming rules will mandate notification to the Data Protection Board and affected customers within specified timeframes of a personal data breach. Ask every platform vendor: what is your breach detection SLA? What is your notification workflow? Who in your organisation has the authority to trigger it? Vendors who cannot answer these questions with specificity — not slide-deck generalities — are telling you something important about their operational maturity.
User Experience for Consent Transparency
Regulators and customers are increasingly aligned on one expectation: consent should be comprehensible, not just legally defensible. A privacy notice written in 9-point legalese that a customer scrolls past in 0.3 seconds is not informed consent in any meaningful sense. The DPDP Act's emphasis on 'clear and plain language' notice is a signal that user experience is now a compliance variable, not just a conversion optimisation question.
For loyalty programmes, this means the enrolment flow — whether in-app, on a web portal, at a mall kiosk, or via a WhatsApp-based onboarding sequence — must present consent choices in plain Hindi, Tamil, Telugu, Kannada, Bengali, or whichever language the customer is most comfortable with. India's linguistic diversity is not a UX nice-to-have; it is a consent validity question. A customer who did not understand what they were agreeing to has not provided free and informed consent, regardless of what the checkbox says.
The consent dashboard — the interface through which customers can review, modify, and withdraw their consent choices — is another critical UX surface. Brands like Apollo Pharmacy or Cafe Coffee Day, which have millions of loyalty members, need a consent centre that is as intuitive as a WhatsApp settings screen. If a customer cannot find where to withdraw consent in under 30 seconds, the platform is structurally non-compliant with the ease-of-withdrawal requirement. Evaluate this flow yourself, as a customer, before signing any contract.
There is also a commercial argument for consent transparency, not just a compliance one. Research from European markets post-GDPR consistently shows that customers who explicitly opt into personalisation with full understanding of what it entails have materially higher engagement rates and lower churn than those whose data is collected passively. Indian retail brands that treat consent transparency as a trust-building exercise — rather than a compliance hurdle — will build first-party data assets that are both legally sound and commercially superior. The brands that will win the next decade of Indian retail loyalty are those that earn data permission, not those that harvest it.
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.
5-Step Playbook for Evaluating a Consent-Based Loyalty Data Platform
Map Your Current Consent Gaps
Audit your existing loyalty enrolment flows across every channel — app, web, kiosk, in-store paper form, WhatsApp. Identify where purpose-specific consent is missing, where withdrawal mechanisms are inadequate, and where consent records lack timestamps or privacy notice version references. This gap map becomes your vendor evaluation scorecard.
Define Your Integration Perimeter
List every POS system, CRM, campaign tool, CDP, and analytics platform in your current stack. Require each vendor to demonstrate — not describe — working integrations with at least 80% of your named systems. For mall operators, this list will include tenant billing systems from at least 5-6 different vendors. Fundle's 50+ POS connector library is the benchmark to test others against.
Run a Security Architecture Review
Issue a formal security questionnaire covering: encryption standards, key management model, tenant data isolation, role-based access control granularity, penetration testing cadence, breach detection SLA, and DPDP breach notification workflow. Score each vendor against your enterprise security baseline, not against their self-reported certifications alone.
Test the Consent UX End-to-End
Create a test customer account and go through the full consent journey: enrolment, purpose selection, preference update, partial withdrawal, full deletion request. Time each step. Attempt it in at least two Indian languages. Evaluate whether the language is plain and specific. This single test will differentiate mature platforms from those with consent features that exist only on sales decks.
Validate Compliance Roadmap and Vendor Accountability
Ask each vendor for their DPDP readiness roadmap with committed delivery dates, not aspirational ones. Understand whether their Data Processing Agreement is DPDP-aligned. Confirm who is the designated Data Protection Officer on their side. Vendors who cannot answer these questions are not ready partners for a compliance-sensitive deployment, regardless of their feature set.
KPIs to Track in a Consent-First Loyalty Programme
Choosing the right platform is only the first decision. Operating a consent-first loyalty programme requires a new set of metrics that sit alongside traditional loyalty KPIs like redemption rate, repeat purchase frequency, and net promoter score. Indian retail operators who do not instrument these compliance and data quality metrics will be flying blind when their first DPDP audit arrives.
Consent coverage rate — the percentage of active loyalty members with valid, current, purpose-specific consent on file — is the foundational metric. For most legacy programmes, this number will be alarmingly low on first measurement. A programme with 2 million enrolled members might discover that only 40% have consent records that meet DPDP standards. That gap represents both a compliance exposure and a marketing limitation, because campaigns can only legally target the consented segment.
Consent withdrawal rate, tracked weekly, is an early warning indicator of customer trust deterioration. If withdrawal rates spike after a specific campaign or a data incident, you need to know within days, not at the next quarterly review. Platforms should surface this metric natively in their analytics dashboards, with drill-down by channel, store, and customer cohort. Brands like Tanishq or Lenskart, which have high customer trust as a brand asset, should treat rising withdrawal rates with the same urgency as a spike in net promoter score decline.
Data completeness per consented purpose is a metric that bridges compliance and commercial value. A customer who has consented to personalised recommendations but whose purchase history is incomplete because of POS integration gaps is a missed revenue opportunity as much as a data quality problem. Track what percentage of your consented customers have sufficient data depth for each AI model or segmentation use case to be meaningful. Finally, erasure request fulfilment time — from customer request to confirmed deletion across all systems — must be measured and reported. Statutory obligations will set the outer limit; best-in-class platforms should target fulfilment well within that limit.
- Purpose-specific consent buckets with individual timestamps and privacy notice version tracking
- Real-time, self-serve consent withdrawal propagated to all connected POS, CRM, and campaign systems within defined SLA
- Native integrations with major Indian POS systems including Petpooja, POSist, GoFrugal, and Wondersoft
- Multilingual consent UI supporting at least Hindi, Tamil, Telugu, Kannada, and Bengali
- Tenant-isolated data architecture with per-client encryption key management
- Automated erasure workflow with audit trail and statutory SLA tracking dashboard
- DPDP-aligned Data Processing Agreement with a named Data Protection Officer on the vendor side
“In India, consent is not a compliance checkbox — it is the new currency of customer trust. The retailer who earns data permission honestly will outperform the one who harvests it quietly, every single time.”
How Fundle solves this
The Fundle AI Platform was not built as a points engine with compliance features added later. Vineet Narang's founding thesis was that in a market like India — with its scale, linguistic diversity, and rapidly maturing regulatory environment — the only sustainable loyalty architecture is one where the customer's consent state is the primary key around which every other data operation is organised. That conviction is visible in every layer of the platform.
Fundle Loyalty and Fundle Mall Loyalty both ship with a purpose-built consent management module that supports granular, per-purpose consent collection across in-app, web, WhatsApp, and in-store kiosk channels. The module handles multilingual consent notices, consent versioning when privacy policies change, and a customer-facing consent centre that meets the DPDP Act's ease-of-withdrawal standard. For mall operators running 100-plus tenant brands under one roof, Fundle Mall Loyalty enforces consent-based data access controls at the brand level — a Manyavar tenant manager cannot access data from a FabIndia customer who has not consented to cross-brand sharing.
Fundle Brand Loyalty extends these controls to enterprise retail chains with multi-city footprints. When a customer of an Apollo Pharmacy store in Chennai withdraws consent for personalised marketing, that withdrawal is propagated in real time through Fundle AI Workflow automations to every connected system — CRM, campaign scheduler, and the store POS — so no non-compliant communication fires in the interim. Fundle AI Agents can also proactively surface consent health metrics to marketing managers, flagging cohorts where consent coverage is dropping before it becomes a compliance problem.
The Fundle Agentic AI capability brings a further dimension: it can dynamically adjust campaign eligibility rules based on live consent state, so that a segment that was valid for targeting at 9am is automatically excluded if consent withdrawals push it below the minimum eligible threshold by midday. This is not something a human campaign manager can police manually across thousands of daily micro-campaigns. It requires an AI-native architecture where consent is a live, queryable data field — not a static tag applied at enrolment. For Indian retail CMOs and CIOs evaluating their options in this space, that architectural difference between Fundle and legacy alternatives like Capillary, EasyRewardz, or Antavo is the single most important factor to pressure-test in any proof-of-concept engagement.
Frequently asked
What does the DPDP Act 2023 require from a loyalty programme's consent management?+
The Digital Personal Data Protection Act 2023 requires that consent be specific to purpose, freely given, informed through clear and plain language notice, and revocable at any time with equal ease to how it was given. For a loyalty programme, this means separate consent for transactional notifications, personalised marketing, and any third-party or cross-brand data sharing — each with its own audit trail.
How does Fundle handle consent withdrawal in real time?+
When a customer withdraws consent through the Fundle self-serve consent centre — available in-app, on web, or via WhatsApp — Fundle AI Workflow propagates that withdrawal to all connected systems including POS, CRM, and campaign schedulers within a defined SLA. No batch processing, no overnight sync delays that create compliance windows.
Does Fundle integrate with the POS systems used by Indian retailers?+
Yes. Fundle integrates with 50+ POS connectors to manage consent seamlessly, including Petpooja, POSist, GoFrugal, and Wondersoft, as well as the proprietary billing systems used by major enterprise retail chains. Each integration passes the customer's consent state as a first-class field in the transaction payload.
How is consent managed in a multi-brand mall environment?+
Fundle Mall Loyalty enforces brand-level data access controls based on each customer's consent state. A customer who has consented to offers from a specific tenant category but not to cross-brand profiling will have their data access scoped accordingly. Mall operators can configure consent rules at the property level, the brand level, and the campaign level.
What languages does the Fundle consent UI support?+
The Fundle AI Platform's consent flows and consent centre support multilingual delivery, including Hindi, Tamil, Telugu, Kannada, Bengali, and other major Indian languages. This is a compliance requirement under the DPDP Act's plain language standard, not merely a UX feature.
How does Fundle compare with other loyalty platforms on DPDP compliance?+
Legacy platforms like Capillary, EasyRewardz, and Antavo were architected before DPDP became law and have added compliance features as modules. Fundle was built consent-first: the customer's consent state is the foundational data contract, not a layer on top of an existing points engine. This architectural difference means consent enforcement is automatic and systemic rather than dependent on manual campaign governance.
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.