Fundle.ai: The Future of Customer Engagement Software for Retail in India

Emerging Technologies Shaping Retail Engagement

Five technology vectors are converging to redefine what customer engagement software for retail can actually do. Understanding each is essential for any retail CMO or loyalty programme manager making budget decisions in FY25-26.

First, generative AI and large language models have moved from experiment to production. Platforms can now generate personalised offer copy, WhatsApp nudges, and reactivation messages at the individual customer level — not the segment level. A Pantaloons customer who buys kidswear every August for back-to-school season should receive a categorically different message than the same customer who last purchased in December for a wedding. This is no longer aspirational; it is table-stakes for any serious engagement platform.

Second, real-time event streaming — powered by infrastructure like Apache Kafka or managed equivalents — means that a POS transaction at a Reliance Trends store in a mall can trigger a loyalty point credit, a personalised cross-sell recommendation, and a survey ping within 90 seconds. Indian retailers running GoFrugal or Petpooja at the store level are already generating this data. The bottleneck has never been data generation; it has been data activation.

Third, computer vision and footfall analytics are entering the loyalty equation. Malls like Select CITYWALK and Phoenix Marketcity are piloting anonymised footfall-to-loyalty-ID matching at entry gates, which allows the engagement platform to know a loyalty member has entered the mall before they make their first transaction. This converts the entire mall visit into a context window, not just the moments around a billing event.

Fourth, conversational AI — specifically WhatsApp-native AI agents — is replacing the traditional push notification as the primary engagement channel. India has 550 million WhatsApp users. A well-designed AI agent can resolve a points balance query, push a personalised offer based on purchase history, and collect explicit consent for a new campaign category, all within a single thread. The open rates for well-timed WhatsApp messages exceed 85% in Indian retail, compared to 18-22% for email. Fifth, agentic AI workflows — where AI models can autonomously plan, execute, and optimise multi-step engagement journeys without human intervention at each step — represent the next frontier. A retail engagement platform that uses agentic AI can, for example, automatically identify a high-value customer who has not visited in 45 days, select the optimal reactivation channel, craft a personalised incentive based on their RFM score, and dispatch the communication — all without a marketing manager lifting a finger.

Role of AI, Automation, and Real-Time Data in Customer Engagement

The phrase 'AI-powered customer engagement platform' has been diluted by overuse. Every legacy loyalty vendor from Capillary to EasyRewardz now has an AI badge on their website. The meaningful distinction is not whether AI is present in the platform — it is where AI sits in the operational architecture and how much of the engagement workflow it can own end-to-end.

In the legacy model, AI is a reporting layer. It tells the marketing manager which segment to target after the fact. The manager then manually builds a campaign in a journey builder, approves the copy, sets the send time, and waits two weeks for a performance report. This is AI as a dashboard accessory, not AI as an operator. The result is campaigns that are 12-18 days behind the actual consumer signal they are responding to.

In the emerging model — which platforms like Fundle AI Agents are beginning to operationalise — AI is the orchestration layer. It ingests real-time transaction streams from POS systems (POSist, GoFrugal, Wondersoft), app events, and third-party signals like weather data and local events. It continuously scores every loyalty member on RFM dimensions — Recency, Frequency, Monetary — and updates those scores after every interaction, not once a week. It then selects the right channel (WhatsApp, in-app push, SMS, email, store associate alert), the right message variant, and the right incentive level — and dispatches the engagement autonomously.

Automation in this context is not about sending more messages; it is about sending fewer, better-timed messages that produce measurable outcomes. Indian consumers are acutely sensitive to communication fatigue. A study of tier-1 mall loyalty programmes found that customers who received more than 12 push notifications per month showed a 34% higher opt-out rate than those who received 4-6 highly personalised communications. Real-time data is the input that makes restraint intelligent: the system knows not to push a coffee offer to a Cafe Coffee Day loyalist who just made a purchase 20 minutes ago. It also knows that a customer browsing FabIndia's ethnic section during Navratri week is a contextually perfect candidate for a Tanishq ethnic jewellery offer — if those two brands share data within a common mall loyalty framework. This cross-brand intelligence is exactly the kind of value that a single-brand engagement platform can never produce but a mall-level loyalty infrastructure — with proper consent architecture — can.

Growing Importance of Data Privacy and Consent in India

The Digital Personal Data Protection Act 2023 is not a compliance checkbox. It is a structural shift in how Indian retailers must think about their relationship with consumer data — and the loyalty and engagement platforms that process it. The Act mandates explicit, informed, purpose-specific consent before any personal data is collected or processed. It grants Indian consumers the right to withdraw consent at any time, the right to access their data, and the right to grievance redressal. For a mall loyalty programme that runs marketing campaigns on behalf of 150 tenant brands, the consent architecture implications are significant.

Most existing loyalty programmes in India today operate on a single omnibus consent captured at the time of enrolment — a checkbox buried in a 4,000-word terms-and-conditions document. Under DPDP, this is legally inadequate. Consent must be specific to the purpose of processing. If a mall wants to share a customer's fashion purchase history with a jewellery brand tenant for cross-selling, that is a separate processing purpose that requires a separate, explicit consent signal. If a brand wants to use transaction data to train a recommendation model, that requires disclosure and consent for that specific use.

For loyalty programme managers, this is not just a legal risk — it is a strategic opportunity. Brands that build trust-first consent experiences will achieve higher opt-in rates, higher data quality, and higher engagement response rates. Indian consumers, as noted in the KPMG 2023 survey, are willing to share data when the value exchange is transparent and the control is real. Apollo Pharmacy's app, for example, has seen materially higher prescription data opt-in rates when they clearly articulate what the data enables — personalised health reminders, automatic refill alerts — rather than using vague language about 'improving your experience'.

The practical implication for platforms is significant. Customer engagement software for retail in India must now ship with a native consent management layer — not a bolt-on integration with a third-party cookie banner tool. Consent must be captured, stored, versioned, and auditable. When a customer withdraws consent for a specific category — say, behavioural profiling for cross-brand targeting — the platform must enforce that withdrawal across all downstream processing pipelines in real time. Platforms that cannot do this will represent active legal liability for their retail operator clients. The DPDP penalty framework includes fines of up to ₹250 crore for significant breaches — a number that concentrates minds in any retail boardroom.

Industry Predictions and KPIs for the Indian Retail Engagement Market

The next three years in Indian retail engagement will be defined by consolidation, capability leap, and competitive separation. Here are the projections that retail operators should be tracking.

By FY27, the Indian retail loyalty and engagement software market is projected to reach ₹4,800 crore, growing at a CAGR of 22% from FY24 levels. The growth will not be uniform: AI-native platforms will take disproportionate share from legacy rule-based systems. Vendors who cannot demonstrate real-time personalisation capability at scale — and a credible DPDP compliance architecture — will face accelerating customer churn from mid-market and enterprise retail accounts.

Mall operators specifically will face pressure from two directions: the increasing sophistication of direct-to-consumer apps from anchor brands (Lifestyle, Pantaloons, and Reliance Trends all have their own engagement programmes), and the expectation from consumers that the mall-level programme should offer experiences neither brand can provide individually. The mall CMO's winning answer is a cross-brand intelligence layer that individual brands simply cannot replicate — and that requires a mall-grade customer engagement platform India that can aggregate and anonymise cross-brand data with explicit consumer consent.

Key KPIs that will separate leading programmes from laggards in FY25-27: Loyalty ID capture rate at POS should cross 65% for tier-1 mall programmes. Repeat visit rate — defined as a loyalty member returning within 30 days of a qualifying transaction — should reach 38-45% for well-optimised programmes, up from the current industry average of 24%. Redemption rate should exceed 55%, compared to the current average of below 35%. And Customer Lifetime Value for programme members should be demonstrably 3.5-5× that of non-members — a metric that directly funds the business case for investment in premium engagement infrastructure.

The brands that will win are those that treat customer engagement software for retail not as a marketing cost centre but as a revenue intelligence function — one that generates compounding returns as data accumulates and AI models improve. The window to build that data moat is now. Every month of operating on a legacy batch-campaign system is a month of engagement data that is not being captured in a form that feeds an AI model.

How Fundle solves this

Fundle was built from the ground up as an AI-first customer engagement software for retail — not a CRM with an AI module bolted on, and not a points engine with a journey builder added as an afterthought. The Fundle AI Platform integrates directly with India's leading POS systems — POSist, GoFrugal, Wondersoft, Petpooja — to capture transaction events in real time and resolve them to a unified customer identity profile within seconds of a billing event. This is the data foundation that makes everything else possible.

Fundle Loyalty powers both mall-level and brand-level programmes through two distinct but integrated product lines. Fundle Mall Loyalty gives mall operators — from Phoenix Marketcity to mid-size regional malls — a cross-brand engagement infrastructure where a customer's interaction with any tenant brand enriches the shared loyalty profile, enabling cross-category personalisation that no single brand's programme can achieve. Fundle Brand Loyalty gives individual retail brands — whether an ethnic wear chain running 300 stores or a pharmacy network with 1,200 outlets — a standalone AI-powered engagement stack that scales from campaign creation to redemption tracking without manual intervention.

The intelligence layer is where the Fundle AI Platform differentiates most sharply from legacy alternatives like Capillary, EasyRewardz, or even newer players like Xeno and Customer Capital. Fundle AI Agents are purpose-built for retail engagement use cases: a lapsed-customer reactivation agent that autonomously identifies at-risk high-value customers, selects the optimal channel, generates a personalised offer, dispatches the communication, and reports the outcome — without a marketing manager needing to configure each step. Fundle Agentic AI goes further: it allows multi-step, goal-directed workflows where the AI can adaptively choose between different engagement strategies based on real-time customer response signals. And Fundle AI Workflow gives marketing operations teams a no-code interface to design, test, and deploy these agentic journeys without engineering dependency.

The consent and compliance layer in Fundle is not an integration — it is native to the platform architecture. Every data point collected, every consent signal given or withdrawn, every processing purpose is logged in a versioned audit trail that satisfies DPDP 2023 requirements. When a customer opts out of cross-brand targeting through a WhatsApp interaction with the Fundle AI Agent, that withdrawal propagates across all downstream audience segments and campaign queues in real time — not at the next batch sync. Vineet Narang's founding vision for Fundle was that the platform would make Indian retailers genuinely competitive on the consumer trust dimension, not just the points-and-discounts dimension. Fundle today engages over 1.33 crore Indian consumers — a network that generates the data density required for AI models to produce meaningfully personalised outcomes at scale. For retail operators preparing for the next three years, Fundle is the platform that is already living in the future they are planning for.