Fundle.ai: Best Customer Engagement Platform for Indian Brands: NewU Beauty Case Study

Background: NewU Beauty's Customer Challenges

NewU Beauty's retail footprint spans chemist-adjacent beauty stores, shop-in-shop formats inside large-format retailers, and standalone outlets in malls including Select CITYWALK in Delhi and Phoenix Marketcity in Mumbai. The brand stocks over 15,000 SKUs — international prestige, homegrown D2C labels like Dot & Key and The Derma Co, and VLCC's own in-house range. Category breadth is a strength in merchandising but a nightmare in personalisation. A customer who buys a hair mask from Streax and a lip tint from Colorbar occupies a completely different intent cluster from one who cross-shops anti-ageing serums and SPF moisturisers. Legacy CRM systems cannot distinguish between them because they were built on transaction value, not category affinity.

Before the Fundle engagement, NewU operated a points-accumulation programme that awarded one point per ₹100 spent, redeemable at ₹1 per point after a 500-point threshold. Participation numbers looked acceptable on the surface — roughly 40% of billing transactions were tagged to a loyalty ID. But the quality of that participation was hollow. Only 14% of enrolled members had transacted more than once in a six-month window. The programme had no triggered communication logic: birthday messages went out as blanket SMS blasts with no offer differentiation, lapsed-customer win-back campaigns were executed quarterly rather than dynamically, and there was zero integration between in-store POS data from GoFrugal and any outbound engagement stack.

The marketing team was running what insiders called 'spray and pray' — a ₹18 lakh per month SMS spend producing click-through rates under 1.2% and offer redemption rates under 3%. The cost per incremental visit was climbing toward ₹420, eroding any margin case for the programme. The brief handed to Fundle.ai was unambiguous: build an engagement engine that knows who the customer is, predicts what they want next, reaches them on the right channel at the right moment, and does not require a data science team of ten to operate.

Adopting Fundle's AI-Native Engagement Products

The first decision NewU's marketing team made was architectural: stop treating loyalty as a points ledger and start treating it as a real-time behavioural intelligence layer. Fundle's AI Platform made this transition operationally feasible without requiring a rip-and-replace of existing POS infrastructure. The GoFrugal POS integration was live within three weeks via Fundle's pre-built connector, meaning every billing event — SKU, category, price point, store location, time-of-day — began flowing into Fundle's customer data layer in near real time.

Fundle AI Agents then went to work on segmentation. Rather than the standard RFM buckets that most platforms including EasyRewardz and Capillary default to, Fundle's agents built dynamic micro-segments based on category affinity sequences, visit-gap patterns, and channel responsiveness scores. A customer who visited twice in a fortnight and browsed skincare but only purchased haircare was automatically flagged as a 'skincare intent, haircare converter' — and entered a WhatsApp-first nurture sequence with targeted skincare content and a time-bound 12% discount on a curated skincare bundle. This kind of segment-of-one logic, previously the domain of enterprise D2C brands with dedicated data science teams, became executable by NewU's four-person marketing team through Fundle's no-code AI Workflow builder.

Fundle Agentic AI also powered the win-back engine. Customers who had not transacted in 45 days received a dynamically generated 'we miss you' message calibrated to their last category purchase — not a generic voucher, but a contextually relevant nudge. A customer whose last purchase was a Lakme foundation received a message highlighting a new arrival in complexion care; a customer who last bought a hair oil from Indulekha received content anchored to a seasonal haircare routine. Offer values were dynamically tiered: customers with a 60-day lapse gap received a ₹75 off on ₹499 coupon; those lapsed 90+ days received ₹150 off on ₹799 to compensate for the deeper intent gap.

Fundle Mall Loyalty capabilities added a geographic intelligence layer. NewU outlets inside malls like Nexus Seawoods and Ambience Mall could see footfall correlation data, enabling campaigns to fire precisely when a customer was identified as being within the mall's geo-fence — converting a passive mall visit into a targeted store visit with a time-sensitive offer valid for the next 90 minutes. This kind of proximity-aware engagement is simply unavailable in tools like MoEngage or WebEngage without significant custom engineering work.

Results: Increased Repeat Purchases and Customer Lifetime Value

Twelve weeks after full platform go-live, the numbers told a clear story. Repeat purchase rate among enrolled loyalty members climbed from 14% to 31% — a 121% improvement in the metric that most directly predicts CLV. NewU Beauty scaled repeat customer engagement by 30% using Fundle's AI-powered loyalty and data platform, a figure that translated into an incremental ₹2.4 crore in annualised revenue from the base of stores in the initial rollout cohort. Average order value among members who received AI-personalised outreach was 18% higher than those on the legacy broadcast cadence — driven primarily by cross-category uptake in skincare and wellness, categories with structurally higher margins than colour cosmetics.

The win-back campaigns delivered a cost-per-reactivation of ₹94 — versus ₹420 under the previous broadcast model — because the spend was concentrated on high-propensity segments rather than sprayed across the entire lapsed base. The WhatsApp channel outperformed SMS by 4.3x on click-through and 6.1x on conversion, validating the channel-responsiveness scoring that Fundle AI Agents computed per customer. Crucially, this improvement did not require additional headcount: the same four-person marketing team managed three times the campaign volume through Fundle's AI Workflow automation.

From a CLV perspective, the shift was structural. Before Fundle, the top 20% of customers by spend accounted for 68% of revenue — a concentration risk common in Indian beauty retail. After 12 weeks, the top 20% still led but the mid-tier (customers ranked 20th to 50th percentile by spend) grew their share of revenue from 19% to 26%. This is the CLV expansion that matters: not squeezing more from existing high spenders but activating the latent value in the middle cohort. Manyavar and Tanishq have demonstrated this playbook in occasion-wear and jewellery; NewU's data shows it is equally applicable in everyday beauty retail.

Store-level NPS improved by 11 points over the same period, driven largely by the perception of relevance — customers noted in post-visit surveys that offers felt 'meant for me' rather than generic. This soft metric has hard consequences: an 11-point NPS improvement in Indian retail contexts correlates with a 6-8% reduction in acquisition cost through word-of-mouth referrals, based on benchmarks from Apollo Pharmacy's loyalty programme data.

Ensuring Data Privacy Compliance Throughout

India's Digital Personal Data Protection Act (DPDP) 2023 fundamentally changes the compliance calculus for retail loyalty programmes. The days of purchasing third-party data lists, running opted-out customers on broadcast SMS, or storing customer data in unstructured CRM fields without a clear consent trail are numbered. For a brand like NewU Beauty, which operates across 300+ stores with multiple franchise operators and collects customer data via in-store staff-assisted enrolment, ensuring a clean, auditable consent chain was one of the most operationally complex requirements of the entire platform transition.

Fundle's architecture addressed this by embedding a consent management layer directly into the customer data platform — not bolted on as an afterthought. Every customer record in Fundle carries a consent timestamp, the channel through which consent was collected, the specific data processing purposes consented to, and an expiry flag if consent needs renewal. Campaign audiences are automatically filtered against this consent layer before any message is sent. A customer who consented to promotional communications but not to data sharing with third-party brands is automatically excluded from co-branded offers — without any manual intervention from the marketing team.

This matters commercially, not just legally. In the NewU deployment, the consent-led audience segment — customers who had explicitly opted into personalised recommendations — showed a 2.4x higher offer redemption rate than the broad loyalty base. This is consistent with what Reliance Trends and Lifestyle have observed in their own loyalty data: customers who knowingly share preferences engage more deeply because the reciprocity loop is intact. Privacy compliance, done well, is a conversion driver.

For mall operators who aggregate loyalty data across multiple anchor and inline brands — the model that Select CITYWALK and Phoenix Marketcity are increasingly pursuing — DPDP compliance becomes even more critical. Sharing customer engagement data across brand boundaries requires explicit, granular consent. Platforms that cannot enforce this at the data layer create legal exposure for every brand in the mall ecosystem. Fundle Mall Loyalty was designed with this multi-brand consent architecture from the ground up, making it the only platform in the current Indian market that can operate a mall-wide loyalty programme under DPDP without requiring custom legal engineering for every brand partnership.

How Fundle solves this

The NewU Beauty case study is not an isolated success story — it is a blueprint that Vineet Narang's vision for Fundle has been architecting toward since the company's founding: build the best customer engagement platform for Indian brands by combining AI-native intelligence, first-party data discipline, and DPDP-ready infrastructure into a single, operator-friendly platform.

The Fundle AI Platform sits at the intersection of four capabilities that no single competitor currently combines in the Indian market. First, Fundle Loyalty — the core points, tiers, and rewards engine — is built to handle the complexity of Indian retail: multi-store, multi-format, franchise-operated networks where a single customer might transact at a standalone NewU store in Jaipur and a mall-embedded outlet in Gurugram. Points, tier status, and consent records travel with the customer ID across every touchpoint. Second, Fundle Brand Loyalty extends this engine to multi-brand operators who want to run a unified loyalty currency across, say, a beauty chain, a pharmacy, and a café — the kind of adjacent category bundling that malls and large retail groups are actively pursuing to increase visit frequency and basket value.

Third, Fundle AI Agents handle the intelligence layer: real-time behavioural scoring, next-best-action prediction, channel affinity optimisation, and dynamic offer calibration. These agents operate autonomously within guardrails defined by the marketing team — a no-code AI Workflow builder means a four-person team can manage campaign logic that previously required a data science function of ten. Competing platforms like Capillary, Antavo, and EasyRewardz offer segmentation and campaign orchestration, but none natively combine agentic AI with a mall-aware geo-intelligence layer. Fundle Agentic AI goes further: it does not wait for a marketer to trigger a campaign. It monitors behavioural signals continuously and fires engagement actions when the model determines the moment of highest conversion probability has arrived.

Fourth, Fundle Mall Loyalty addresses a white space that generic marketing automation tools like MoEngage, WebEngage, and Xeno cannot fill: the operational complexity of mall-level loyalty, where a CMO needs to see engagement metrics not just by brand but by zone, anchor tenant versus inline brand, weekday versus weekend footfall, and event-driven traffic spikes. For mall operators managing 80-120 brands across a single property, this kind of aggregated yet brand-partitioned view is essential — and it is built into Fundle's dashboard without custom development. The result, as NewU Beauty's numbers demonstrate, is an engagement platform that moves the needle on the metrics that actually matter: repeat purchase rate, CLV expansion, and cost-per-reactivation.