Tracking 12,400 SKUs Across Zepto, Blinkit & Swiggy Instamart in 8 Cities for a Leading FMCG Brand

Introduction

How a top-5 personal care company turned quick commerce visibility into a 3.1x share-of-shelf gain in 90 days

Actowiz Solutions  |  Case Study  |  Industry: FMCG / Personal Care

Client Snapshot

The client is a multi-category personal care and home care FMCG brand with annual revenues above 4,500 crore INR, present across 1.2 million retail outlets nationally. As quick commerce shifted from a long-tail novelty to a meaningful sales channel — crossing 12 percent of urban modern-trade equivalent volume — the client's category teams realized their existing modern-trade audit infrastructure was blind to the channel.

Brand name and exact SKUs are anonymized at the client's request.

The Business Challenge

The client operated across six product categories with more than 12,000 active SKUs. Quick commerce coverage was managed by three different distributor partners with weekly Excel-based reporting. The category leadership team faced four compounding problems:

• Pricing on Zepto and Blinkit was drifting below MAP in specific Bengaluru and Mumbai dark stores, with no early-warning visibility.
• A competitor had quietly secured exclusive listings for two key SKUs on Swiggy Instamart in three cities, eroding the client's share-of-shelf before any audit picked it up.
• Out-of-stock rates were estimated at "under 5 percent" by distributors but were actually closer to 18 percent during peak demand windows on weekends.
• Trade promotion ROI could not be measured because uplift was confounded by simultaneous platform-funded discounts that were invisible to the brand.

"We were running a 600 crore quick commerce business with the dashboards of a 60 crore one." — Category Head, Personal Care Division

Solution Architecture

1. Distributed pin-code-anchored scrapers

Actowiz deployed a fleet of geo-anchored crawlers running residential IP rotation across the 1,840 target pin codes. Each crawler programmatically set the delivery address before requesting product pages, ensuring the captured price and assortment reflected the actual dark store serving that pin code.

2. Hourly price and stock tracking

Price-sensitive SKUs were polled every 60 minutes. A change-detection layer pushed events into a Kafka stream so that any deviation greater than 3 percent from the brand's MAP, or any stockout flag flip, triggered an alert to the category and sales operations teams in under 15 minutes.

3. Competitor and assortment intelligence

A parallel pipeline tracked competitor SKUs in the same categories, computing share-of-shelf, share-of-search (top-10 positions for category keywords), and competitor promo intensity by city and platform.

4. Unified dashboard and BI integration

Data was delivered both as a hosted Actowiz dashboard for category managers and as a daily Parquet drop into the client's Snowflake warehouse, where it joined sell-out, distribution, and trade-spend data.

Results After 90 Days

• Share of shelf in the personal care category lifted from 11.4 percent to 35.1 percent across tracked dark stores — a 3.1x improvement — driven by faster listing-gap closure and better placement negotiation backed by data.
• MAP violations identified and resolved within hours instead of weeks. Average price-leak duration dropped from an estimated 9 days to under 4 hours.
• True out-of-stock rate measured at 14.2 percent against the distributor-reported 4.8 percent. Distributor SLAs were renegotiated using the Actowiz dataset as a single source of truth.
• Two competitor exclusivity deals on Swiggy Instamart were detected in week 5 and countered with platform-direct co-investment within 11 days.
• Trade promotion ROI measurement became possible for the first time. Three out of nine running promotions were paused or restructured based on net-uplift analysis after stripping platform-funded discounts.
• The category team estimated incremental quick commerce revenue contribution from the program at 38 to 46 crore INR in the first 90 days.

"Actowiz did not just give us data. They gave us a 90-day swing in our quick commerce P&L." — VP, Channel Strategy

Why It Worked

• Pin-code-level fidelity. City-level data would have averaged away the very dark-store anomalies that drove the highest commercial impact.
• Sub-hourly freshness on the right fields. Refresh cadence was tuned per field type — hourly on price and stock, daily on assortment, weekly on reviews — keeping costs in check while protecting the decisions that mattered.
• Operational integration, not just dashboards. Alerts were wired into the client's existing sales operations workflows. Data the team did not act on would have produced no result.