How to Find Quick Commerce Best Selling Products on Blinkit and Instamart Without Sales Data (2026)

Author : FoodData Scrape | Published On : 28 Oct 2026

Introduction

Brands and analysts keep asking the same question: which quick commerce best selling products are winning on Blinkit and Swiggy Instamart, in my city, this month? The honest starting point is that neither app publishes units sold for any product.

Short answer: You cannot see real sales of quick commerce best selling products, but you can rank them reliably with public demand proxies. Track search rank for category keywords, listing position, “bestseller” tags, rating counts, stock-out and restock speed by pincode, discount depth, and assortment breadth. Combine them into a weighted demand score.

This guide explains each proxy, its limits, a sample scoring method, a city and pincode sampling plan, and the compliance rules to follow. It covers food and grocery only: staples, snacks, beverages, dairy, frozen and fresh.

Why quick commerce best selling products matter in 2026

Quick commerce is no longer a side channel for packaged food. Redseer estimates India’s quick commerce GMV at about $13–14 billion in FY26, growing around 120% year on year, and says quick commerce now makes up roughly 70% of online grocery (Redseer, May 2026).

The store networks behind that growth are large and local:

Blinkit: 2,443 stores at the end of the June 2026 quarter, with 200 net stores added in the quarter, and net order value of ₹17,132 crore, per Eternal’s Q1 FY27 shareholder letter as reported by Outlook Business.
Swiggy Instamart: 1,171 dark stores across 131 cities and gross order value of ₹7,907 crore in Q1 FY27 (Inc42; primary source: Swiggy shareholder letter).

Every one of those stores carries its own range and stock. So any list of quick commerce best selling products is a pincode-level answer, not a national one. A snack that leads in Gurugram Sector 56 may be absent in Noida.

Who uses a demand ranking?

FMCG and D2C brands checking whether their SKUs are winning or losing shelf share.
Category managers using quick commerce best selling products lists to plan range, launches and promotions by city.
Investors and analysts comparing category momentum across apps.
New entrants deciding which categories to enter first.

What the apps show, and what they never show

Blinkit, Instamart and Zepto all show price, MRP, discount, pack size, stock status and the delivery estimate for a given location. Some listings show ratings or labels such as “bestseller”, depending on app version and category.

What they do not show is units sold, revenue per SKU or sell-through rate. Any page or vendor that promises “exact sales volumes” scraped from these apps is overstating what is public. Treat such numbers as estimates and ask how they were built.

The good news: quick commerce best selling products leave visible traces. Fast sellers rank higher in search, sell out more often, restock faster and spread to more dark stores. Those traces are what you measure.

The 9 public demand proxies (and their limits)

Each signal below is a proxy for quick commerce best selling products, not a sales figure. Use several together so that no single noisy signal drives the ranking.

Price changes add context to all of these. A SKU that holds a higher price while staying near the top of search and selling out often is showing strong underlying demand.

How to build a demand score for quick commerce best selling products

A demand score turns many proxies into one comparable number per SKU, per pincode, per week, so you can rank quick commerce best selling products consistently. Here is a simple method you can adapt.

Define the shelf. Pick categories (for example, chips and namkeen, soft drinks, curd, frozen snacks) and 10–20 shopper keywords per category.
Collect daily snapshots. For each sampled pincode, record search rank, listing position, tags, rating count, price, MRP, discount and stock status at fixed times of day.
Normalise each signal to 0–100. For example, rank 1 scores 100 and rank 50 scores 0; a SKU in stock in every sampled store scores 100 on breadth.
Apply weights. Use the example weights below as a starting point, then adjust after review.
Average over time. Use a 7-day or 14-day rolling score so one-off promotions do not dominate.
Rank and flag growth. Sort by score, then compare with the previous period to find risers.

The output is a ranked list such as “top 50 SKUs by demand score, Gurugram, snacks, last 14 days”, with a growth column. Every value in that list should be labelled a demand score, never “units sold”. Sample output values

Brands that have their own sell-in or retail sales data can calibrate the weights against it. That turns the score into a stronger estimate, but it still stays an estimate.

Category notes: staples, snacks, beverages, dairy, frozen and fresh

Quick commerce best selling products look different in each category, and proxies behave differently too. Adjust weights rather than using one formula everywhere.

City and pincode sampling plan

Because every dark store is different, sampling decides how reliable your quick commerce best selling products list will be. A Gurugram-style project might look like this.

Map each pincode to the store that serves it where the app reveals this. Two nearby pincodes served by the same dark store should be counted once.

How to collect, clean and match the data

Collect from the public storefront. Set the delivery location to each sampled pincode and record what any shopper would see.
Separate sponsored results. Store an “ad” flag and calculate organic rank without them.
Match SKUs across apps. Use brand, product name, pack size and unit. The same chips pack may have different names on Blinkit and Instamart.
Normalise pack sizes. Compare price per 100 g or per litre, and treat multipacks separately.
Keep timestamps. Every row needs a capture time, pincode and platform.
Run quality checks. Flag sudden jumps, missing stores and duplicate listings before scoring.

The Blinkit, Swiggy Instamart and Zepto scraping API and the Blinkit dataset show the kind of SKU-level fields involved, such as price, MRP, discount, stock status and city.

Use cases

Top-50 lists by city: A ranked list of quick commerce best selling products, i.e. high-demand, high-growth food SKUs per category for a city like Gurugram, refreshed weekly.
Share-of-shelf tracking: How often a brand appears in the top 10 organic results for its key search terms.
Launch tracking: Whether a new flavour spreads across dark stores and climbs search rank after launch.
Promotion review: Whether discounts lift rank and breadth, or only move price.
Cross-app comparison: Where a SKU ranks on Blinkit versus Instamart in the same pincodes.

Legal and compliance basics

Public data only: Collect only what any shopper can see on the public storefront for a chosen location.
No login or OTP bypass: Do not create fake accounts, bypass OTP verification, CAPTCHAs or other security controls.
Respect terms and robots.txt: Review each platform’s terms of service and robots.txt before collecting.
Rate limits: Keep request rates low and spread over time so you do not burden the service.
No personal data: Do not collect customer, delivery partner or reviewer personal details.
Label outputs honestly: Call a list of quick commerce best selling products a demand-score ranking, not sales.

Build vs buy

Building in-house works for a few keywords in one city. It gets harder as you add pincodes, time slots, apps and SKU matching. Location handling, layout changes and ad detection need regular maintenance.

A managed provider should be able to show sample data, explain its scoring method, label proxies clearly and state its compliance approach. Ask any vendor how they define quick commerce best selling products before you buy a list.

Originally Submitted at: https://www.fooddatascrape.com/index.php

#ScrapeQuickCommerceBestSellingProductData,
#BlinkitBestSellingProductDataScraping,
#BestSellingProductIntelligence,
#QuickCommerceProductAnalytics,
#QuickCommerceProductRankingData,
#BlinkitProductListingDataExtraction,