Dark Store Availability Data Scraping: India FMCG Report 2026

Author : Actowiz Metrics | Published On : 04 Nov 2026

Executive summary
On quick-commerce apps, an out-of-stock product is invisible. It drops down search results, disappears from collections, and the shopper buys a competitor's product instead, often in seconds. For FMCG brands, on-shelf availability in dark stores has become one of the most important drivers of quick-commerce sales.
In 2026 Actowiz Metrics received many availability requests: a brand needing "daily dark store availability data," a client wanting pincode-wise stock on Zepto and Instamart, another asking for real-time stock for many items across Flipkart Minutes stores, and an investment firm interested in digital shelf analytics across quick commerce. This report uses Dark Store Availability Data Scraping to measure in-stock rates for top FMCG SKUs across six Indian metros.
Key illustrative findings:
The average in-stock rate for top FMCG SKUs is 87%, meaning about one in eight checks finds the product unavailable.
Evening availability is 6 to 9 points lower than morning availability across platforms.
Chilled and fresh categories show the lowest availability, especially dairy and bakery.
The bottom 10% of pincodes have availability below 70%, often in newer or fast-growing areas.
Availability gaps between leading and challenger brands in the same category reach 10 to 15 points.
Why availability matters on quick commerce
No substitute shelf. Shoppers do not wait; they choose a different brand.
Search ranking. In-stock products typically rank higher than unavailable ones.
Lost sales are hidden. Brands see what sold, not what could have sold.
Local variation. National averages hide dark stores that are frequently empty.
Methodology
Cities and pincodes
Six metros: Mumbai, Delhi NCR, Bengaluru, Hyderabad, Pune and Chennai. In each city, 50 pincodes were selected to represent different dark stores and neighbourhood types, for 300 pincodes in total.
Platforms
Blinkit, Zepto and Swiggy Instamart, with Flipkart Minutes and BigBasket's quick-delivery service tracked in a subset of pincodes.
SKUs
The top 400 FMCG SKUs across eight categories, selected by search rank and bestseller presence:
The Dairy and Eggs category will include 60 SKUs.
The Bakery category will include 30 SKUs.
The Snacks category will include 70 SKUs.
The Beverages category will include 60 SKUs.
The Staples category (Atta, Rice, Dal, and Oil) will include 50 SKUs.
The Personal Care category will include 50 SKUs.
The Home Care category will include 50 SKUs.
The Baby Care category will include 30 SKUs.
Collection
Dark store availability data scraping checked each SKU in each pincode on each platform at three time slots (8 am, 1 pm and 8 pm) daily for four weeks. Each check recorded listed or not listed, in stock or out of stock, low-stock messages, price and delivery promise.
Definitions
In-stock rate: share of checks where a listed SKU was available to order.
Listing rate: share of pincodes where the SKU was listed at all.
Effective availability: listing rate × in-stock rate, the share of all pincode checks where a shopper could buy the SKU.
Key findings
1. In-stock rate by platform
Platform A has an in-stock rate of 89%, a listing rate of 94%, and an effective availability of 84%.
Platform B has an in-stock rate of 86%, a listing rate of 91%, and an effective availability of 78%.
Platform C has an in-stock rate of 87%, a listing rate of 92%, and an effective availability of 80%.
All values illustrative; platforms anonymised.
Differences between platforms are small on average but larger in specific categories and cities.
2. By category
Staples has an overall in-stock rate of 93% and an evening in-stock rate of 90%.
Home Care has an overall in-stock rate of 92% and an evening in-stock rate of 90%.
Personal Care has an overall in-stock rate of 91% and an evening in-stock rate of 89%.
Snacks has an overall in-stock rate of 89% and an evening in-stock rate of 84%.
Beverages has an overall in-stock rate of 88% and an evening in-stock rate of 81%.
Baby Care has an overall in-stock rate of 87% and an evening in-stock rate of 84%.
Bakery has an overall in-stock rate of 80% and an evening in-stock rate of 69%.
Dairy and Eggs has an overall in-stock rate of 79% and an evening in-stock rate of 68%.
All values illustrative.
Fresh and chilled categories have the lowest availability, and the evening drop is steepest for dairy and bakery. Beverages also fall sharply in the evening, particularly in warm months.
3. By city
Bengaluru has an in-stock rate of 89%, with 6% of pincodes recording an in-stock rate below 75%.
Mumbai has an in-stock rate of 88%, with 8% of pincodes recording an in-stock rate below 75%.
Delhi NCR has an in-stock rate of 86%, with 12% of pincodes recording an in-stock rate below 75%.
Pune has an in-stock rate of 87%, with 9% of pincodes recording an in-stock rate below 75%.
Hyderabad has an in-stock rate of 86%, with 11% of pincodes recording an in-stock rate below 75%.
Chennai has an in-stock rate of 85%, with 13% of pincodes recording an in-stock rate below 75%.
All values illustrative.
4. By time of day
8 AM: The overall in-stock rate across all categories is 91%.
1 PM: The overall in-stock rate across all categories is 88%.
8 PM: The overall in-stock rate across all categories is 83%.
All values illustrative.
Evening is when quick-commerce demand peaks for many categories, and it is also when availability is lowest. That is where lost sales concentrate.
5. Pincode spread
The worst-performing 10% of pincodes had in-stock rates below 70%. These were often in newer residential areas served by recently opened dark stores, where demand forecasting is less mature, or in very high-demand central areas where stock runs out quickly.
6. Leaders vs challengers
Curd: The leading brand has an in-stock rate of 88%, compared to 74% for the challenger brand, resulting in a 14-percentage-point gap.
Packaged Bread: The leading brand has an in-stock rate of 85%, compared to 72% for the challenger brand, resulting in a 13-percentage-point gap.
Energy Drinks: The leading brand has an in-stock rate of 92%, compared to 81% for the challenger brand, resulting in an 11-percentage-point gap.
Chips: The leading brand has an in-stock rate of 94%, compared to 86% for the challenger brand, resulting in an 8-percentage-point gap.
All values illustrative.
Leading brands generally achieve better availability, often through stronger distributor service and closer forecasting with platforms. For challengers, closing the availability gap may be cheaper than winning share through discounts.
7. Availability and search rank
In the sample, SKUs that were in stock ranked on average 3 to 5 positions higher for their main keyword than the same SKUs when out of stock. Availability directly affects visibility.
8. Day-of-week pattern
Monday: The overall in-stock rate across all categories is 88%, while dairy and bakery record 81%.
Wednesday: The overall in-stock rate across all categories is 88%, while dairy and bakery record 80%.
Friday: The overall in-stock rate across all categories is 86%, while dairy and bakery record 76%.
Saturday: The overall in-stock rate across all categories is 84%, while dairy and bakery record 72%.
Sunday: The overall in-stock rate across all categories is 85%, while dairy and bakery record 73%.
All values illustrative.
Weekends show lower availability, particularly for fresh categories, as household orders rise and replenishment schedules do not always keep pace.
9. Listing gaps
Availability is not only about stock-outs. In the sample, about 8% of top SKUs were not listed at all in a given pincode, even though they were listed in neighbouring pincodes. These listing gaps are often caused by catalogue set-up in individual dark stores, and they are usually quicker to fix than supply problems. Dark store availability data scraping distinguishes "not listed" from "out of stock" so brands can raise the right issue with the right team.
Out of Stock (Listed): Accounts for 62% of unavailable checks. The typical fixes include improving replenishment processes and demand forecasting.
Not Listed in This Dark Store: Accounts for 31% of unavailable checks. The typical fix is catalogue activation in coordination with the platform.
Listed but Not Deliverable: Accounts for 7% of unavailable checks. The typical fix involves resolving delivery-zone or serviceability issues.
All values illustrative.

 Estimating lost sales
Availability data can be combined with a brand's own sales data to estimate lost sales. A simple approach:
Calculate average sales per in-stock hour for a SKU in a pincode.
Count the hours the SKU was out of stock in that pincode.
Multiply to estimate sales lost, and add up by city and platform.
Curd 400 g: Generates weekly sales of 12,000 units when in stock, with an out-of-stock share of 22%, resulting in an estimated loss of 2,640 units per week.
Paneer 200 g: Generates weekly sales of 6,500 units when in stock, with an out-of-stock share of 28%, resulting in an estimated loss of 1,820 units per week.
Toned Milk 500 ml: Generates weekly sales of 40,000 units when in stock, with an out-of-stock share of 9%, resulting in an estimated loss of 3,600 units per week.
All values illustrative. This simple method assumes sales would have continued at the in-stock rate; brands should adjust for substitution and demand timing.
Recommendations for brands
Track availability by pincode and time slot, not just daily national averages.
Focus on evenings, when demand is high and stock is lowest.
Prioritise fresh and chilled categories, where stock-outs are most frequent.
Share data with distributors and platforms to target replenishment at problem dark stores.
Monitor the link between availability and search rank to understand the full impact of stock-outs.
Limitations
Availability reflects what the app shows for a pincode at the time of the check.
Platforms may assign different dark stores to the same pincode at different times.
The SKU set covers top products and may not represent long-tail SKUs.
Lost-sales estimates require the brand's own sales data and simplifying assumptions.
Frequently asked questions
How often can dark store availability data scraping run?
From daily to several times per hour for priority SKUs and pincodes.
Can we track our full range?
Yes. Most brands track their full quick-commerce range plus key competitor SKUs.
Which platforms are covered?
Blinkit, Zepto and Swiggy Instamart, plus Flipkart Minutes, BigBasket and other services on request.
Is any shopper data collected?
No. Actowiz Metrics collects public product availability information only.
Conclusion
In quick commerce, availability is visibility, and visibility is sales. Dark store availability data scraping shows that even top FMCG SKUs are unavailable in a meaningful share of checks, with the biggest gaps in the evening, in fresh categories and in specific pincodes. Brands that measure availability at this level can target the gaps that matter most.
Get your availability report from Actowiz Metrics
Actowiz Metrics delivers dark store availability data scraping, share-of-search and price tracking for FMCG brands across India's quick-commerce platforms. Book a 15-minute demo to see your availability by pincode and time slot.
Source : https://www.actowizmetrics.com/quick-commerce-search-rank-scraping.php
Original: https://www.actowizmetrics.com

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