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Business Problem
The client faced challenges across three dimensions:
Delivery Time Fluctuations
Same products were delivered in: - 10–12 minutes in some pincodes - 25–40 minutes in others. This created unpredictable customer experience, higher drop-offs and inconsistent conversions.
No Visibility Into Dark-Store Behaviour
Hundreds of dark stores operate with different capacities, rider availability and demand pressure. The brand had no way to track store-level delays or serviceability.
Peak-Hour Variations
Between 6 PM – 10 PM, delays surged 40–120% depending on region and platform. The brand couldn’t track pattern shifts or prepare inventory accordingly.
Food Data Scrape’s Solution
Food Data Scrape deployed a high-frequency hyperlocal monitoring system consisting of:
- Real-Time Crawlers: Capturing live ETA, slot availability, platform speed, zone serviceability, surge and peak delays.
- Dark Store Mapping Engine: Mapped delivery zones, service areas, store-level delays and stability signals.
- Pincode Intelligence: Created heatmaps showing fastest and slowest pincodes.
- Multi-Platform Benchmarking: Compared performance across Zepto, Instamart and Blinkit.
- Dashboards + API: Delivered live insights to operations, marketing and supply-chain teams.
Sample Data — Dark Store Level
| Dark Store ID | City | Pincode | ETA (Minutes) | Slot Availability | Peak-Hour Delay | Status |
|---|---|---|---|---|---|---|
| STM-118 | Bengaluru – Indiranagar | 560038 | 11 | Available | +4 min | Stable |
| STM-205 | Bengaluru – Whitefield | 560066 | 22 | Limited | +11 min | Unstable |
| STM-321 | Delhi – Lajpat Nagar | 110024 | 9 | Available | +2 min | Fast |
| STM-442 | Mumbai – Andheri East | 400059 | 27 | Unavailable | +15 min | Delayed |
| STM-501 | Pune – Wakad | 411057 | 14 | Available | +6 min | Stable |
Sample Data — Pincode Comparison
| City | Pincode | Zepto ETA | Instamart ETA | Blinkit ETA | Fastest Platform |
|---|---|---|---|---|---|
| Mumbai | 400053 | 13 min | 17 min | 11 min | Blinkit |
| Bengaluru | 560034 | 12 min | 10 min | 16 min | Instamart |
| Delhi | 110049 | 15 min | 18 min | 14 min | Blinkit |
| Pune | 411001 | 20 min | 13 min | 17 min | Instamart |
| Chennai | 600020 | 19 min | 22 min | 16 min | Blinkit |
Sample Data — Peak Hour Delay
| City | Platform | Avg ETA (Normal) | Avg ETA (Peak) | Delay % |
|---|---|---|---|---|
| Mumbai | Blinkit | 12 min | 24 min | +100% |
| Bengaluru | Instamart | 10 min | 17 min | +70% |
| Delhi | Zepto | 14 min | 26 min | +85% |
| Hyderabad | Instamart | 15 min | 30 min | +100% |
| Pune | Zepto | 17 min | 33 min | +94% |
Key Insights Delivered
- Dark Store Capacity Shapes Speed: Low-capacity stores triggered 2–3x delays.
- Delivery Performance is Hyper-Local: Neighbouring pincodes behaved differently in delays and slot availability.
- Blinkit Faster in Dense Markets: Blinkit dominated central zones of Delhi, Mumbai and Gurugram.
- Instamart Strong in Tier-1/2 Mix: Especially effective in outskirts of Bengaluru, Pune, Jaipur.
- Peak-Hour Delays are Predictable: Patterns helped the brand plan inventory and promotions.
Business Impact
- 27% Higher Conversion Rate: Triggered by better ad targeting and stable delivery zones.
- 40% Drop in ETA-Related Complaints: Customer experience became more predictable.
- Better Supply-Chain Planning: Stock allocated strategically to high-speed zones.
- Improved Geo-Targeted Marketing: Ads focused on faster delivery regions.
- Predictive Forecasting: Early-delay signals improved operational readiness.
Why Hyperlocal ETA Intelligence Works
Fast delivery equals competitive advantage. Accurate, predictable ETA improves customer trust and loyalty while optimizing campaign efficiency.
Why Brands Choose Food Data Scrape
- Real-time hyperlocal extraction
- Pincode-level mapping
- 99% accurate datasets
- Quick-commerce focused engineering
- API + dashboard support
- Scalable across cities and stores
The Client
A fast-growing FMCG brand dependent on hyperlocal sales needed visibility into ETA behaviour, dark-store delays and pincode speed variations. They relied on Food Data Scrape to bring transparency into delivery performance.
Advantages of Collecting Data Using Food Data Scrape
- Real-time ETAs
- Dark-store performance mapping
- Purpose-built quick-commerce scrapers
- Pincode micro-intelligence
- Multi-platform speed benchmarking
- Predictive delay alerts
- API + dashboard integration
- High scalability across regions
Client’s Testimonial
“Before Food Data Scrape, our hyperlocal delivery insights were pure guesswork. Their dark-store ETA tracking changed our visibility overnight. We can clearly see which regions face delays, which platforms serve fastest and how delivery time impacts conversions. This data has become essential to our growth strategy.”
—Head of E-Commerce & Growth, FMCG Brand
Final Outcome
Food Data Scrape helped the brand turn unpredictable hyperlocal delivery times into clear, actionable insights. The result was faster deliveries, fewer ETA complaints, smarter inventory planning and higher conversions across Swiggy Instamart, Zepto and Blinkit.
