Scraping ETA & Delivery Time Accuracy via Rider Tracking APIs (Zepto, Blinkit, Getir, Gorillas)

July 15, 2025

Introduction

In the fast-paced realm of quick commerce and on-demand delivery, the Estimated Time of Arrival (ETA) is more than just a number—it’s a promise. Whether you're ordering groceries from Blinkit, late-night snacks from Zepto, or essentials from Getir or Gorillas, customers rely heavily on real-time delivery updates. But how accurate are these ETAs? What factors influence them? And how can businesses gain insights from these delivery timelines using rider tracking APIs?

Welcome to the age of ETA and delivery time analytics, powered by web scraping and API data extraction. This blog dives deep into how brands and analysts are using Rider Tracking APIs and delivery platform data to measure, analyze, and improve delivery efficiency—using real-time insights from industry giants like Zepto, Blinkit, Getir, and Gorillas.

Why ETA Matters in Quick Commerce?

The promise of 10-minute delivery has revolutionized how customers shop for everyday goods. However, maintaining delivery speed requires a complex orchestration of supply chain, inventory, traffic data, and rider allocation. At the heart of this system lies ETA prediction.

Importance of ETA Accuracy:

Customer Satisfaction: Accurate ETAs reduce anxiety and build trust.
Operational Efficiency: Helps in optimizing rider allocation and warehouse operations.
Competitive Advantage: Brands with better delivery reliability attract more repeat users.
Fraud Prevention: Detect discrepancies between promised and actual delivery times.

Understanding ETA accuracy isn’t just a technical challenge; it’s a competitive necessity.

What Are Rider Tracking APIs?

Rider Tracking APIs provide real-time updates about the delivery executive’s location, route, speed, and status. These APIs form the backbone of apps like Zepto and Blinkit, allowing them to dynamically estimate delivery times based on:

Rider location (via GPS)
Order preparation status
Traffic conditions
Delivery queue load
Time of day or week

These APIs not only help the platform offer a live ETA to the user but can also be scraped or accessed via monitoring techniques to collect large-scale data on delivery trends, delays, and optimization patterns.

Enhance decision-making with Real Data APIs—access live insights, track key metrics, and power smarter, real-time business operations now!

Get Insights Now!

Platforms Analyzed: Zepto, Blinkit, Getir, Gorillas

Let’s take a look at how each of these platforms provides tracking data and how their delivery systems differ.

1. Zepto: The 10-Minute Grocery Giant in India

Zepto is one of India’s leading instant delivery platforms with a heavy focus on hyperlocal fulfillment centers. Their ETAs are aggressively short, often promising delivery within 7-10 minutes.

Rider Tracking Behavior:

Once an order is placed, the app shares:
- Live location of the delivery partner
- Estimated delivery time
- Distance remaining
APIs behind the app update every: 5-10 seconds

Scraping Use Case:

Businesses scrape Zepto’s rider movement and timestamps to evaluate:
- Route optimization efficiency
- Actual delivery time vs. promised ETA
- Peak hour performance

2. Blinkit: Instant Delivery Across Indian Metros

Blinkit (previously Grofers) has refined its delivery model by partnering with cloud warehouses and local retailers. It provides ETAs that vary from 10 to 20 minutes depending on the city and location.

ETA Analytics from Blinkit APIs:

API calls update the app’s ETA countdown every few seconds.
Rider location and last-mile journey can be traced via map overlays.
Timestamped notifications (e.g., "Rider picked up your order at 12:03 PM")

Use Case for Scraping:

Delivery time accuracy by zone or city
Correlating weather/traffic data with delivery delays
Identifying fulfillment centers with longer delays

3. Getir: Turkey’s Global Instant Delivery Export

Getir operates across Turkey, Europe, and the U.S., offering rapid delivery of groceries and daily essentials. Unlike Zepto, Getir places more weight on delivery fleet management and multi-city dispatch centers.

Rider Tracking Observations:

Rider locations visible to users with real-time map
ETAs visible during and after checkout
Order lifecycle statuses (e.g., “Out for Delivery”, “Arriving Soon”)

Scraping Use Case:

Analyzing how rider density affects delivery times
ETAs based on SKU weight or order type
Efficiency benchmarks between Getir vs. Gorillas in the EU market

4. Gorillas: Fast Delivery in Europe’s Urban Hubs

Gorillas built its brand around 10-minute delivery in major European cities. Its app also offers highly detailed rider tracking and ETA data.

Delivery Tracking Features:

Rider path and direction shown on map
Time of dispatch and delivery logged
Push updates when rider is nearby

Use Case for Scraping:

Real-time rider movement logging
Estimating delay causes (construction, weather, traffic)
Comparing actual delivery vs. SLA (Service Level Agreement)

How to Scrape ETA and Rider Tracking Data?

Scraping real-time ETA and tracking data from these platforms can be done in two primary ways:

1. Front-End Web/App Scraping

Many apps expose tracking data on their web/mobile front-ends, which can be scraped using:

Selenium / Playwright: Simulate a user placing an order and watching the ETA update.
Puppeteer: Automate data extraction from the live map and rider tracking interfaces.
Mobile Emulator Scraping: Use Android emulators to run the app, monitor API calls, and extract data.

Example Fields to Scrape:

Estimated delivery time
Actual delivery time (order timestamp vs delivery confirmation)
Rider GPS path
Number of stops before delivery
Delay reason (if shown)

2. Direct API Monitoring

Using tools like Charles Proxy, Fiddler, or Burp Suite, you can inspect and monitor network traffic from the app, revealing Rider Tracking APIs such as:

/rider/eta/{order_id}
/delivery/track/{rider_id}
/map/ping/position

Once these APIs are reverse-engineered, developers can:

Log real-time delivery metrics
Build dashboards for ETA prediction benchmarking
Cluster analysis of high-delay zones

Note: Always respect platform robots.txt rules, rate limits, and ethical boundaries.

Unlock delivery insights—scrape rider tracking and ETA data safely with proxies and APIs to optimize logistics and predict delays!

Get Insights Now!

Challenges in ETA Data Scraping

Scraping ETA data is immensely valuable, but it comes with its share of complexities.

1. Dynamic Token Authentication

Many Rider APIs are protected by short-lived tokens or encrypted headers.

Solution: Use session replay or automation scripts to refresh tokens dynamically.

2. Geo-Specific Content

Delivery data is highly geo-targeted; your IP or device must be within a service zone.

Solution: Use residential proxies or emulate mobile devices in supported locations.

3. Anti-Bot Measures

Platforms like Getir and Gorillas use bot detection via CAPTCHAs and behavior tracking.

Solution: Rotate user agents, implement headless browsing, and maintain realistic interaction rates.

4. Incomplete Data

Sometimes apps don’t show the exact delivery time post-delivery.

Solution: Timestamp the order placed, order picked, and order delivered events yourself for accuracy.

Real-Time Data Use Cases for Businesses

Scraped ETA and delivery accuracy data offer valuable business intelligence across multiple domains:

Retailers & Partners:

Compare delivery efficiency across platforms (e.g., Zepto vs Blinkit).
Benchmark SLAs for partnership negotiations.

Logistics & 3PL Providers:

Understand rider idle times, route inefficiencies, and under-performing zones.

Urban Planners & Government Agencies:

Analyze last-mile congestion and suggest optimized zoning or traffic flows.

Investors & Analysts:

Track company performance during peak times (holidays, promotions).
Measure user retention via delivery reliability.

Customer Service Optimization:

Preemptively flag risky deliveries based on scraped trends and escalate with priority.

Building a Rider Tracking Dashboard

With scraped ETA and rider tracking data, businesses can build dashboards that visualize:

Live Delivery Heatmaps
Delay Reasons & Counts
Average ETA vs. Actual Time
Top Performing Zones
Low Reliability Locations

Using tools like Power BI, Tableau, Grafana, or custom React dashboards, you can plug in APIs or CSV data pipelines from scraping bots and enable real-time decision-making.

Ethical Considerations

While Rider Tracking API scraping offers immense insights, it must be done with caution:

Avoid overloading servers with high-frequency API calls
Respect platform terms of service and user data privacy
Use scraped data solely for benchmarking or analytics, not for interference
Always anonymize data before use

Future of ETA & Delivery Time Scraping

As quick commerce matures, platforms may start offering public analytics dashboards or developer APIs for select delivery metrics. Until then, web scraping remains a key tool to gain independent, unbiased performance insights.

Expect innovations like:

AI-powered ETA forecasting
Cross-platform benchmarking APIs
Predictive delivery route simulators
Weather-delivery correlation scrapers

Conclusion

For enterprises and analysts seeking reliable access to rider tracking data, ETA benchmarking, and delivery accuracy scraping, partnering with experienced scraping providers is essential.

Real Data API offers enterprise-grade scraping solutions for platforms like Zepto, Blinkit, Getir, and Gorillas. From real-time rider tracking to historical delivery accuracy datasets, Real Data API helps businesses unlock performance metrics across the quick commerce ecosystem—with full compliance and efficiency.