How Scraping Zepto Order Data For Analyze Stockouts & Delivery Speed - Mumbai vs Delhi

Quick Overview

A leading quick-commerce analytics firm partnered with Product Data Scrape to improve fulfillment performance and reduce order failures across two of India’s busiest metros. Using Scraping Zepto Order Data For Analyze, the project focused on comparing stockouts and delivery turnaround times between Mumbai and Delhi. By building a structured Zepto Grocery Store Dataset, the client gained visibility into order success rates, item availability, and last-mile efficiency. Over a 90-day engagement, the initiative delivered measurable improvements—cutting stockout-related cancellations by 32%, improving average delivery speed by 18%, and enhancing operational forecasting accuracy across both cities.

The Client

The client operates in the fast-growing quick-commerce intelligence space, supporting retailers and delivery platforms with performance benchmarking and operational insights. With hyperlocal grocery delivery becoming intensely competitive, brands are under constant pressure to meet customer expectations for instant fulfillment and zero stockouts.

Before engaging Product Data Scrape, the client relied heavily on manual data collection and limited platform reports, which lacked granularity and real-time visibility. As demand surged post-2022, their existing approach struggled to capture city-level variations in fulfillment performance. They needed a scalable way to scrape zepto order data for business insights, especially to understand why some metros consistently outperformed others.

The urgency grew when Mumbai and Delhi showed diverging performance trends—Mumbai delivering faster but facing higher stockouts, while Delhi maintained better availability but slower last-mile speed. Without structured datasets, leadership lacked clarity on operational bottlenecks. The partnership aimed to transform fragmented data into actionable intelligence, enabling smarter decisions across inventory planning, logistics optimization, and city-specific service strategies.

Goals & Objectives

• Goals

Improve visibility into city-wise stockout patterns and fulfillment gaps.

Enable faster decision-making through real-time performance insights.

Build a scalable data foundation for future quick-commerce analytics.

• Objectives

Automate extraction of order-level metrics across Mumbai and Delhi.

Centralize datasets for faster comparison and reporting.

Support real-time dashboards for operational teams.

• KPIs

Reduce stockout-driven order cancellations by 30%.

Improve average delivery time by at least 15%.

Increase data accuracy to above 98%.

To achieve these targets, the solution prioritized the ability to scrape zepto product availability data, ensuring precise visibility into which SKUs were unavailable, when, and in which locations—critical for understanding service gaps at scale.

The Core Challenge

Before this engagement, the client faced multiple operational hurdles. Data on stockouts and delivery performance existed, but it was scattered across manual reports, internal spreadsheets, and delayed platform summaries. This fragmentation made it nearly impossible to correlate availability issues with fulfillment delays.

Operational teams struggled to pinpoint whether late deliveries were caused by warehouse bottlenecks, rider shortages, or inaccurate stock information. Additionally, inconsistent reporting formats made it difficult to compare Mumbai and Delhi objectively.

Without the ability to Scrape Zepto App Data for Real-Time Grocery Insights, the client relied on historical data that was already outdated by the time it reached decision-makers. This lag resulted in missed optimization opportunities, reactive firefighting, and poor alignment between inventory planning and last-mile execution. The challenge wasn’t just data access—it was transforming raw information into timely, city-specific intelligence that could guide daily operational decisions.

Our Solution

Product Data Scrape designed a phased, automation-first approach to address the client’s challenges and deliver high-impact insights.

Phase 1: Data Architecture & Pipeline Setup

We established automated scraping workflows to capture order success rates, SKU availability, delivery timestamps, and fulfillment status across both cities. This eliminated manual reporting delays and created a consistent data foundation.

Phase 2: Smart Automation & Validation

Advanced validation checks ensured near-perfect accuracy while minimizing duplicate or missing records. This phase laid the groundwork for reliable benchmarking.

Phase 3: Operational Analytics Layer

Using quick commerce operational analytics using scraping, we transformed raw order data into dashboards highlighting stockout frequency, average delivery time, and fulfillment success by zone and time of day.

Phase 4: City-Level Performance Comparison

Custom analytics models enabled side-by-side comparisons of Mumbai and Delhi. Leadership could instantly see where stockouts peaked, which neighborhoods faced longer delivery windows, and how performance changed during peak hours.

Phase 5: Continuous Optimization

The final phase focused on automating daily performance reports and alert systems—flagging unusual spikes in stockouts or delays so teams could respond proactively rather than reactively.

This structured rollout ensured fast value delivery while building a scalable intelligence framework for long-term growth.

Results & Key Metrics

• Key Performance Metrics

Stockout-related order cancellations reduced by 32%.

Average delivery time improved by 18% in Mumbai and 14% in Delhi.

Data accuracy increased to 99% across monitored SKUs.

Real-time dashboards adopted by 4 operational teams.

These gains were powered by seamless integration of the Zepto Quick Commerce Data Scraping API, ensuring always-on visibility into performance shifts.

Results Narrative

Within the first 60 days, the client transformed how leadership viewed fulfillment performance. What was once anecdotal became data-driven. Teams could now quantify the true impact of availability gaps on delivery speed and customer experience. The insights not only improved daily operations but also reshaped long-term planning, enabling smarter staffing models, better micro-warehouse stocking strategies, and city-specific service benchmarks.

What Made Product Data Scrape Different?

Product Data Scrape delivered more than just data—we delivered operational intelligence. Our edge came from proprietary automation frameworks, adaptive scraping logic, and AI-ready validation pipelines. By enabling Real-time scraping for stockout and delivery monitoring, we empowered the client to move from reactive reporting to proactive optimization. The result was a future-proof system that scales effortlessly with growing order volumes and expanding city coverage.

Conclusion

This case study highlights how data-driven strategies redefine performance in quick commerce. By analyzing delivery speed differences between Mumbai and Delhi, the client gained unprecedented clarity into operational strengths and gaps. With Product Data Scrape’s scalable scraping and analytics framework, they now operate with confidence—armed with real-time insights that drive faster decisions, better customer experience, and stronger competitive positioning.

FAQs

1. What type of data was extracted from Zepto?
We collected order success rates, SKU availability, delivery timestamps, stockout frequency, and fulfillment status to build a complete view of operational performance across cities.

2. How long did the project take to deliver value?
Initial insights were delivered within 30 days, with full performance dashboards operational in under 90 days.

3. Was the solution scalable beyond Mumbai and Delhi?
Yes, the framework was designed to expand across additional cities and categories without major reengineering.

4. How did this improve decision-making?
Leadership gained real-time visibility into fulfillment gaps, enabling faster corrective actions and smarter inventory planning.

5. Can similar analytics be applied to other quick-commerce platforms?
Absolutely. The same approach can be extended to multiple delivery apps and marketplaces for unified operational intelligence.