A leading Indian apparel and lifestyle company needed digital shelf analytics across 6 online marketplaces and 26 brands to track competitive pricing and grow brand visibility.
Client Overview
A leading lifestyle and apparel company, headquartered in Gujarat and rooted in a flagship textile manufacturing business, has grown into a portfolio of close to 26 fashion brands sold through more than 1,300 stores across 192 cities in India and beyond. Competing at this scale meant tracking not just its own performance but how each brand stacked up against competitors at the same category and price point, across every major online marketplace carrying its products.
Understanding a market this fragmented needed more than periodic manual checks. The company needed to see its online share of revenue across 6 marketplaces, track how micro markets within the fashion category actually behaved, and keep digital shelf analytics current enough to act on, not just reference after the fact. That need brought the company to PromptCloud.
Client Requirements
The brief covered both the company’s own brands and its competitors across the same marketplaces:
- Data from 6 online marketplaces, comparing price points and operations
- Coverage for both in-house brands and competitor brands, not one or the other
- Structured, accurate data delivered in the format the business team specified
- Weekly delivery to the company’s FTP server
- Support for ad-hoc requests under tight deadlines, alongside the standing weekly crawl
Challenges
Tracking 26 brands across 6 marketplaces meant the scope of the crawl was never going to be small. Thousands of SKUs, spread across multiple brands and multiple sellers on each marketplace, needed to be captured consistently enough that week over week comparisons actually meant something, not just a snapshot that happened to be convenient to collect.
Digital shelf analytics at this scale also could not run on a fixed schedule alone. Alongside the standing weekly crawl, the business team regularly needed ad-hoc data pulls under tight deadlines, for a competitor promotion, a pricing move, or a question that came up between scheduled deliveries, and the crawl setup had to flex for that without disrupting the weekly cadence everything else depended on.
Solutions
PromptCloud built this as a custom Data as a Service engagement, sized for weekly volume in the millions of pages and flexible enough to absorb requests outside that schedule.
Crawlers Built for Close to a Million Pages a Week
Custom crawlers were built to extract data from close to a million pages on average every week, covering both the company’s own brand listings and the competitor brands sold alongside them across all 6 marketplaces. That volume made digital shelf analytics genuinely comparable week over week, since the same scope was captured on the same cadence rather than sampled differently each time. Dedicated tech resources kept delivery running smoothly at that scale, rather than treating a million-page weekly crawl as a one-off technical challenge to solve once and leave alone.
A Weekly Cadence the Business Team Could Plan Around
The crawl ran every week at a time the client specified, with data uploaded to the company’s FTP server before the new week began. That timing mattered as much as the data itself, since the business team built its own planning cycle around knowing exactly when fresh numbers would land. Ad-hoc requests were handled alongside this standing schedule rather than instead of it, so a tight-deadline request for one competitor or category did not mean waiting for the next full weekly cycle.
Data Delivered Ready to Feed Internal Systems
Every delivery arrived as a structured JSON file built to plug directly into the company’s internal systems, rather than a flat export needing translation first. That structure is what let the business team move straight from data delivery to analysis, and it is the same kind of foundation PromptCloud lays out in its guide to web data infrastructure for AI, since a continuously refreshed, structured feed like this is exactly what later analytics and modeling work tends to depend on.
Digital Shelf Analytics, Before and After PromptCloud
| Area | Before | After |
| Marketplace coverage | Manual checks, inconsistent across brands | Automated weekly crawl across 6 marketplaces |
| Own vs competitor data | Tracked separately, hard to compare | Same fields, same cadence, directly comparable |
| Ad-hoc requests | Competed with standing reporting work | Handled alongside the weekly schedule |
| Data format | Needed reformatting before use | Structured JSON, ready for internal systems |
Benefits to the Client
Brand visibility across the tracked marketplaces grew 30 percent, backed by data the business team could actually trust week to week rather than a single improved snapshot. Competitive awareness improved across every relevant market the company’s 26 brands competed in, and pricing strategy decisions started from real, current competitor data instead of a periodic manual check.
Operations across brands became easier to optimize once the company could see all of them side by side on the same schedule, and reviews and ratings across platforms could be managed consistently rather than brand by brand. None of this needed a larger internal team, PromptCloud’s crawl and delivery process ran the collection side entirely.
“PromptCloud has been clinical in its approach to providing timely and accurate data, which served as a strong foundation for the top management to comprehend the complex online landscape.”
Director Engineering
Digital Shelf Analytics That Covers 26 Brands Without Losing the Detail
A portfolio this size, 26 brands, 6 marketplaces, thousands of SKUs, was never going to stay understandable through manual checks alone. Digital shelf analytics only earns its name if it can hold that much detail on a schedule the business can actually plan around, not just produce a report occasionally.
What changed the outcome here was matching crawl scale to the size of the portfolio itself, close to a million pages a week, delivered the same way every time, with room left for whatever came up between cycles. That combination is what turned a fragmented, multi-brand marketplace footprint into one dataset the business could act on.
