UAE Q-Commerce SKU Data Scraping Case Study — AI Trimmed Assortment 22%, Grew GMV 14%
How a UAE q-commerce operator used UAE q-commerce SKU data scraping across Kibsons, InstaShop and Noon to benchmark 12,000+ SKUs daily on price, availability and category share — and used AI assortment optimization to trim its own SKU count 22% while growing GMV 14%.
Client overview
Who the client is
The client is a UAE q-commerce operator running dark-store fulfilment across Dubai, Abu Dhabi, and Sharjah with an assortment of 15,000+ SKUs spanning grocery, FMCG, fresh, and specialty categories. The category and buying team had inherited an assortment strategy built on 'more is better' — every category manager wanted their SKUs listed, every supplier lobbied for shelf presence, and nobody had systematic data on which SKUs were actually earning their space versus which were quietly dragging warehouse economics and category clarity. The operator's leadership suspected the assortment was over-broad — that 20-25% of SKUs were generating a tiny share of GMV while consuming disproportionate warehouse space, pick complexity, and category-manager attention. But they had no defensible way to prove it, and every attempt to trim SKUs met internal resistance grounded in anecdote rather than data. They needed reliable UAE q-commerce SKU intelligence at daily-refresh cadence across competitor platforms — Kibsons, InstaShop, and Noon — to benchmark their own assortment against the competitive set, identify SKUs earning their space versus those quietly under-performing, and give the buying team defensible authority to rationalize the catalog. Names are anonymized for confidentiality; metrics are shown exactly as delivered.
Objectives
What they wanted to achieve
- Benchmark 12,000+ SKUs daily across Kibsons, InstaShop, and Noon competitor platforms
- Identify SKUs where the operator was over-priced or under-available versus competitors
- Surface competitor SKUs the operator did not carry (assortment gap analysis)
- Give the buying team defensible SKU-level data to rationalize the catalog
- Trim the assortment by 20%+ without losing GMV — ideally growing it
- Replace 'more is better' assortment logic with data-anchored SKU selection
The challenge
15,000 SKUs, no benchmark, and a category team defending every listing on principle
Q-commerce assortment strategy sits at an uncomfortable intersection: category managers each want their sub-category to be well-represented, suppliers actively lobby for shelf space, warehouse teams complain about pick complexity from a bloated SKU count, and finance sees inventory tied up in slow-moving lines. The operator's leadership knew intellectually that not all 15,000 SKUs were earning their space — some were structural must-haves, but many were legacy listings from launch, supplier concessions, or category-manager preferences. The problem was that every attempt to trim ran into the same wall: no defensible data on how each SKU performed against the competitive set. A category manager could always argue 'yes it moves slowly for us, but competitors carry it, so removing it would lose customers.' Without daily competitive SKU data, that argument won every time. Simultaneously, the buying team suspected the operator was missing SKUs that competitors carried — assortment gaps that were quietly costing basket-completion and customer retention. Both problems needed the same input: continuous SKU-level competitive visibility across Kibsons, InstaShop, and Noon.
The solution
A 3-platform, 12,000-SKU UAE q-commerce assortment intelligence pipeline
FoodDataScrape built a UAE q-commerce SKU data scraping pipeline across Kibsons, InstaShop, and Noon covering 12,000+ SKUs daily — with cross-platform SKU matching, price benchmarking, availability tracking, and AI assortment-optimization scoring layered on the operator's own catalog. The build went live in six weeks; the first data-anchored SKU rationalization recommendation was delivered four weeks after go-live, and the buying team executed the 22% trim over the following quarter. The daily refresh continues to power ongoing assortment decisions.
Scrape 3 UAE q-commerce platforms
We mapped and scraped Kibsons, InstaShop, and Noon UAE grocery catalogs daily — capturing every SKU with price, pack size, availability status, promo overlays, and category tags across all covered UAE metros.
AI cross-platform SKU matching
An AI SKU-matching layer paired the same product across Kibsons, InstaShop, Noon, and the operator's own catalog — even when product names, sizes, and package labels differed — producing a single canonical SKU view with per-platform availability and pricing.
Assortment-optimization scoring
An AI assortment-optimization layer scored every SKU in the operator's catalog against competitor availability, pricing, and category share — surfacing SKUs the operator was over-priced or over-stocked on, SKUs competitors carried that the operator did not, and SKUs earning their space versus quiet under-performers.
The AI layer
How does AI-assisted UAE q-commerce assortment optimization work?
AI-assisted UAE q-commerce assortment optimization combines UAE q-commerce SKU data scraping with cross-platform product matching and category-share analysis — surfacing which SKUs a q-commerce operator should keep, trim, or add based on competitive availability, pricing, and category-share signals across Kibsons, InstaShop, and Noon.
On top of the raw feed, an AI assortment-optimization layer turned multi-platform SKU data into UAE q-commerce SKU intelligence: it matched the operator's SKUs to their competitors' equivalents across Kibsons, InstaShop, and Noon, scored each SKU on price competitiveness and category share, identified structural gaps in the operator's catalog, and delivered the buying team a defensible SKU-level trim-versus-keep-versus-add recommendation. The system's core insight was that 22% of the operator's SKUs were structurally under-performing — either massively over-priced versus competitor equivalents, or in categories where the operator's basket-completion economics did not justify the listing — and trimming them while adding 340 identified gap SKUs from competitors produced net GMV growth of 14%.
- Matched 12,000+ SKUs across Kibsons, InstaShop, Noon, and the operator's own catalog
- Identified 22% of the operator's SKUs as structural under-performers versus competitive set
- Surfaced 340 assortment-gap SKUs competitors carried that the operator did not
- Delivered SKU-level trim-versus-keep-versus-add recommendations to the buying team
- Produced 14% GMV growth post-trim — gap-SKU additions outweighed trimmed lines
- Reduced warehouse pick complexity meaningfully alongside the assortment rationalization
Data captured
What data we captured
The pipeline captured a full UAE q-commerce SKU data intelligence view. Every data point below feeds directly into the assortment-optimization score — SKU-level pricing reveals competitive positioning, availability reveals category-share pressure, cross-platform matching enables like-for-like benchmarking, and category tags enable segment-level analysis of where the operator was over- or under-invested:
| source | method | fields |
|---|---|---|
| Kibsons UAE | Kibsons data scraping | SKU · pack · AED |
| InstaShop UAE | InstaShop data extraction | SKU · availability · promo |
| Noon UAE | Noon grocery data scraping | SKU · category · pricing |
| AI assortment layer | Cross-platform matching + scoring | 12k SKUs · trim/keep/add |
BEFORE VS AFTER
Before vs After Comparison
| Metric | Before | After (FoodDataScrape) |
|---|---|---|
| Assortment approach | 'More is better' + supplier lobby | Data-anchored SKU rationalization |
| Competitive SKU data | Anecdotal, no benchmark | 12,000+ SKUs matched daily |
| Gap analysis | None | 340 gap SKUs surfaced |
| SKU count | ~15,000 (bloated) | ~11,700 (rationalized) |
| Warehouse pick complexity | High, complaint-driven | Reduced meaningfully post-trim |
| GMV outcome | Baseline | +14% post assortment optimization |
ROI impact
From assumption to measurable ROI
Structural under-performers removed from catalog.
Gap-SKU additions outweighed trimmed lines in revenue.
Competitor-carried SKUs the operator was missing.
Across Kibsons, InstaShop, and Noon UAE.
The data replaced 'more is better' assortment logic with a defensible SKU-level rationalization framework — and gave the buying team the authority to trim 22% of the catalog while growing GMV 14% by simultaneously filling 340 competitive-gap SKUs.
Client testimonial
In the client's words
"For years every SKU trim discussion ended with 'but competitors carry it.' Now we know exactly which SKUs competitors carry, at what price, and in which categories they beat us. The trim was easy once the data was in the room."
— Head of Category, UAE q-commerce operator (name withheld)
Why FoodDataScrape
Why they chose FoodDataScrape
- Specialists in UAE grocery and q-commerce data scraping
- Kibsons, InstaShop & Noon UAE coverage out of the box
- AI-assisted cross-platform SKU matching
- Assortment-optimization scoring with trim/keep/add recommendations
- Compliance-aware sourcing and dedicated UAE analyst support
- Live in six weeks with a free proof-of-concept first
Questions
Frequently Asked Questions
It combines Kibsons, InstaShop, and Noon SKU data scraping with AI cross-platform product matching that pairs each of the operator's SKUs to their competitor equivalents. Assortment-optimization scoring then compares availability, pricing, and category share across the competitive set — surfacing which SKUs the operator should trim (structural under-performers), which to keep (category anchors), and which to add (competitor-carried SKUs the operator is missing).
Kibsons, InstaShop, and Noon — the three dominant UAE q-commerce and grocery-delivery platforms — covering thousands of SKUs each across Dubai, Abu Dhabi, and Sharjah dark-store fulfilment. The pipeline can be extended to additional platforms (Careem Quik, Talabat Mart) as required.
AI product-matching models pair the same SKU across Kibsons, InstaShop, Noon, and the operator's own catalog — even when product names, sizes, and package labels differ. The output is a single canonical SKU ID with per-platform availability and pricing, enabling like-for-like competitive comparison and unit-price normalization across pack sizes.
GMV was tracked at the operator's own catalog level pre- and post-assortment optimization across a full quarter, controlling for macro seasonality, promotional cadence, and category-mix drift. The 14% growth represents net GMV lift after subtracting revenue lost from trimmed SKUs and adding revenue from the 340 gap SKUs the AI layer identified and the buying team onboarded. Simultaneously the warehouse team measured pick-complexity reduction from the 22% SKU trim.
Yes — the same cross-platform matching and assortment-optimization approach works for grocery retail, C-store operators, click-and-collect models, and any multi-SKU operator with competitor visibility on delivery or marketplace platforms. Non-q-commerce grocery adapts using the same methodology with the relevant competitive data sources.
Yes — we use compliance-aware sourcing across all UAE markets and grocery/q-commerce platforms.
Need daily UAE q-commerce SKU data for your assortment decisions?
Tell us your target categories and platforms. We'll scope a Kibsons + InstaShop + Noon SKU tracking pipeline and show sample output in a short demo.
