Grocery Price Data Feed for a Comparison App




 Industry: Consumer Tech (Price Comparison / Savings App)

Region: United Kingdom (model replicated for US and South Africa clients)

Retailers covered: Tesco, Sainsbury's, Asda, Morrisons, Aldi, Lidl, Waitrose, M&S, Ocado

Services used: UK Grocery Data API, Product Matching, Real-Time Price Scraping

The Client

A pre-launch UK startup building a basket-comparison app: a shopper enters their weekly list, and the app shows the total cost of that basket at every major supermarket nearby, including current promotions and loyalty prices.

The Challenge


The product's entire promise rests on one thing — accurate, current prices. That created a brutal data problem for a small team:

  • No tracked SKU list. Unlike a brand monitoring its own products, a comparison app needs broad coverage — initially 25,000 everyday grocery SKUs per retailer across nine retailers.

  • Prices change constantly. UK supermarkets reprice and rotate promotions weekly (and sometimes daily), including loyalty-card pricing (Clubcard, Nectar) that differs from shelf price. A stale price visible to a user destroys trust on day one.

  • Matching is the hard part. "Semi-skimmed milk 2L" exists under nine different names, pack descriptions, and own-label variants. Without reliable cross-retailer product matching, basket comparison is meaningless.

  • Build vs. buy. The founding team scoped an in-house scraping stack and estimated 2 engineers and 4–6 months just to reach launch quality — before the ongoing cost of fighting anti-bot defenses and site redesigns.

The Solution

Actowiz Solutions delivered a managed grocery data feed designed for consumer-facing freshness requirements.

1. Nine-retailer daily extraction.

Our maintained UK grocery scrapers collect product name, pack size, shelf price, loyalty/member price, promotion text and mechanics (e.g., "2 for £3"), unit price, stock availability, and product imagery — refreshed daily, with a 6-hour refresh tier on the top 2,000 highest-velocity SKUs.

2. Cross-retailer product matching.

We delivered a master product graph linking equivalent items across retailers using EAN/barcode data where exposed, plus normalization of titles, brands, and pack sizes — including own-label equivalence mapping (e.g., Tesco own-brand baked beans ↔ Asda own-brand baked beans) reviewed through a human-in-the-loop QA queue. Match accuracy at launch: 97%+ on the core 25,000-SKU universe.

3. API-first delivery.

A REST API in JSON serves the app backend directly, with each record carrying a captured_at timestamp so the app can display "price checked X hours ago" — turning data freshness into a visible trust feature. A weekly full CSV export supports the analytics team.

4. Pilot-to-scale pricing.

The engagement began as a 3-retailer pilot with a free sample dataset, expanded to nine retailers at launch, and is structured to add international markets without re-architecture — the same model we run for US clients (Walmart, Kroger, Target, Aldi, Publix and regional chains) and South African clients (Checkers Sixty60, Pick n Pay, Woolworths, SPAR).

The Results

  • Launched 4 months sooner than the in-house build estimate, with zero scraping engineers on payroll.

  • 25,000 SKUs × 9 retailers refreshed daily — roughly 225,000 price points per day — with feed uptime of 99.5%+ over the first six months.

  • 97%+ cross-retailer match accuracy, making basket-level comparison credible enough to be the app's headline feature.

  • Users see prices on average under 18 hours old (under 6 hours for top sellers), supporting the app's "checked today" trust badge.

  • The app reported an average claimed saving of £14–18 per weekly shop for active users in its launch-quarter PR — a number generated directly from our promotion and loyalty-price coverage.

  • When two retailers redesigned their sites in the same month, feeds were restored within 36 hours with no action required from the client.

"We're a product company, not a scraping company. Actowiz let us stay that way." — Founder & CEO, Client

Why It Worked

  • Freshness engineered for consumers, not dashboards. Internal analytics can tolerate week-old data; a consumer app cannot. Tiered refresh and visible timestamps were designed around that.

  • Matching as a deliverable, not an afterthought. Price data without cross-retailer matching is a spreadsheet; with matching, it's a product.

  • Managed maintenance. Retailer sites change constantly; the client never noticed.

FAQs

Which UK supermarkets can Actowiz provide pricing data for?

Tesco, Sainsbury's, Asda, Morrisons, Aldi, Lidl, Waitrose, M&S, Ocado, Iceland, and Co-op.

Do you capture loyalty and promotional pricing?

Yes — loyalty prices (Clubcard, Nectar, etc.), multibuy mechanics, and promotion text are captured alongside shelf price.

Can the same setup run in other countries?

Yes. We operate equivalent grocery feeds in the US, South Africa, Germany/DACH, the Middle East, and India, using the same matching and delivery architecture.

How is the data delivered?

REST API (JSON) for live app use, plus scheduled CSV/Excel or warehouse delivery for analytics. Free sample datasets are available before any commitment.

Comments

Post a Comment

Popular posts from this blog

Rappi Menu and Rating Datasets - Monitoring Restaurant Performance

Image
  Introduction In the fast-growing food delivery market, restaurants need actionable insights to optimize menu offerings, pricing, and customer satisfaction. Actowiz Solutions helped a leading food chain monitor its performance on Rappi using Rappi Menu and Rating Datasets. By leveraging advanced scraping techniques, we could  Extract Rappi Food Delivery Data  for thousands of restaurants in real time. This allowed the client to track menu changes, promotions, and customer feedback efficiently. With structured and clean data, the restaurant chain gained visibility into performance trends, enabling better decision-making, improved customer satisfaction, and optimized operational strategies across multiple locations and categories. About the Client The client is a national restaurant chain serving urban customers in the food delivery sector. To remain competitive, they needed to track performance metrics on Rappi, including menu changes, pricing, and ratings. Using Scrape R...
Read more

Colombian Stores Price Comparison API - Exito, Carulla, Alkosto

Image
  Introduction In today's fast-paced retail environment, keeping track of pricing trends across multiple stores is critical for both businesses and savvy shoppers. With increasing competition in Colombia's retail sector, brands and consumers are looking for tools that can provide real-time visibility into price fluctuations, promotions, and inventory levels. Leveraging a Colombian stores price comparison API allows businesses to monitor and analyze price changes across Exito, Carulla, Alkosto, and other leading retailers efficiently. By integrating automated data collection, companies can gain actionable insights, optimize pricing strategies, and enhance revenue management. For consumers, such real-time insights make it easier to identify the best deals and save on everyday purchases. The growing need for transparency in pricing and product availability has accelerated the adoption of API-driven solutions that deliver structured, timely, and accurate data from multiple sources....
Read more

How AI-Powered Web Scraping Delivered Unified Blinkit, Zepto, Zomato, Swiggy, and BigBasket Datasets through a Single API Integration

Image
Introduction In the fast-moving world of quick commerce and food delivery, reliable and real-time data drives smarter business decisions. Our client, a leading retail data analytics firm, required continuous access to multiple datasets and APIs — including Blinkit Datasets, Zepto Datasets , Zomato Food Delivery Dataset, Swiggy Food Delivery Dataset, and the Big Basket Grocery Data Scraping API. To achieve this, we implemented advanced AI-Powered Web Scraping and Mobile App Data Extraction Services that unified all delivery and grocery datasets into one structured and easily accessible data pipeline. Client Challenge The client wanted to build a single-source data ecosystem to monitor real-time price changes, product availability, and delivery trends across various apps. However, each platform presented different challenges: Blinkit and Zepto datasets used dynamic app-based structures. Zomato and Swiggy food delivery datasets contained frequently changing menus, ratings, and offers. B...
Read more