Grocery Price Inflation Dataset Over 12 Months

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Grocery Price Inflation Dataset Over 12 Months

Quick Overview

A leading FMCG brand partnered with Product Data Scrape to monitor grocery price movements over 12 months. The project created a structured Grocery Price Inflation Dataset Over 12 Months covering product prices, MRPs, categories, pack sizes, retailers, and historical changes. The dataset supported inflation analysis for products such as Amul milk, Britannia biscuits, Nestlé products, packaged foods, beverages, and household essentials.

Key Impact: 93%+ data consistency, 72% faster pricing analysis, and 12 months of structured historical price coverage.

The Client

The client operated in India's competitive FMCG and grocery market, where prices vary across supermarkets, online grocery platforms, quick-commerce applications, and marketplaces. Manual checks and spreadsheets provided only fragmented snapshots and made historical comparison difficult.

Goals & Objectives

The project focused on:

  • Tracking product prices consistently for 12 months.

  • Improving pricing visibility across grocery categories.

  • Identifying significant price increases.

  • Comparing retailers and channels.

  • Building a reusable historical pricing database.

  • Automating collection, validation, and normalization.

  • Standardizing product names, brands, categories, and pack sizes.

  • Capturing MRP, selling price, discounts, availability, retailer, and timestamps.

Core Challenge

Grocery prices change because of inflation, promotions, seasonal demand, retailer strategies, pack-size changes, and competitive activity. Product matching across different pack sizes and inconsistent collection schedules made reliable comparisons difficult. The client required a Grocery Price Index Dataset with continuous historical observations.

Our Solution

Product Data Scrape implemented a phased framework:

1. Product & Category Mapping: Standardized brands, products, categories, pack sizes, MRP, and retailer information.

2. Automated Price Collection: Recurring extraction captured prices, MRP, discounts, availability, retailer, location, and timestamps.

3. Data Cleaning & Normalization: Product names were standardized, duplicates identified, and pack sizes structured for meaningful comparisons.

4. Product-Level Grocery Inflation Analysis: Monthly observations were compared to identify increases, decreases, stable periods, promotions, and category-level movements.

5. Dashboard & Reporting: Data was prepared for dashboards and reports covering products, brands, categories, retailers, locations, and months.

Results

  • 93%+ pricing-data consistency

  • 72% faster historical price analysis

  • 95%+ target validation rate

  • 12 months of structured price observations

  • 65%+ reduction in repetitive manual checks

  • Daily/weekly recurring monitoring for priority products

Conclusion

The Grocery Price Inflation Dataset Over 12 Months gave the client a reliable historical foundation for pricing and category analysis. Automated collection reduced manual work while standardized data improved consistency and enabled inflation tracking, competitive benchmarking, and product-level price analysis. Product Data Scrape can help FMCG brands and retailers build recurring grocery pricing intelligence datasets for smarter retail decisions.



Source : https://www.productdatascrape.com/grocery-price-inflation-dataset-12-months.phphttps://www.productdatascrape.com/grocery-price-inflation-dataset-12-months.php

Original : https://www.productdatascrape.cm/


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