#fmcgstrategy

Not all grocery price hikes come with announcements. Some happen quietly — and that silence is what damages brand trust the most.

A product that cost ₹99 yesterday suddenly shows ₹109 today. No notification. No explanation. No promo tag.

Customers notice it instantly on platforms like Amazon Fresh, Instacart, Blinkit, and BigBasket. They compare screenshots, leave reviews, and question the brand — long before pricing or revenue teams even see the change in their monthly report.

By the time brands realize what happened, the damage is already done:

Trust erosion

Drop in repeat purchases

Negative reviews blaming the brand, not the platform

The real problem isn’t the price increase. It’s not knowing when, where, and how often it happened.

Without daily SKU-level price tracking, silent hikes stay invisible inside dashboards — until customers make them visible on social media.

FoodDataScrape tracks daily selling price vs MRP history across grocery platforms, helping brands detect silent price hikes early, understand platform-level changes, and protect brand trust before it’s impacted.

📩 DM “GROCERY” to monitor price changes daily — before customers notice them first.

🎯 How Food Data Scrape Empowered an FMCG Giant to Scrape 1,000+ Diwali Offers Across Quick Commerce Apps

When the festival of #Diwalirollsaround, offers flood in from all corners—quick-commerce apps, grocery platforms, and local stores. One #FMCGpowerhouse turned this chaos into competitive advantage by scraping over 1,000 offers across apps during the Diwali period. Here’s what they gained—and how others can replicate their success.

🔍 Key Takeaways from the #FMCGGiant’sStrategy

📦 Massive offer coverage — captured a large sample of offers (bundle deals, flash sales, discount promos) across quick commerce platforms.

📉 Trend spotting in real time — identified which categories, items, or promotion types (e.g. combo / “buy 1 get 1”) resonated most with consumers.

💡 Pricing behavior & discount depth — mapped how steep discounts were across different product lines and what discount levels moved demand.

🕒 Promo timing insights — observed when offers peaked (pre-Diwali rush, Diwali eve, post-festival) to time launches or marketing pushes effectively.

📍 Localized differences — saw variation between cities / zones in what offers were prevalent, what #productssoldbest, and which promotions lifted demand more.