FMCG

Pack size matrix & unit pricing

Product variations, promotional bundles, and their costs are tracked across FMCG retailers to derive accurate per-unit pricing. Pack sizes and bundle formats differ from one retailer to the next, which makes like-for-like comparison hard. Normalized to a per-unit price, the data lets you compare packs like for like.

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  • Uber Eats
  • Getir
  • Eventim
  • Booking.com
  • Audible
  • Skyscanner
Common problems

Gaps in pack size data cost margin and market share

Pack size and unit price data comes in sheer volume and changes constantly, which keeps in-house scrapers in constant repair. Dedicated specialists in data scraping, advanced matching, and proxy management deliver higher data quality and operational efficiency.

Pack sizes that resist comparison

Standardizing product data across numerous retailers and formats is immensely difficult for FMCG brands. Clients struggle to compare 'like for like' offerings, such as different pack sizes, bundle deals, or promotional variations, and describe an inability to get 'true apples-to-apples insights' for competitive analysis.

Margins eroded by flawed price choices

Pricing choices made without precise Pack Size Matrix & Unit Pricing data can erode margins or miss market opportunities. Revenue growth, competitive positioning, and the capacity to adapt swiftly all suffer in the fast-moving FMCG retail environment.

Mobile apps, CAPTCHAs, and IP blocks

Granular pricing and pack size data is hard to extract at scale, with anti-bot countermeasures, shifting website structures, and mobile app complexities in the way. Non-specialist teams hit IP blocks, CAPTCHAs, and data inconsistencies, and high-fidelity collection at scale becomes nearly impossible.

Scraper and proxy upkeep in-house

Building and maintaining scraping infrastructure for Pack Size Matrix & Unit Pricing pulls internal engineering talent away from core product innovation. Constant scraper adjustments and proxy management turn into an unsustainable operational burden that slows the whole organization.

Our solutions

Comparable unit prices across pack sizes

Our team scrapes pack sizes and bundle prices from each retailer, matches equivalent products, checks the per-unit prices by hand, and runs automated QA.

Unit prices normalized across pack sizes

Data models and human-in-the-loop validation normalize diverse pack sizes, promotional bundles, and product variations into comparable unit prices. You get a standardized view of the market for precise competitive analysis.

Clear visibility for category managers

Granular Pack Size Matrix & Unit Pricing data gives your pricing and category management teams clear market visibility. Use it for dynamic pricing adjustments, competitive benchmarking, and promotional planning to maximize margin and capture market share.

Anti-bot bypasses for apps and e-commerce

Our proprietary, enterprise-grade infrastructure runs adaptive anti-bot bypasses, intelligent proxy rotation, and active monitoring. Data from the most challenging e-commerce platforms and mobile applications keeps coming in high volume, backed by a 99.9% uptime SLA.

The whole data lifecycle, handled

As a fully managed data service, we take care of the entire data lifecycle, from scraping to delivery. Costly in-house acquisition teams and infrastructure become unnecessary, and your engineering and data science teams concentrate on innovation and core business strategy.

Other use cases

Related FMCG data use cases

FMCG enterprise clients also use our data for the use cases below, each backed by enterprise-grade data scraping, advanced matching, and infrastructure.

Start comparing unit prices across pack sizes

A dedicated FMCG data specialist who knows Pack Size Matrix & Unit Pricing in depth will discuss your challenges and strategic objectives with you.

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