Retail & Brands
ML, Data Engineering & Delivery at Scale
Solution Architecture & Integration

A global consumer-goods leader operating across 70+ markets.

Advanced AI Analytics Platform: Time-To-Insight Cut By 65%

65% faster time-to-insight
16% higher trade efficiency
6%+ profitability uplift
Cut By 65%

The Challenge

Across more than 70 markets, the business couldn’t unify and analyse complex consumer and retail data. Fragmented systems and inefficient analytics delayed insights and slowed decisions, leaving teams slow to react to shifting demand. Trade segmentation was inefficient, which blunted campaign effectiveness, and inconsistent product and pricing strategies held back profit.

What We Did

We brought data streams from more than 70 markets into one governed analytics platform, then added AI-driven predictive models - forecasting, portfolio-optimisation and micro-marketing - on top. Local teams gained real-time, market-specific insight, precise consumer segmentation, dynamic pricing and more accurate product-performance forecasting, with reporting and segmentation automated to take manual effort out of the process.

The Impact

Time-to-insight down 65% - local markets act on data in real time
Trade efficiency up 16% year on year
Profitability up 6%+ on key lines
Brand equity up 8%
Feature performance up 8%
Share-of-market up 3%+ on key SKUs

How We Delivered

Technology stack
Java · Python · MongoDB · data-lake integration · deployable on Azure, AWS or private hosting
Team & approach

A 7-person senior, Europe-based delivery team - two data engineers, a senior data engineer, an AI developer, a DevOps engineer, a senior analytics consultant and a project manager - working in an agile, data-driven model.

Inside The Build

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