Flux Retail: Data Strategy and AI-Driven Customer Personalisation

The challenge

Flux Retail operates three consumer brands across online and physical stores. Each brand had its own customer database, its own email platform, and its own loyalty programme. A customer who shopped across two brands appeared as two separate people in the system. Marketing teams were sending generic campaigns to undifferentiated lists, and the loyalty programmes — originally designed to drive repeat purchases — had become little more than discount delivery mechanisms with no personalisation.

The data to personalise the customer experience existed, but it was scattered across seven systems with no unified view. Purchase history lived in the POS and eCommerce platforms. Browsing behaviour was tracked by each brand's analytics tool. Loyalty data sat in a standalone programme. Email engagement metrics were in a separate marketing platform. When Flux's leadership calculated the cost of this fragmentation — duplicated campaigns, irrelevant offers, customer churn from poor experiences — they estimated it was costing $1.2M annually in wasted marketing spend and lost revenue.

What we built

A unified customer data platform and AI-driven personalisation engine that delivers relevant experiences across all three brands and every customer touchpoint:

• Customer data unification — identity resolution across all seven source systems, creating a single customer profile that captures purchase history, browsing behaviour, loyalty status, email engagement, and store visit patterns across all three brands
• Segmentation engine — dynamic customer segments based on behavioural patterns, purchase frequency, category affinity, and lifecycle stage, updating continuously as new interaction data flows in rather than relying on static quarterly segmentation
• Personalised recommendations — product and content recommendations tailored to individual customer profiles, served across the eCommerce sites, email campaigns, and in-store clienteling tools, with models that learn from response patterns and adapt over time
• Offer optimisation — the system determines the right offer type (discount, early access, loyalty bonus, bundled deal) and the right timing for each customer segment, replacing blanket promotions with targeted incentives that protect margin while driving conversion
• Cross-brand discovery — when a customer's profile suggests affinity for a sister brand they haven't yet shopped, the platform surfaces relevant cross-brand recommendations, increasing customer lifetime value across the portfolio

Technology

Customer data platform with identity resolution engine connecting POS, eCommerce, loyalty, email, and analytics systems across all three brands. Machine learning models for segmentation, recommendation, and offer optimisation, retrained weekly on fresh interaction data. Deployed as a cloud-hosted platform with APIs serving personalised content to web, email, and in-store systems. Flux Retail retains full ownership of all customer data and models.