
A retail leader wanted to increase engagement and sales by personalizing the customer journey across online and in-store touchpoints. Vimix implemented an AI-driven recommendation engine and journey orchestration layer that used behavioral data, purchase history, and real-time context to deliver tailored offers, content, and next-best actions—resulting in measurable gains in engagement and revenue.
The client had rich data but no unified view of the customer journey or the ability to act on it in real time. Marketing and merchandising relied on segment-level campaigns rather than individualized experiences. They needed a scalable AI solution that could integrate with existing commerce and marketing systems.
Vimix's AI-powered customer journey and personalization engagement helped the retailer move from segment-level marketing to individualized experiences, driving engagement and revenue with a scalable, data-driven approach.
We unified customer data from web, app, and POS into a single view and mapped key journey stages. We defined KPIs and success metrics for personalization at each touchpoint.
We built and deployed a recommendation engine combining collaborative filtering, content-based signals, and contextual features. Models were trained and refreshed to adapt to changing behavior and catalog.
We integrated the engine with the client's commerce platform and marketing tools so that recommendations and personalized content could be served in real time across channels.
Personalized product and content recommendations drove significant increases in click-through rates, add-to-cart, and conversion.
Attributed revenue from personalized experiences showed a clear uplift versus non-personalized baselines.
The platform supported ongoing experimentation and expansion to new segments and channels.

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Vimix's AI-powered customer journey and personalization engagement helped the retailer move from segment-level marketing to individualized experiences, driving engagement and revenue with a scalable, data-driven approach.

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