Predictive Product Propensity & Basket Optimisation

🚩 The Challenge
A leading multi-channel retailer needed to increase Average Order Value (AOV) and Customer Lifetime Value (CLV) across its vast product inventory. The challenge was to move beyond basic "customers also bought" logic to a sophisticated predictive model that could identify hidden product associations and anticipate the customer's "Next Best Buy" in real-time.

🛠️ The Solution
🧺 Market Basket Analysis (MBA): Employed association rule mining (Support, Confidence, and Lift) to identify high-affinity product bundles and cross-sell opportunities within millions of transactions.

🔮 Next Buy Analysis: Developed predictive propensity models to determine the most likely subsequent purchase category for a customer based on their historical lifecycle and seasonal trends.

🛠️ Algorithmic Personalisation: Integrated these insights into the automated CRM and web-personalisation engines to deliver hyper-relevant product recommendations.

📈 The Result
📈 Increased Average Order Value: Successfully drove a measurable uplift in AOV by optimising cross-category recommendations at the point of sale.

🎯 Precision Cross-Selling: Improved the efficiency of "Next Best Action" campaigns, resulting in higher click-through rates and conversion from single-category to multi-category shoppers.

🛡️ Churn Mitigation: Identified early indicators of declining interest by monitoring "Next Buy" windows, allowing for proactive retention interventions.