Market Basket Analysis Dashboard

Igniting Powerful Insights

• The Challenge Transaction data existed only as raw item-level purchase records, with no visibility into which products were actually bought together or how reliable those pairings were. The business had no way to distinguish a genuinely strong product association from pure coincidence, making cross-sell and shelf-placement decisions largely guesswork.
• The Process Raw transactions were cleaned and restructured in Power Query into item-pair combinations, then modelled to calculate Support %, Confidence (both directions), and Lift for every product pair. These core association measures were built once in DAX and reused across both report pages, so a "strong pair" on one page means exactly the same thing on the other.
• Complexity and Innovation Beyond simple co-occurrence counts, the model layers in bidirectional Confidence scoring, a Support-vs-Lift scatter to separate popular pairs from truly associated ones, and a Pair Quality Filter Funnel that progressively narrows 100% of all pairs down to the 0.8% that clear Co-occurrence, Confidence, and Lift thresholds simultaneously isolating only the highest-quality, actionable pairs.
• Adaptive Learning for Enhanced Accuracy Thresholds for "Strong," "Moderate," and "Weak/No Association" categories were tuned iteratively against the Lift distribution, and basket-size segmentation (Single, Small, Medium, Large, Jumbo) was added to check whether associations held consistently across different basket sizes refining which pairs were flagged as genuinely reliable versus incidental.