This is the Electee Review. Today, market basket analysis and why the algorithm that put beer next to diapers is now a full AI discipline in retail and finance. The core argument is simple. Every transaction a customer completes is a record of what gets bought together, and buried in that record are product affinities no clerk would ever notice by hand. Market basket analysis is the method for pulling those affinities out of raw sales data at scale. The mechanics rest on three metrics. Support tells you how often an item or item pair shows up at all. Confidence tells you how often one item leads to another once the first is in the basket. Lift tells you whether that pairing is a real association or just coincidence. A lift above one means the items are genuinely linked, not just both popular. The stakes are practical, not academic. In retail, these associations drive shelf placement, bundle offers, and cross-sell prompts at checkout. In finance, the same technique is repurposed to flag co-occurring transactions that signal fraud or unusual account activity. The method doesn't care what the basket contains, line items, claims, clicks. It only cares what shows up together. What's changed heading into 2026 is scale and tooling. AI systems now run these association rules continuously across millions of transactions, refreshing product affinities in near real time instead of a quarterly report. That's a real shift in speed, but it doesn't change the underlying math or its limits. Lift and confidence still describe correlation. They still say nothing about why two items travel together, and treating a strong association as a causal lever is where retailers and analysts get it wrong. So the practical guide amounts to this. Market basket analysis is a well-understood statistical technique, newly accelerated by AI tooling, that surfaces which products or transactions cluster together. It is genuinely useful for merchandising and fraud detection. It is not a substitute for understanding customer behavior, and anyone selling it as prediction rather than pattern detection is overselling the tool. That's the review.