Most small retailers are sitting on a goldmine of data they never analyze. Your POS system contains information that AI can turn into actionable insights in minutes.
What AI finds in your sales data:
Best and worst performers: Which products by SKU, category, and supplier have the highest and lowest margin contribution.
Sales patterns: Which days, hours, and seasons drive the most revenue — and what products sell best in each period.
Basket analysis: Which products are most frequently purchased together, revealing bundling and placement opportunities.
Slow movers: Products that have not sold in 30+ days that are tying up cash and taking up shelf space.
Customer segments: Who your most valuable customers are, how often they visit, and what they buy.
Pricing opportunities: Where you have pricing power — products where demand is inelastic and you could increase margin.
How to do it: Export your POS data (most systems do this easily). Paste it into ChatGPT or Claude with the prompt: “Analyze this retail sales data and identify: top 10 products by margin contribution, bottom 10 products by sell-through rate, products frequently purchased together, and any unusual sales patterns worth investigating.”
Result: Most retailers who do this for the first time find 3–5 significant opportunities they were not aware of.
When did you last analyze your full sales data? What surprised you most?