Predicting Customer Response to Discounts

Lisa managed marketing for a retail chain. She spent heavily on discount campaigns—monthly promotions, flash sales, seasonal offers. But she sent them to everyone. Some customers bought immediately. Others ignored them completely. She was wasting money on people who wouldn't respond, and she had no way to tell who was who.

Lisa hired a data analyst to build a model that could predict which customers would actually respond to discounts. The analyst examined purchase history: total spending, purchase frequency, past discount usage, product categories. The goal was to identify discount-responsive customers so Lisa could target them specifically and stop wasting budget on non-responders.

The model predicted that 84.72% of customers would respond positively to discounts. This was higher than Lisa expected—it meant most of her customer base was discount-sensitive. The question wasn't whether to offer discounts, but which products to discount and which specific customers to target within that 85%.

The analyst identified high-value responders. Customers like ID 2500, 3713, and 3870 were top spenders who consistently responded to discounts. These weren't bargain hunters buying cheap items—they were valuable customers who bought more when offered deals. Lisa realized she could use discounts to drive revenue from her best customers, not just clear inventory.

Three product categories dominated: Beauty, Home & Kitchen, and Electronics. These showed the highest spending and strongest discount response rates. Electronics was interesting—the average discount was 27.32%, much higher than other categories, yet customers still responded strongly. This told Lisa that electronics buyers expected big discounts and responded when they got them.

The model's technical performance was perfect: ROC AUC score of 1.0. This meant it flawlessly distinguished between customers who would and wouldn't respond to discounts. Lisa had never seen a model this accurate. It made her confident she could rely on its predictions for campaign planning.

Lisa restructured her discount strategy. She stopped sending blanket offers to all customers. Instead, she used the model to score every customer's likelihood of responding. Customers above the threshold got discount offers. Those below got different messages—new product announcements, loyalty rewards, content marketing—anything but discounts that wouldn't work anyway.

She focused discount campaigns on the three high-performing categories. Beauty and Home & Kitchen got regular promotional emails. Electronics got bigger discounts (25-30%) during strategic periods—back to school, holidays, tax season. She stopped discounting categories that didn't show strong response patterns in the data.

For high-value responders, Lisa created exclusive offers. The top 10% got early access to sales, higher discount percentages, or bundle deals. This personalized approach made her best customers feel valued while maximizing their purchase frequency. She wasn't just moving products—she was building loyalty among her most profitable segment.

Lisa tracked results. The model was right—response rates improved when she targeted predicted responders. But she also noticed something: some customers who never responded to discounts bought regularly at full price. These were valuable too, just in a different way. The model helped her avoid annoying them with constant sale notifications.

Lisa learned that not all customers are discount-driven, but most are—and identifying which is which matters more than the discount percentage itself. The perfect model accuracy gave her confidence, but she stayed aware that customer behavior could shift. She planned to retrain the model quarterly using updated purchase data.

Today, Lisa runs targeted discount campaigns instead of mass promotions. She knows which customers to target, which products to discount, and how deep the discounts need to be. Her marketing budget goes further because she's not wasting money on people who won't respond. The model is a tool for precision, not a replacement for strategy—but it makes her strategy far more effective.