[2026 Latest] Avoiding Wasteful Coupon Distribution and Profit Erosion with Uplift Modeling

In the world of EC business, the biggest factor squeezing operating profits despite heavy promotional spending is "blanket coupon distribution." Providing discounts to loyal customers who would have purchased anyway—resulting in "profit erosion"—is a typical trap for companies lagging in data-driven decision-making. This article provides a professional, practical explanation of how to use uplift modeling (causal inference) to identify only those customers whose behavior will turn positive due to an intervention (coupon) using AI.

1. Structural Flaws of "Blanket Coupons" That Erase Profits

A common issue we see in consulting is the excessive use of "10% OFF for all members" to hit sales targets, which ultimately lowers the average transaction value and causes gross profit to fall below the previous year. This happens because of "cannibalization," where incentives are given to customers who didn't actually need a coupon to buy.

In traditional CRM strategies, "response prediction"—creating lists in descending order of purchase probability—was the mainstream approach. However, this causes "people who would buy anyway (Sure Things)" to appear at the top of the list, encouraging wasteful promotional spending. In our actual consulting projects, we have seen many cases where simply cutting this "wasteful distribution" reduced advertising costs by 20% while maintaining or even increasing profits.

2. The Four Customer Segments Identified by AI and "Persuadables"

Uplift modeling classifies customers into the following four segments. The key is predicting "how behavior changes (the increment)" based on the presence or absence of a campaign.

• Sure Things: Customers who will purchase even without a coupon. Sending them one leads to pure "profit erosion."
• Persuadables: Customers who only purchase when they have a coupon. This is the only true target.
• Lost Causes: Customers who will not purchase regardless of whether they have a coupon. Sending them one is a "waste of distribution costs."
• Sleeping Dogs: Customers who may actually churn or refrain from purchasing due to the intervention (e.g., unsubscribing because they find notifications annoying).

In our Own EC Construction and Growth Support, we use AI to score these "Persuadables" and narrow down the distribution targets to maximize incremental profit. It is not uncommon to see ROAS (Return on Advertising Spend) improve by over 300% compared to traditional "blanket distribution."

3. Steps to Maximize Profit Through Uplift Modeling

In the specific implementation process, we first train the model on the causal relationship between "intervention (coupon distribution)" and "outcome (purchase)" using past campaign data. A common real-world challenge is having biased data where coupons were only sent to a specific segment in the past. However, by using the latest algorithms based on causal inference, it is possible to build highly accurate predictive models while correcting for such biases.

Next, using the constructed model, we calculate the difference (lift value) between the "purchase probability if a coupon is sent" and the "purchase probability if it is not sent" for all customers. Customers with a large difference are the true targets to approach. This methodology serves as a powerful tool in Own EC Construction and Growth Support to achieve both higher repeat rates and improved profit margins.

4. Barriers and Breakthroughs in Implementing "Causal Inference" in the Field

While uplift modeling seems perfect in theory, there are significant hurdles to implementation. A particular case is when staff in the field feel psychological resistance to intentionally creating a "control group" (a group that does not receive coupons). For staff chasing sales targets, not applying a measure to some customers can look like a "lost opportunity."

But by conducting repeated small-scale A/B tests and visualizing how "sending coupons to Sure Things" erodes profits, organizational buy-in can be achieved. The secret to success is not following textbook theory blindly, but gradually expanding the scope of AI personalization while aligning it with on-site KPIs.