How Customer Segmentation and Targeting Helped Two QSR Brands Lift Average Order Values

How can QSR brands get customers to increase their average order value? What about reactivating lapsed customers?

As we explored in an earlier case study, there’s an assumption that only large-value offers will do the trick. But as the results demonstrated, that’s not necessarily true. Correctly segmenting customers and matching them with targeted offers – as we did with these two restaurant chains – will answer both cases. And it’ll do it with less overhead.

Two brands, one problem

Encouraging increased customer spend was the goal for two of our clients. One is a large chain specializing in Filipino fast food; they have over 1,000 locations spread throughout Asia, Europe, North America, and the Middle East. The other brand focuses on Filipino-Chinese cuisine and has over 500 stores in Asia, the Middle East, and the United States.

What we did

To drive increased spend, we created a targeted Average Order Value campaign that would offer customers one of five coupon values. Customers were grouped into five tiers based on their average order value (e.g. under 10, 10-15, etc.). Each tier was given a special offer that was above their average value (e.g. the under-10 tier was given a coupon for orders over 12).

Next, we used email and push notifications to announce the customer had a special offer. For the first brand, this was sent to all customers; for the second, it was sent to customers that hadn’t placed an order in the last 30 days.

Results: 20-50% Increase in AOV

The results for both companies were definitive. The first company, which had offers sent to all customers, showed that users spent 21%-58% more than their initial average order value. And these percentages are based on the upper end of each range, so the uplift was probably a bit higher for most customers.

For the second company, which sent offers to inactive users, the re-activated customers spent 30%-51% more than their initial averages. And the actual uplift for most customers was likely more, for the same reasons as described above.

The Takeaway

Two important lessons can be learned from these outcomes.

The first is the importance of segmenting customers. Without segmentation, a one-size-fits-all offer would not motivate some customers. But dividing customers into groups allows us to offer precise discounts.

The second key lesson is correctly targeting offers. The lowest-value discount wouldn’t motivate many users with high average order values. Meanwhile, sending a high-value discount to customers with low AOVs could result in unnecessarily lost value; a smaller discount would have motivated them just as well. Matching discounts to AOV groups in this way reduces waste.

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