C-Store: Promote smarter and more profitably with effective forecasting, modeling, and design

"We lose a nickel on every one we sell, but we make it up in volume."

It's an old joke, and most convenience retailers have seen a promotion that played out that way. Units go up. The report looks great. The store ends up with less profit than before.

Three questions matter most when it comes to promotions:

• Does the category stay profitable?
• Is it pulling sales from items you would have sold anyway?
• What does it tell shoppers about your regular prices?

A couple of years ago, a c-store chain tested a flatbread breakfast sandwich at a sharp price. It was a big seller. By the usual measure, it was a winner.

Them, the team reviewed the data.

Average transaction dollars were down. Sales of higher-margin items were down.

The sandwich was winning units and giving up the basket. The data led the chain to decide against rolling it out.

Measurement is key. You cannot see the answer by looking at one item. You have to look at what changes around the promoted item.

Why c-store promotions are hard to judge

Convenience pricing is complex in ways that make intuition unreliable.

• High traffic, small baskets. With so few items per transaction, a promotion that moves one item can change the whole mix of the basket.
• A diverse product mix. Coffee, packaged beverages, tobacco, beer, snacks, and foodservice all respond to price differently.
• Seasonality and dayparts. The morning rush, the afternoon lull, and late night each behave differently.
• Close competition. Other c-stores, QSRs, and grocers can be a few hundred yards away.
• Rules around the plan. Manufacturer buydowns, tobacco and beer programs, and zone pricing all limit what a promotion can do.

Without a strategic approach, it's easy to run promotions that drive traffic but not profit.

Go deeper on tobacco pricing. Download the guide: Improving performance in the most challenging convenience store category

An essential element of merchandising

Price promotions have long been an essential tool to drive customer traffic, increase average transaction and basket size, and build customer loyalty. They can also undermine category profits or shake customer faith in your everyday prices if not well designed.

NACS Research reported at this year's NACS Show that total in-store transactions fell 4.5% in the first half of 2026 compared with the first half of 2025. With fewer visits to go around, every one is an opportunity for the convenience store to deliver shopper satisfaction, learn something about shopper response, obtain customer insights, and earn a fair margin.

Do you track and understand the signals your guests send you every day from their store purchases? How completely do you evaluate their promotional responses?

There are crucial insights embedded in POS transaction data that can be applied to the planning process to deliver a reliable forecast. Promotional and regular price purchases, basket composition, lift and interaction effects may be detected in aggregate data and used to set the right promotional depth and timing.

A reliable forecast, powered by machine learning, lets you answer a host of promotion planning questions, like:

• What will the impact be on our total coffee sales if we use a hot price on a breakfast sandwich to drive morning visits?
• How early should our holiday weekend promotion begin?
• Is it better to cut the price on bagged ice and sell packaged beverages at full price, or keep the margins on ice but offer a great deal on drinks?
• Is our twofer price on packaged beverages helping our profits? What would the impact be if we upped the deal by 10 cents?

Retail pricing optimization for convenience retailers goes beyond “mark it down and hope for the best.”

A modern pricing strategy uses:

• Machine learning demand models to forecast unit lift and profit impact.
• Price elasticity analysis to determine optimal discount depth.
• Interaction effect modeling to measure how one item's promotion affects related items (for example, breakfast sandwich and coffee sales).
• Basket analysis to see the total transaction impact.
• Scenario testing to compare promotional alternatives before you spend the margin.

Intuition is not the answer

When a price promotion is designed with a “volume at any cost” goal, the results are predictable, but not in a good way: An early bump in volume, too often ending with an out-of-stock situation before the next order is delivered and with a negative impact on gross margin.

It is essential to use analytics to identify which items are worth promoting and which will actually have a negative impact on margins. Defining depth and duration are crucial aspects of the planning process.

Modeling response with an understanding of price elasticity and interaction effects will help with setting the effectiveness of promotions, and determining what discounts will deliver the best overall response.

Convenience stores have an opportunity to improve promotion planning and performance by substituting an advanced pricing solution for simple lift analysis and old-style intuition. Machine learning can enable deeper understanding of customer response and the design of offers that deliver desired sales lift without sacrificing profits.