C-Store: Promote Smarter and More Profitably with Effective Forecasting, Modeling, and Design

“We lose a nickel on every one we sell,” goes the well-worn quip about retail promotion, “but we make up for it in volume.”

📂 File that one in the category of “funny if it’s not you.”

For the rest of us, it’s a concern faced with every promotional plan… sure, we can drive volume with a sharp price, but

• How will our category profitability be affected?
• Will it cannibalize complementary or related items?
• What signal will it send to our shoppers about our regular prices?

A couple of years ago, a leading c-store tested a flatbread breakfast sandwich that looked like a stunning success… at first.

The breakfast sandwich was priced below most other breakfast options, and it sold – well – better than hotcakes.

But analytics told a different story: in test stores, average transaction dollars were down, and sales of higher-margin items declined. The sharp price drew traffic, but cannibalization and lower margins erase the benefit. The data led the company to decide against rolling ito ut.

Lesson: Promotions need to be planned and evaluated not only in terms of the lift they generate, but also in terms of how they affect the whole store through interaction effects. This isn’t as hard as it sounds, if you use the right pricing tools and approach.

What Makes C-Store Pricing Challenging

Convenience retail pricing is uniquely complex because of:

High traffic, small baskets: The margin impact of small pricing missteps adds up quickly.

Diverse product mix: foodservice, packaged beverages, tobacco, snacks, and fuel all have different elasticity patterns.

Seasonality & daypart variations: Morning coffee traffic vs. afternoon snack runs require different pricing strategies.

Competitive proximity: C-stores often compete with other convenience locations, QSRs, and grocery within a few hundred yards – making pricing a very important differentiator.

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

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.

With an average transaction count of 1,100 shoppers per store per day (NACS), every visit presents 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 c-stores and fuel retailers goes beyond “mark it down and hope for the best.” A modern 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 (e.g., 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.