AI offers the opportunity to close the loop between what corporate plans and what guests actually encounter.

The most difficult stretches of each year for QSRs are peak traffic seasons—summer break, back-to-school, the holidays at the end of the year. Guests show up en masse while corporate pushes new, limited-time offers. The stress put on individual outposts is real, and the gap between what HQ has planned and what guests actually see has typically been viewed as an unavoidable cost of doing business. With AI, that’s starting to change.

Closing the gap between corporate and the individual location

QSRs know there are communications breakdowns during these high-intensity promotional periods, which can lead to a disconnect between corporate and franchise operators. Despite all the advances in technology, many of these programs still run on rudimentary systems. At the beginning of a campaign, a corporate email arrives with a photo of what the promotional setup should look like. Managers, in turn, send a photo back to HQ of what they put up.

In between those two emails, though, a lot can happen. Materials are lost in transit. Window clings end up unopened in a stockroom. New employees may not even know about the promotion.

On top of all that, customers may learn about a new offer from a TV ad or billboard before location teams have been trained about the promotion, putting employees in difficult and awkward positions.

One photo and an AI model can do what the email chain never could. A team member can take a single image in the front of house, and then AI can identify whether the respective parts of the promotion have been executed: window cling, menu strip, register-area material. QSRs can be sure corporate knows the individual locations are matching the expected execution of the promotional plan.

What separates AI-driven execution from a simple email chain is what happens to the data after the photo is taken. When location-level execution data flows into a centralized system, corporate has a standardized record it can query at any point during the campaign window, not just when an above-store leader happens to walk into a location. Problems get flagged and corrected in real time, and above-store leaders can focus on doing the coaching and training rather than just doing the auditing.

Fixing pricing and messaging mismatches

Many QSR operators are starting to adopt digital menu boards, a technology for which it’s easy to see immediate benefits. Instead of having employees manually put up new panels, the mothership can make the change and see it reflected almost immediately.

But this creates another challenge during promotional periods. Specially printed materials—such as table tents, window decals, and register toppers—obviously can’t be updated in real time. A limited-time offer priced at $3.99 on the menu board but $4.29 on the table tent is the kind of mismatch team members inside the operation might not catch until a guest points it out at the counter.

AI image recognition gives operators a way to verify consistency across surfaces from a photo. It can confirm that the price on the menu board strip matches the price on the table tent, the window cling, and the register topper. And it can flag the surfaces that don’t match.

Turning execution data into smarter promotional planning

Most QSR chains judge promotional campaigns the way they always have: Did the limited-time offer hit its number or not?

But that sales number doesn’t tell the whole story. Consider a restaurant running an elaborate summer limited-time offer. HQ has developed and approved a full activation with window clings, menu board strips, table tents, drive-thru toppers, and register signage. If the chain doesn’t have location-level execution data, it doesn’t know if the window clings went up, if the menu board strips were installed, or if the table tents ever came out of the box. And that means the chain doesn’t really know what accounted for the sales lift.

When execution data is collected location by location and analyzed alongside restaurant-level sales data, operators gain real insight. Perhaps the summer LTO drove a sales lift, but only in the small number of locations where the signage went up on time. Maybe the drive-thru toppers dramatically outperformed the register signage. And maybe the table tents were a waste of time and money because certain locations never took them out of the box. This kind of granular intelligence allows the QSR to plan more effectively for future promotions.

A short window with real consequences

Peak seasons are unforgiving for all QSRs. An eight-week LTO that takes two weeks to set up has already squandered a quarter of its window—and, almost certainly, a meaningful chunk of revenue. Without location-level insights, corporate is unable to make intelligent planning decisions for future campaigns.

Promotional execution is only one part of what determines whether a peak-season campaign lands—but promotional execution is the most measurable layer, and it’s where AI is making the most immediate difference for QSRs today. For QSR operators preparing for the next peak season, AI offers the opportunity to close the loop between what corporate plans and what guests actually encounter—in a time frame that’s fast enough to make a difference that really matters.

Scott Lasher is FORM’s Strategic Account Director, where he helps restaurant, retail and CPG organizations improve in-store execution using AI-powered field technology, image recognition and real-time operational insights.

Source https://www.qsrmagazine.com/story/how-ai-is-helping-qsrs-improve-promotional-execution-during-peak-traffic-seasons/