TL;DR
- You can grow your check average without touching a menu price by making sure every guest hears a good suggestion.
- The steps: know your baseline, suggest on every order, match the suggestion to the daypart and promotion, keep it helpful, and measure the lift honestly.
- Hi Auto’s system can offer an upsell on every order where the operator enables it, and Hi Auto reports about a 1.5% average ticket lift per store (1-2% range).
- Start with your own numbers, one store at a time.
Picture a busy Thursday afternoon. A guest pulls up to the speaker, ready to order. The person on the headset is also handing off a bag, taking a payment and answering the kitchen. The suggestion never gets made, and nobody forgot. There was simply no room in the moment.
That gap is where your average ticket size quietly slips, order after order and shift after shift. Raising prices is the visible fix, and guests notice it. The quieter fix is to make sure every guest is asked.
This guide is a how-to for multi-unit operators. It walks through five steps you can act on in the lane, without touching the menu board.
What to Have in Place First
Three things need to be working before you start.
- A POS system that records transaction-level data. You need check averages by store and by daypart. Without them you are guessing at your baseline.
- A drive-thru audio system that works. The order point is where the suggestion happens, so a speaker or headset that cuts out limits everything else.
- Reliable internet. A cloud-based ordering system depends on it.
Your menu stays yours. New menu items and limited-time offers are set up by the Hi Auto team, and the lead time depends on complexity, generally about one week. Corporate holds brand standards, and the steps below work inside that structure.
On timing, a Hi Auto pilot can be live in about 8 weeks, and the path from pilot to scale runs 90-120 days. To see what an AI order taker does before you commit, start with the Hi Auto product page and our post on the marketing power of drive-thru AI order takers.
Step 1: Know Your Baseline Before Changing Anything
You cannot measure a lift if you do not know where you started. Before you change scripts, promotions or technology, pull two things from your POS.
Your check average by store and by daypart. Breakfast, lunch peak, afternoon and dinner behave differently. A blended daily number looks tidy and hides where the gap lives.
How often an add-on is offered and accepted. Your upsell conversion rate is the number to watch as you make changes. It shows whether the opportunity is in how often you ask, how well you ask, or both.
Pull at least four weeks of data. One strong week or one slow week will skew the picture, and four weeks covers a holiday weekend, a quiet Tuesday and a local event. Write the numbers down by store and by daypart. Everything that follows is measured against them.
Step 2: Make the Suggestion Happen on Every Order
Consistency is the lever. A person on a headset during a lunch rush is taking the order, handing off food, processing payment and keeping the kitchen moving. Suggesting on every single order, across every shift, is difficult in that seat. It is a limit of the job design, not a question of effort or skill.
Ryan Weaver, CEO of Lee’s Famous Recipe Chicken, described the difference on the Nift Marketing Bites: Restaurant Growth Unwrapped podcast:
“You might get an upsell 20% of the time with humans in the Drive-Thru. With Hi Auto, our voice order taking partner, we’re getting it 100% of the time.”
Ryan Weaver, CEO, Lee’s Famous Recipe Chicken. Nift Marketing Bites: Restaurant Growth Unwrapped podcast, Episode 32, 1 April 2025. Watch Ryan Weaver on how often upsells are offered
Hi Auto’s AI Order Taker handles the ordering conversation, and its system can offer an upsell on every order where the operator enables it. That is the operator’s choice, because an upsell does not always make sense. The 100% figure above is Lee’s own, from Ryan Weaver’s quote, and it belongs to Lee’s.
The useful question is how to make the suggestion happen on every order, no matter who is on shift or how deep the queue gets.
Step 3: Match the Suggestion to the Daypart and the Promotion
A suggestion that fits the moment feels like good service. A suggestion that does not fit feels like noise.
A breakfast guest ordering coffee and a sandwich is in a different conversation from a dinner guest ordering for the family. The add-on that suits 7 AM is rarely the one that suits 6 PM. If a limited-time offer is running, it should come up at the speaker and not only sit on the menu board.
Jose Armario, CEO of Bojangles, described how his team uses this:
“You can set BoLinda to suggest to sell by day part, by promotion… She’s very consistent and very reliable.”
Jose Armario, CEO, Bojangles. QSR webinar AI Order Taker That Works: The Bojangles Model, July 2025. Watch Jose Armario on setting suggestions by daypart
This is where daypart awareness and just-in-time upselling work together: the suggestion arrives at the right point in the conversation and fits what the guest ordered. The system considers multiple factors, such as order context and time and space, and backs off automatically.
Step 4: Keep It Helpful, Not Pushy
A suggestion that feels like pressure sours the guest relationship. What separates a suggestion that lands from one that gets tuned out is timing, relevance and how easy it is to decline. If it fits the order, comes at the right moment and is easy to wave off, most guests take it well.
Automated back-off logic is part of that. The system backs off on its own, so the guest does not feel pushed and the line keeps moving.
We cover the craft of natural-sounding suggestions in a separate piece, “How to Make AI Upselling Feel Natural, Not Pushy, at the Drive-Thru.”
Step 5: Measure the Lift Honestly
A modest ticket increase per store can sound small. Multiply it across your whole chain before every store is live and you have a number that does not exist yet. Measure the way you would want a vendor to.
- Compare like for like. Set a pilot store against a similar non-pilot store in the same trade area, not against the chain average. Different trade areas carry different traffic, and those differences can swamp a small ticket signal.
- Run it for several weeks, not days. Week-to-week swings in a drive-thru lane are wide enough to hide a real gain or invent a false one.
- Keep the numbers per store. Do not multiply a per-store result across your store count.
On what to expect, Hi Auto reports an average ticket increase of about 1.5% per store, within a 1-2% range. Ryan Weaver gives his own estimate from his own stores:
“The technology pays for itself with the labor hours we are able to take out of our budget every week, and then the sales lifts, which we think is about 1-2% is just kind of gravy on top of that.”
Ryan Weaver, CEO, Lee’s Famous Recipe Chicken. Nift Marketing Bites: Restaurant Growth Unwrapped podcast, Episode 32, 1 April 2025. Watch Ryan Weaver on labor hours and sales lift
Jose Armario described the check impact at Bojangles:
“We’ve seen a healthy increase in average check as well because she’s one of the best salespeople on the team with order taking or suggestive selling.”
Jose Armario, CEO, Bojangles. QSR webinar AI Order Taker That Works: The Bojangles Model, July 2025. Watch Jose Armario on the lift in average check
Results vary by store, and these figures are each customer’s or Hi Auto’s own. Use them as a starting point for your own measurement.
Every Guest Asked
Your check average grows when more guests are asked. Training, scripts and incentives help at the margin, and a person managing food, payment and the window during a rush still has limited room to suggest on every order. Making the suggestion happen on every order, every daypart and every shift is a quieter and more durable way to grow the check than raising prices.
Next step: run your own per-store numbers
Run your own per-store numbers, using your own check average and store data. If the numbers make sense, talk to the Hi Auto team about a pilot at one or two stores.
