Turn past projects into proof.
A remodel changes 30 things at once, from the layout, lighting and coffee bar to the pickup counter, seating, signage, coolers, checkout and flooring, and the client launches a new menu the same week, with a grand reopening and a new competitor nearby. That makes it super hard to answer: what did your redesign actually add? Before-and-after numbers count the opening rush and the new menu too, so prospects discount them: this store's 12 weeks after reopening ran 24% above the 12 weeks before construction. Causal inference gives each remodeled store a twin without your redesign, built from your client's stores that weren't remodeled. The twin allows us to isolate the effect of the redesign itself on your client's sales: the new menu shows in both lines, the construction weeks and the opening rush are set aside, and the gap that lasts after the rush fades, 9%, is your design's. Across all of your past projects, causal inference can even break down which sub-elements had the highest payout: the pickup counter added 5% and the coffee bar 4%, while seating added nothing measurable. Each of 14 past remodels gets its own read, and together they show a lift of 9%, paid back in about 3.5 years, so past work becomes proof new customers believe. Turn past projects into proof. Start by sharing 1 past client's sales export. Illustrative data.
What did your redesign actually add?
- A remodel changes 30 things at once.The layout, lighting, coffee bar, pickup counter, seating, signage, coolers, checkout and flooring all change, and the client launches a new menu the same week.
- Prospects discount before-and-after numbers.A before-and-after comparison counts the opening rush and the new menu too.
- Each remodeled store gets a twin.Causal inference builds a twin without your redesign from your client's stores that weren't remodeled. The twin allows us to isolate the effect of the redesign itself on your client's sales.
- The gap that lasts is yours.Construction weeks and the opening rush are set aside. The gap that lasts after the rush fades is your design's.
- See which elements paid.Across all of your past projects, causal inference can even break down which sub-elements had the highest payout, so past work becomes proof new customers believe.
What it takes
- Your client's weekly sales.Start with 1 past client's sales export.
- Remodel dates and scope.When each store closed and reopened, and what changed in it.
“Has this worked at stores like mine?”
That's the question your next prospect is about to ask. If you sell equipment, software, a food program or media into convenience stores, we answer it on your own installed base, and the number is yours to use.
What you get
- Start small.A single install at a single site is enough for the first read.
- The lift, with a range.We measure what the store sold with your product installed against what it would have sold had it never been installed, and put a range around the difference.
- A number you own.It's yours to put in front of your next prospect, and it comes from someone other than you.
- Room to grow.Add sites as your installed base grows. More sites give a tighter range.
Why convenience gets measured store by store
- Grocery and big-box retail measure through the loyalty ID.The ad reaches a named shopper, the purchase lands under the same ID, and the ad can be withheld from a matched group of shoppers to show what they would have bought had they never seen it.
- Convenience mostly can't.Loyalty capture is low, most transactions are anonymous, the basket is 1 to 3 items, and there's no e-commerce order to close the loop.
- So the store becomes the unit.We compare stores that have your product with matched stores that don't, whether it's a screen, a fixture or a food program.
- It's the only method available.In convenience, measuring by store isn't a preference for our method, and vendors that measure shopper by shopper can't serve this channel.