How a US Coffee & Dessert Franchise Benchmarks Five Competitors' Menu Prices at the Stores Nearest Its Own — Every Quarter, Automatically

100+

own locations, each with a mapped competitor set

5 brands

benchmarked on a fixed item basket

Quarterly

refresh with QoQ price-movement report

Client: Regional US doughnut & coffee franchise, 100+ locations (name withheld)

Industry: QSR / Food Retail

Use Case: Localized competitor menu-price benchmarking

Competitor Set: Dutch Bros, 7 Brew, Starbucks, Crumbl, Nothing Bundt Cakes

Delivery: Quarterly Excel workbook + trend dashboard

About the Client

Our client operates a fast-growing doughnut and specialty-coffee franchise with more than 100 locations across several US states. Like every QSR operator navigating post-inflation menu economics, its pricing team faces the same quarterly question: can we take price on lattes, cold brew and dozens — and where?

The honest answer depends on the competition — not nationally, but at the corner. A latte's "market price" in suburban Ohio is set by the Dutch Bros and Starbucks a mile away, not by a national average. The client needed competitor pricing with exactly that geometry: the specific competitor locations nearest each of its own stores.

The Challenge

• Location-specific pricing, at franchise scale. Chains like Starbucks and Dutch Bros vary menu prices by location. A national scrape would produce averages the pricing team explicitly didn't want. The requirement: for each of 100+ own stores, price the nearest relevant location of each of five competitor brands.
• A comparable basket across incomparable menus. A doughnut chain, two drive-thru coffee brands, a cookie brand and a bundt-cake brand don't share a menu. The team needed a fixed, limited basket — core lattes, cold brews, signature items, comparable dozen/box equivalents — matched consistently enough to trend over time.
• Manual collection had already failed. Franchise business managers had tried spot-checking competitor apps quarterly. It consumed days, covered a fraction of locations, and produced inconsistent item matches nobody fully trusted.
• Quarterly cadence, boardroom output. The deliverable had to land before each quarterly pricing review, in a format finance could open — not a raw JSON dump.

The Actowiz Solution

1. Store-Pair Mapping

We geocoded the client's 100+ locations and, for each, resolved the nearest operating location of each competitor brand (with distance caps, so a competitor 40 miles away doesn't pollute a local benchmark). The result: a maintained map of ~500 own-store ↔ competitor-store pairs, refreshed each cycle as stores open and close.

2. Location-Anchored Menu Collection

Each quarter, menu prices are captured from the competitor's ordering channel for that specific store — the same price a customer ordering at that location sees. No averages, no default-store shortcuts.

3. A Locked, Versioned Comparison Basket

Working with the client's pricing team, we defined a fixed basket (~25 items across beverage sizes and bakery equivalents) with explicit matching rules — e.g., which competitor SKU counts as the "medium iced latte" equivalent. The basket is versioned: when a competitor renames or reformulates an item, the change is documented, not silently swapped — so quarter-over-quarter trends stay honest.

4. Boardroom-Ready Delivery

Each cycle ships as an Excel workbook — one tab per market, own-vs-competitor gaps highlighted, QoQ movement flagged — plus a dashboard view for the pricing team. The headline sheet answers the only question leadership asks: where did competitors move, and where do we have room?

The Results

• ~500 competitor store-level price sets collected per quarter — full network coverage that manual spot-checks never achieved on even a tenth of locations
• Days → hours of franchise managers' time per quarter reclaimed; collection and matching are fully automated, review takes one meeting
• Market-level moves — the first cycle revealed competitor price increases concentrated in specific metros — letting the client take price selectively in those markets while holding elsewhere, instead of a blunt national increase
• Trusted trends — versioned basket matching ended the "are we even comparing the same drink?" debate — QoQ deltas are now accepted directly into the pricing model

Client Feedback

"Our old process was three people, two weeks, and a spreadsheet nobody believed. Now the benchmark shows up before the pricing meeting, covers every store, and the arguments are about strategy — not about the data."

— VP of Operations, US Coffee & Dessert Franchise

Why It Worked

• Geometry over averages. Nearest-competitor pairing made the data match how pricing power actually works in QSR — corner by corner.
• A small basket, ruthlessly consistent. Twenty-five well-matched items beat two hundred loosely matched ones. Versioned matching rules are what made trends trustworthy.
• Deliver into the existing ritual. The output was designed for the quarterly pricing review that already existed — adoption required zero process change.

Benchmark Your Competitors — At the Stores Next to Yours

Send us your store list and competitor brands. We'll map the store pairs and deliver a free single-market pilot benchmark so your pricing team can judge the match quality first.

Frequently Asked Questions

Can this run monthly or weekly instead of quarterly?

Yes — cadence is configurable. QSR pricing reviews are typically quarterly, but promo-heavy periods (holiday menus, LTOs) often warrant a monthly or ad-hoc cycle on a subset of items.

Can delivery-platform prices (DoorDash, Uber Eats) be included?

Yes, and it's often revealing — marked-up marketplace prices vs first-party app prices are tracked as separate channels per store, so you see both the shelf price and the delivery price.

What about markets where a competitor has no nearby store?

Distance caps mean those pairs are recorded as "no local competitor" — itself a useful signal for pricing power — rather than filled with a misleading faraway price.

Is menu-price scraping compliant?

We collect only publicly displayed menu and pricing information — what any customer sees when ordering — with no accounts or personal data, under Actowiz's responsible-scraping framework.

Conclusion

For QSR franchises, pricing power isn't national—it's local, determined by the competition at each individual street corner. By anchoring competitor data to specific store pairs, using a tightly versioned basket, and delivering insights in the format leadership already uses, Actowiz transformed a manual, error-prone quarterly ritual into a trusted, automated benchmark. The result is a pricing strategy that's both data-driven and operationally seamless, giving the client confidence to make selective moves in a volatile market.