Multi-location dashboard for auto service businesses: what to roll up and how to use it

A multi-location dashboard for auto service should show every store on one screen, with the same handful of numbers for each: cars, sales, average ticket, labor percentage, missed calls, reviews and comebacks. The hard part is making the numbers comparable across stores and deciding who looks at what, and how often. A dashboard that tries to show everything gets opened twice and then ignored.

This post is about the rollup itself. If you want the formulas behind each quick lube number, our quick lube KPIs post covers them one by one, with a weekly scorecard.

Why one-store reports stop working

With two or three stores, an owner can read each store's POS report and keep the picture in their head. Somewhere around five or six stores that breaks down. The reports come in different formats, at different times, from different systems. One store's manager sends a spreadsheet on Monday. Another sends nothing until asked.

A rollup dashboard fixes three problems:

• Comparison. You see Store 4 next to Store 7 on the same scale, so the outlier is obvious.
• Timing. Everyone looks at the same week's data at the same time, so a bad trend gets caught in days instead of at month end.
• Accountability. Each manager sees their store against the group. That alone changes behavior.

What to roll up

Keep the top level to about eight to ten numbers. Everything else belongs one click down.

Volume and sales

• Cars or repair orders per day
• Total sales
• Average ticket or average repair order
• Sales per bay or per technician hour

Labor

• Labor hours worked
• Labor as a percentage of sales
• Cars or billed hours per labor hour

Customer acquisition

• Inbound calls, answered rate and missed calls
• Calls that turned into bookings, if your phone system tracks it
• New versus returning customers

Quality and reputation

• Star rating and new reviews this week
• Comebacks or warranty returns
• For car washes: active members, new members and cancellations

Exceptions

• Any store that moved more than a set amount against its own average
• Any store missing data this week

That last item matters. A store with no data looks like a store with no problems. The dashboard should flag missing feeds in red.

Making stores comparable

Raw totals mislead. A 6-bay store will always beat a 2-bay store on car count. A store in a dense suburb will always get more calls than one on a highway. Before you compare, normalize.

Also compare each store to itself. A store that's average for the group but 12% below its own last-8-week average needs a phone call. A store that's below the group but improving every week might need nothing.

Example: why normalization changes the story

Here's an example with two stores in the same week. The numbers are made up to show the effect.

Store A has the bigger car count and would top a raw ranking. Normalized, Store B gets more out of each bay and each labor hour. The question for Store A is whether its schedule is too heavy or its bays are slow, which is the kind of thing a raw total hides.

Where the data comes from

Most of the numbers already exist. The job is connecting them.

• POS or shop management system: cars, tickets, sales, services sold, ticket timestamps
• Payroll or time clock: labor hours and cost
• Phone carrier or VoIP: call volume, answered and missed calls, call duration
• AI phone agent, if you have one: call reasons and booking outcomes
• Google Business Profile: ratings and reviews
• Cameras: car counts, bay time and lane waits, if you use camera analytics
• Membership system (car washes): members, sign-ups, declines and cancellations

Expect at least one source to be awkward. Some POS systems export cleanly; others need a scheduled report emailed to an inbox and parsed. Ask any dashboard vendor exactly how they'll get data out of each of your systems, and how often it refreshes.

How owners and managers use it

Different people need different views. If everyone gets the same screen, it serves nobody well.

The owner: weekly, five minutes

The owner wants one page every Monday. All stores in rows, the core numbers in columns, red and green against target. The question is "which one or two stores need my attention this week?" If the page can't answer that in five minutes, it has too much on it.

The district or area manager: daily, store by store

A district manager runs four to eight stores and needs yesterday's numbers each morning. They want the exceptions first: the store that missed 30% of calls at lunch, the store with three comebacks, the store whose ticket dropped. Their view should let them drill into a single store, by day and by hour.

The store manager: daily, their store against the group

A store manager wants their own numbers, their rank in the group, and what changed since yesterday. Showing the rank matters. Most managers don't want to be the bottom line on the owner's screen.

A weekly rhythm that works

1. Monday morning: dashboard refreshes with last week's data.
2. Monday: the owner reviews the summary and flags one or two stores.
3. Tuesday: the district manager calls each flagged store, agrees on one fix.
4. Daily: store managers check their numbers and the district manager checks exceptions.
5. Next Monday: check whether the fix moved the number.

One fix per flagged store per week. A list of nine things to improve gets none of them done.

Common mistakes

• Too many numbers on the first screen. Move detail one click down.
• No targets. A number without a target is just a number. Set a target per KPI, even if it's "match our best store."
• Monthly refresh. By the time a monthly report lands, the problem is four weeks old.
• Ignoring data gaps. A broken feed should be flagged in red, never shown as zero.
• Building it all at once. Start with the POS and phone data, prove it's useful, then add labor, reviews and cameras.

Where Trilix fits

Trilix builds an executive dashboard for multi-location quick lube, tire, repair and car wash operators. It pulls POS, phone, camera and review data into one view, with each store side by side and exceptions flagged. If you're weighing a dashboard against other tools, our post on AI tools for multi-unit franchisees covers what to pilot first. Most operators start with the weekly owner scorecard and add views from there.