Multi-location business dashboard: KPIs, layouts and data
A multi-location business dashboard puts the same measures for every site in one view, drawn from the systems each site already runs on, so you can compare sites, spot the one falling behind and drill into why. The hard part is not the charts. It is agreeing the definitions and joining the data.
- Every site in one view
- Ask in plain English
- Purple WiFi footfall included
Proof
- 80,000+
- venues on Purple, in 90 countries
- 500 million
- logins across the Purple network a year
- 2 million
- people onboarded every day
- 1,000+
- connectors
Purple runs WiFi and guest data for brands such as McDonald's, JPMorgan, Whitbread, Premier Inn, SoFi Stadium, Pizza Express, Stonegate.
What is a multi-location business dashboard?
It is one view of the same measures for every site, built on the systems each site already uses. It lets a head office compare sites like with like, a regional manager find the outlier in seconds and a site manager see their own week, all from one set of definitions.
A single-site dashboard answers "how are we doing?" A multi-location dashboard has to answer a harder question: "how are we doing compared with everywhere else, and why?" That second question is what makes it a different thing, not just a bigger one.
It has to hold up when the sites differ. A city-center restaurant and a motorway-services one trade differently, so a dashboard that ranks them on takings alone tells you where the sites are, not how well they are run. It has to hold up when the systems differ too: one site may run on a newer till, another on a different bookings system after an acquisition. And it has to carry definitions that every site manager recognises, or the first meeting about it turns into an argument about whose number is right.
Source systems
Tills, bookings, schedule, finance, WiFi footfall
Then: One list of sites
One list of sites
A stable ID for every location
Then: Measure definitions
Measure definitions
One card per measure
Then: Three role views
Three role views
Head office, region, site
Then: Decisions and questions
Decisions and questions
Ask, drill down, ask again
Why do most multi-site dashboards disappoint?
Because the charts were built before the definitions were agreed and the data was joined. The usual causes are measures that mean different things at different sites, no common site ID, stale exports and a view with too many charts. Each has a specific fix.
The pattern is familiar. A dashboard is commissioned, a first version looks good in the demo, and within a quarter nobody trusts it. Site managers keep their own spreadsheets. Head office asks for a "proper" report. The dashboard is not wrong in a way anyone can point at, which is exactly why it is abandoned: nobody can tell which number to believe.
Nearly every cause is upstream of the chart.
| What goes wrong | What you see | The fix |
|---|---|---|
| The same word means different things | Two managers quote different 'sales' for the same week | One definition card per measure, owned by one person |
| No common site ID | A site appears twice, or not at all, after a rename | One list of sites with a stable ID every source maps to |
| Stale exports | Last week's numbers on this week's screen | Connect the live systems and show the refresh time |
| Too many charts | Nobody knows where to look | Eight to twelve measures, three views, one question per chart |
| Sites compared that are not alike | A small site is always bottom of the table | Peer groups and normalized measures |
| No route to the cause | The chart says 'down', not why | Drill from site to day to source |
| Built once, never changed | The view no longer matches the business | Change it by asking, not by raising a ticket |
Which KPIs belong on a multi-location dashboard?
Start with eight to twelve measures that answer decisions you actually make: sales like for like, labor cost as a share of sales, spend per head, visits and conversion, booking fill and no-shows, space use, on-time checks and customer feedback. Add a measure only when a decision needs it.
A KPI earns its place by changing a decision. If nobody would act differently whether it rose or fell, it is decoration. The measures below cover what most multi-site hospitality, retail and leisure operators decide on weekly. Adapt the list to your business, but resist growing it: every extra measure dilutes the ones that matter.
| Measure | Formula | Source | Cadence | Decision it informs |
|---|---|---|---|---|
| Like-for-like sales | Sales this period ÷ sales in the same period last year, for sites trading in both | Till | Weekly | Which sites are really growing |
| Labor cost percentage | Labor cost ÷ net sales × 100 | Schedule or payroll, till | Weekly | Whether staffing follows demand |
| Spend per head | Net sales ÷ covers or transactions | Till | Weekly | Pricing, upselling, mix |
| Visits | Count of people through the door | WiFi footfall, door counter | Daily | Staffing and opening hours |
| Conversion | Transactions ÷ visits × 100 | Till, WiFi footfall | Weekly | Whether visitors become customers |
| Dwell time | Average time on site | WiFi footfall | Weekly | Layout, queueing, experience |
| Booking fill | Booked covers ÷ capacity × 100 | Bookings | Daily | Where to push demand |
| No-show rate | No-shows ÷ bookings × 100 | Bookings | Weekly | Deposits, reminders, overbooking |
| Space use | Occupied time ÷ available time × 100 | WiFi footfall, bookings, calendar | Weekly | Rooms, desks, floor space |
| Checks on time | Checks completed on time ÷ checks due × 100 | Checklist app | Daily | Compliance and standards |
| Staff hours vs plan | Hours worked ÷ hours planned × 100 | Schedule, time clock | Weekly | Overruns before payroll |
| Customer feedback | Average score and count | Reviews, surveys | Weekly | Where service is slipping |
WiFi footfall counts the devices Purple sees, which is a strong sample of visitors rather than a headcount. Use it for trends and comparisons between sites, and a door counter where you need an exact number.
How do you define a measure so every site means the same thing?
Write one definition card per measure: the name, the formula, the exact source and field, what is included and excluded, the time basis, the owner and the refresh. Publish the cards next to the dashboard. Disagreements then become edits to a card, not arguments in a meeting.
This is the work that decides whether a dashboard survives. A definition card is short, but it forces the decisions that otherwise get made differently at every site: does "sales" include tips, delivery, service charge? Is a week Monday to Sunday or Thursday to Wednesday? Does labor cost include employer on-costs? Is a canceled booking a no-show?
Keep one owner per card. When a definition changes, the owner changes the card and the change date is visible, so a chart that shifts after a change is explained rather than mysterious.
| Field | Example: labor cost percentage | Why it matters |
|---|---|---|
| Name | Labor cost percentage | One name, used everywhere |
| Formula | Labor cost ÷ net sales × 100 | Removes argument about arithmetic |
| Source and field | Schedule: paid hours × rate plus on-costs. Till: net sales | Names the system, not the department |
| Includes | Employed and agency staff; employer on-costs | Stops sites leaving costs out |
| Excludes | Head office salaries; tips passed to staff | Stops estate costs distorting a site |
| Time basis | Monday to Sunday, site local time | Makes weeks comparable |
| Owner | Finance director | One person decides changes |
| Refresh | Daily by 06:00 | Sets what 'current' means |
What should each role's view show?
Three views from one set of definitions. Head office compares sites and finds the estate-level story. A regional manager sees the outlier among their sites and the cause. A site manager sees their own week against their usual week and their peers. Same numbers, different questions.
Three views, one set of numbers
Head office
Asks: which parts of the estate are ahead or behind, and is it the same story everywhere?
- Like-for-like sales and labor cost percentage, by region and peer group
- A ranked list within each peer group, not across unlike sites
- The board pack figures, so the pack and the dashboard never disagree
Regional manager
Asks: which of my sites needs a visit this week, and what is behind it?
- Sites outside their usual range, flagged with the likely cause
- Drill from site to day to source, such as a till or a booking
- Compliance and checks overdue across the region
Site manager
Asks: how is my week going, and what should I do about it today?
- Today and the week so far against the usual week
- Bookings and expected visits for the next three days
- Staffing against the plan, and checks due
Permissions matter here. A site manager should see their own site and their peer group's averages, not another site's takings. Purple Build respects the permissions of the person looking, so people see the data they are allowed to see and nothing more, without a separate dashboard per audience.
What data do you need, and how do you join it?
Sales from the till, demand from bookings, labor from the schedule or payroll, visits from WiFi footfall or a door counter, and costs from finance. Join them on one stable site ID and one date basis. Keep a single list of sites that every source maps to.
Joining is where multi-site data goes wrong. Each system names the same site differently: "Leeds Central", "LEEDS-01", a venue code, a postcode. The reliable fix is a single list of sites with a permanent ID, and a mapping from each source's identifier to that ID. When a site is renamed, rebranded or moves, only the list changes, and every historical figure stays attached to the right place.
Dates need the same care. Decide the time zone, the week start and whether a trading day ends at midnight or after the last customer, and apply it to every source.
The sources come first: connect your business data covers the audit, the list of sites and the quality checks. Footfall and occupancy join the same dashboard, with their limits set out in venue data in decisions.
| Source | Take | Join on | Watch for |
|---|---|---|---|
| Till or point of sale | Net sales, transactions, items, voids | Site ID, trading date | Trading day that ends after midnight |
| Bookings | Covers booked, seated, canceled, no-show | Site ID, service date | Canceled versus no-show definitions |
| Schedule or payroll | Hours planned and worked, cost | Site ID, week | Agency staff held in a different system |
| WiFi footfall | Visits, dwell time, repeat visits | Site ID, date and hour | A sample of visitors, not a headcount |
| Finance | Rent, rates, utilities, budgets | Site ID, period | Periods that do not match the trading week |
| Checklists and reviews | Checks done on time, scores | Site ID, date | Free-text feedback needs grouping |
Questions and agents
Ask in plain English; an agent drafts the weekly pack
Role views
Head office, region, site, each with its own permissions
Measure definitions
One card per measure, one owner
Joined data
Every source on one site ID and one date basis
Source systems
Connected live, not exported
Can you ask a business dashboard questions in plain English?
Yes. In Purple Build you ask a question the way you would ask a colleague, such as which sites are behind their usual week, and get the chart. Then you ask the follow-up. Every answer reads the connected systems, so the figure is the figure in your till.
Fixed dashboards answer the questions someone anticipated. The questions that matter next week are rarely those. Asking in plain English closes that gap: you are not limited to the charts that were built, and you do not wait in a queue for the one you need.
What makes this trustworthy rather than a party trick is the data underneath. The answer comes from the connected systems and the agreed definitions, so "labor cost percentage" means the same thing in a question as it does on the dashboard, and any figure can be traced to the system it came from.
| Ask | Reads | Returns |
|---|---|---|
| Which sites are behind their usual week? | Till, footfall | Sites ranked by gap to their own usual week |
| Where is labor over plan this week? | Schedule, till | Sites with labor cost percentage above target |
| Which offices are used least, and on which days? | WiFi footfall, calendar | Space use by office and weekday |
| Show no-shows by site for the last four weeks | Bookings | A trend per site |
| Why was Saturday down in Leeds? | Till, bookings, footfall | Covers, bookings and visits side by side |
How do you benchmark sites fairly?
Group sites into peer groups by format and size, then compare within the group on normalized measures such as sales per cover, per opening hour or per square metre. Rank within a peer group, not across the whole estate, and always show each site against its own history as well.
A league table of unlike sites teaches the wrong lesson: the small site at the bottom learns it is small. Fair benchmarking does three things. It builds peer groups that share a format, a size band and, where it matters, a type of location. It normalizes, so the comparison is per cover, per opening hour or per square metre rather than absolute. And it always includes the site's own trend, because the most useful comparison is usually with itself.
Where a site is genuinely different, such as a new opening still ramping up, label it rather than excluding it. Excluding it hides the learning; labelling it keeps the data honest.
| Raw measure | Normalize by | Use when |
|---|---|---|
| Sales | Covers or transactions | Sites serve different volumes |
| Sales | Opening hours | Sites trade different hours |
| Sales | Floor area | Sites differ in size |
| Labor hours | Covers or sales | You are judging staffing |
| Visits | Catchment or capacity | Sites draw from different areas |
| Utilities cost | Floor area and opening hours | Comparing running costs |
What goes in a board or portfolio pack?
One page on the estate against budget and last year, one on the best and weakest peer groups, one on labor and costs, one on customers and space use, and one on risks and actions. Build it from the same dashboard figures, drafted by an agent and approved by a person.
A board pack fails when its figures differ from the dashboard managers use, because the first question in the room becomes "why is this different?" Build the pack from the same definitions and the same connected sources and the question disappears. Because the pack is repeatable, an agent can draft it on the schedule you set, with every figure traceable to its source, and the finance director approves it. The draft is waiting before the meeting is booked.
| Page | Shows | Measures | Source |
|---|---|---|---|
| 1. The estate | Performance against budget and last year | Like-for-like sales, spend per head | Till, finance |
| 2. Peer groups | Best and weakest within each format | Ranked within peer group | Till, bookings |
| 3. Labor and costs | Labor and running costs against plan | Labor cost percentage, hours vs plan | Schedule, finance |
| 4. Customers and space | Visits, conversion, space use | Visits, conversion, space use | WiFi footfall, till, calendar |
| 5. Risks and actions | Overdue checks, outliers, decisions needed | Checks on time, sites outside range | Checklists, all sources |
What does manual multi-site reporting cost?
Multiply the sites by the systems each one exports from and the minutes each export takes. At 40 sites, 4 systems and 10 minutes per export, that is about 27 hours a week. Connected sources and a five-minute check per site cut it to about 3 hours.
Manual reporting time calculator
Put in your own estate. Compare the weekly time to assemble the numbers by hand with a connected view that only needs checking.
- Hours a week assembling by hand
- 26.7 hours
- Hours a week checking a connected view
- 3.3 hours
- Hours returned each week
- 23.3 hours
- Hours returned a year
- 1,120 hours
Worked example. At the defaults, 40 sites each exporting from 4 systems at 10 minutes a time take 26.7 hours a week to assemble by hand. A connected view that needs 5 minutes of checking per site takes 3.3 hours, so 23.3 hours a week come back, or 1,120 hours over a 48-week year.
How the numbers are built
- Each export is a separate login, download and paste, which is how most estates assemble a weekly pack.
- Checking time is per site and includes spotting an odd figure.
- The calculation counts reporting time only. Faster decisions and fewer disputes are extra.
How do you build a multi-location dashboard, step by step?
List the decisions first, then pick the measures that inform them, write the definition cards, build the list of sites, connect the sources, create the three role views and run it alongside the old report for a month. In Purple Build you describe each view and it is created on your data.
Seven steps from blank page to trusted dashboard
List the decisions the dashboard must support
Write down the ten decisions a head office, region or site makes every week. If a decision does not appear, no measure for it belongs.
Check: Each decision has an owner and a day it is made.
Choose eight to twelve measures
Map each decision to the one or two measures that inform it. Use the table in this guide as a starting list and strike what no decision needs.
Check: No measure exists without a decision attached.
Write a definition card for each measure
Name, formula, source and field, includes, excludes, time basis, owner and refresh. Get the owner to sign each card.
Check: Two managers reading a card compute the same number.
Build the one list of sites
Give every location a permanent ID, with its format, size band, region and opening date, and map each source's identifier to it.
Check: Every source's sites map to exactly one ID.
Connect the sources
Connect the till, bookings, schedule, finance and WiFi footfall systems. In Purple Build this is one place and 1,000+ connectors, with Purple WiFi data a first-class source.
Check: A figure on the dashboard equals the figure in the source for the same day.
Create the three role views
Describe each view in plain English: what the person asks, the measures they need, the comparison to show. Purple Build creates it. Say what to change and it changes.
Check: Each role can answer their weekly questions without opening another system.
Run it alongside the old report for four weeks
Compare the two line by line and log every difference with a cause. Retire the old report only when the differences are explained.
Check: The last two weeks show no unexplained differences.
The options side by side
| Route | Purple Build | Spreadsheets and exports | A separate BI project | Each system's own reports |
|---|---|---|---|---|
| One view across systems | Yes, 1,000+ connectors in one place | By hand, every week | Yes, once analysts model the data | No: one system each |
| Who builds a new view | The person who runs the site or estate, from a description | Whoever owns the spreadsheet | Analysts, from a brief | Fixed reports only |
| Definitions in one place | Yes, shared by dashboard, questions and agents | In people's heads | In the data model | Differ by system |
| Ask follow-up questions | In plain English | Build another sheet | Raise a request | Not possible |
| WiFi footfall as a source | First class, from Purple | A separate export | A custom feed | Only in the WiFi tool |
| Respects who may see what | Yes, per person | Whoever has the file | Configured by the BI team | Per system |
Purple Build runs on the same credits as Purple Engage, with a free tier.
Multi-location dashboards
All guides in this pillar
Each guide takes one question in this pillar all the way down, with the diagrams, tables and worked examples to act on it.
Dashboards that people use
KPI dashboards for multi-location operators, with the views each role needs.
Definitions and trust
One set of definitions, honest benchmarking across sites and labor cost by site.
Reporting jobs
Board packs, scheduled reports and plain-English questions on business data.
Multi-location dashboards
The terms, defined
- KPI
- A key performance indicator: a measure that informs a decision someone actually makes.
- Like-for-like
- A comparison of sites that traded in both periods, so openings and closures do not distort the trend.
- Definition card
- A one-page record of a measure's name, formula, source, inclusions, time basis, owner and refresh.
- Site ID
- A permanent identifier for a location that every source system maps to.
- Peer group
- Sites of a similar format and size, compared with each other rather than with the whole estate.
- Normalisation
- Dividing a measure by something that makes sites comparable, such as covers, opening hours or floor area.
- Labor cost percentage
- Labor cost divided by net sales, multiplied by 100.
- Drill-down
- Moving from a summary figure to the day, hour or transaction behind it.
- Conversion
- Transactions as a share of visits.
- Refresh
- How often a figure is updated from its source.
FAQ
Questions operators ask
What is a multi-location business dashboard?
It is one view of the same measures for every site, built on the systems each site already uses. It lets head office compare sites like for like, a regional manager find the outlier and a site manager see their own week, all from one set of agreed definitions rather than a stack of separate reports.
Which KPIs should a multi-location dashboard track?
Start with eight to twelve: like-for-like sales, labor cost percentage, spend per head, visits and conversion, booking fill, no-show rate, space use, checks on time, staff hours against plan and customer feedback. Each must inform a decision someone makes weekly. Add a measure only when a decision needs it.
How do I compare sites that are different sizes?
Build peer groups by format and size, then normalize the measure per cover, per opening hour or per square metre, and rank within each group. Always show each site against its own history as well. A league table of unlike sites mostly tells you which sites are small.
How do I get one number when each site uses different systems?
Keep one list of sites with a permanent ID, map each system's identifier to it, and write a definition card for each measure naming the exact source and field. Then join everything on the site ID and one date basis. Purple Build connects 1,000+ systems in one place to do the joining.
Can I ask my business data questions in plain English?
Yes. In Purple Build you ask a question the way you would ask a colleague, get the chart, then ask the follow-up. Answers read your connected systems and your agreed definitions, so a figure matches the one in your till and can be traced to the system it came from.
Can WiFi data be part of a business dashboard?
Yes. Purple WiFi footfall, visits and dwell time plug into Purple Build as a first-class source alongside tills and bookings. It is the one source that shows who came through the door. WiFi counts the devices it sees, so use it for trends and comparisons, and a door counter where you need an exact count.
How long does it take to build a multi-location dashboard?
The work is in definitions and the list of sites, which take days not months if the owners are named. Creating the views is quick when you describe them in Purple Build. Plan four weeks of running alongside the old report before you retire it, so every difference is explained.
How is this different from the dashboards inside my till or bookings system?
Those show one system. A multi-location dashboard joins them, so you can see till takings, bookings, schedule cost and footfall for the same site and week side by side. That is what lets you ask why a number moved, which no single system can answer on its own.
Who can see what on a shared dashboard?
Each person sees the data they are allowed to see and nothing more. In Purple Build a site manager sees their own site and peer averages, a regional manager sees their region, and head office sees the estate, all from one set of numbers rather than separate copies that drift apart.
What does Purple Build cost?
Purple Build runs on the same credits as Purple Engage, so one balance covers both, and there is a free tier to start on. You do not need any other Purple product. Waitlist members get early access first, with the systems they name connected first.
Where to go next
Sources and standards
- UK GDPR, Article 5: principles relating to processing
- ICO: data protection principles, including data minimisation
Reviewed on 9 October 2026.
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