The KPIs that actually matter for foot traffic
A traffic count on its own doesn't tell you much. Here are the metrics we build into every BoostBI dashboard, with the formula, a worked example, and why each one earns its place — plus a full sample dashboard so you can see exactly what you'd be looking at.
The Metrics
8 KPIs, explained with real numbers
Footfall (Total Visits)
Total number of unique entries counted in a given period.
Example: A store counts 1,284 entries on a Saturday, up from 1,050 the previous Saturday — a 22% increase.
Why it matters: It's the foundation every other metric is built on, and the simplest way to measure the real-world reach of marketing, location and seasonality.
Conversion Rate
Conversion Rate = (Transactions ÷ Visitors) × 100
Example: 1,284 visitors and 236 transactions gives a conversion rate of 18.4%.
Why it matters: Two stores can have identical footfall but very different sales — conversion rate shows you which one is actually turning visits into revenue.
Average Dwell Time
Average Dwell Time = Total time spent in-store ÷ Number of visitors
Example: Across 1,284 visitors totalling 299 hours in-store, average dwell time works out to about 14 minutes per visit.
Why it matters: Longer dwell time is generally linked to higher basket size in retail, and helps venues like museums or libraries measure genuine engagement.
Peak Hour / Peak Day
The hour or day with the highest recorded visitor count in a period.
Example: Traffic data shows 12pm–1pm is consistently the busiest hour, with Saturday the busiest day of the week.
Why it matters: This is the single most actionable number for rostering — it tells you exactly when to schedule more staff.
Bounce Rate (Quick Exits)
Bounce Rate = (Visits under a defined time threshold ÷ Total visits) × 100
Example: If 90 of 1,284 visitors leave within 30 seconds of entering, that's a 7% bounce rate.
Why it matters: A rising bounce rate can flag a problem — a closed department, poor first impression, or customers using the doorway as a shortcut rather than browsing.
Repeat Visit Rate
Repeat Visit Rate = (Visitors seen more than once in a period ÷ Total visitors) × 100
Example: A gym with 400 unique members and 1,600 total check-ins in a month has an average of 4 visits per member.
Why it matters: Especially useful for gyms, libraries and community venues where loyalty and regular attendance matter as much as first-time visits.
Staffing Efficiency Ratio
Staffing Efficiency Ratio = Visitors per rostered staff hour
Example: 1,284 visitors across 48 staff hours on a Saturday gives roughly 27 visitors served per staff hour.
Why it matters: Tracked over time, this shows whether rosters are keeping pace with demand, or whether a site is over- or under-staffed relative to actual traffic.
Zone Popularity Share
Zone Popularity Share = (Visits or dwell time in a zone ÷ Total store visits or dwell time) × 100
Example: The front display zone captures 34% of total dwell time, while the back-left corner captures just 6%.
Why it matters: Directly informs merchandising and layout decisions — move key promotions into high-traffic zones and investigate why cold zones underperform.
Metrics your whole team can actually use
See It In Action
A full sample BoostBI dashboard
Illustrative sample data for a single store, showing the layout and metrics a typical BoostBI dashboard view includes.
BoostBI — Burnley Flagship Store
Total Visitors
8,942
+12.4%
Conversion Rate
18.4%
+1.2pt
Avg. Dwell Time
14 min
+0.8 min
Peak Hour
12–1pm
Sat & Sun
Bounce Rate
7.1%
-0.9pt
Repeat Visit Rate
31%
+3pt
Traffic by Hour — Today
Weekly Trend — Visitors
Zone Heatmap — Dwell Share
Top Zones This Week
| Zone | Visits | Avg. Dwell | Conversion |
|---|---|---|---|
| Front Display | 3,420 | 3.2 min | 24% |
| Checkout Area | 2,910 | 4.8 min | — |
| New Arrivals | 2,105 | 5.6 min | 19% |
| Back-Left Corner | 640 | 1.1 min | 6% |
Alerts
- ⚠ Queue building at Checkout 2 — 6 people waiting, average wait 4.5 minutes.
- ✅ New Arrivals zone dwell time up 22% this week — promotion appears to be working.
- ⚠ Back-Left Corner traffic down 15% vs. last week — worth a layout review.
Sample data shown for illustration only. Your BoostBI dashboard reflects your own live sensor data.
Dashboard & KPI FAQs
Which KPI should I look at first?
Start with footfall and conversion rate together — footfall tells you how many people came through the door, and conversion rate tells you what percentage became customers. Together they usually point straight at whether a problem is a traffic problem or a store-experience problem.
Can I create my own custom KPIs or reports?
Yes. Beyond the standard KPIs shown here, BoostBI reports can be filtered and combined by store, zone, and time period to build the specific view your business needs, and scheduled to arrive by email automatically.
Do I need point-of-sale data connected to see conversion rate?
Yes — conversion rate requires both visitor counts and transaction counts. Once your POS data is connected to BoostBI, conversion rate and basket-size-related metrics are calculated automatically.
How often does the dashboard update?
BoostBI updates in real time as Nano sensors report data, so the KPI tiles and charts you see reflect what's happening in-store right now, not a report generated the next day.
Can different staff see different levels of detail?
Yes. Role-based access means a store manager might see just their site's KPIs, while an area or regional manager sees a rolled-up view across every location they're responsible for.
Want to see your own data in a dashboard like this?
Book a walkthrough and we'll build a sample BoostBI view around your actual store layout.