Dwell time and visit frequency: what they really tell you

Visit counts get the headlines, but two quieter metrics often explain more about a store: how long people stay and how often they come back. Both are easy to put on a slide and easy to misread. This post covers what MapDove reports, how the metrics relate, and what a “good” value looks like, which depends heavily on the category.

Visits, visitors and frequency

Start with the definitions:

• Visits: every recorded trip to the place in the selected date range.
• Visitors: the distinct people behind those trips.
• Frequency = visits ÷ visitors: the average number of trips per visitor in that range.

A coffee shop with 12,000 visits and 9,000 visitors over a month has a frequency of about 1.33. Nine thousand people came, and on average each came a little more than once.

The ratio has a property people often forget: it depends on the window. Over a single day almost everyone is a one-time visitor. Over a quarter, regulars pile up repeat visits. Comparing a store’s 7-day frequency with a competitor’s 90-day figure is meaningless. MapDove computes KPIs over the range you select, so compare like with like: same range length, same season.

In the demo dataset (synthetic, modeled from neighborhood-level parameters), each category has a typical visitor-to-visit ratio. Fitness has the most repeat behavior of the everyday categories at about 0.70 visitors per visit, or roughly 1.4 trips per visitor. Coffee sits near 0.78. Grocery, pharmacy and quick service land at 0.82–0.86. Destination categories like apparel, electronics and cinemas are near 0.9, meaning mostly single trips.

What frequency actually tells you

High frequency usually means habit. People keep coming to the gym, the commute-route café or the neighborhood grocer because it is convenient and part of a routine. That is valuable: habitual visitors are less sensitive to new competition and easier to forecast.

But high frequency with flat or falling visitors is a warning. Your base is loyal and shrinking, so the store depends on fewer people coming more often. Low frequency with rising visitors is the reverse. Reach is growing, maybe from a new residential development or a marketing push, and the open question is whether those people turn into regulars.

Read the two together:

Visitors	Frequency	Likely story
↑	↑	Healthy growth: new people are becoming regulars
↑	↓	Reach is expanding, loyalty untested (new trade area, promotion)
↓	↑	A loyal core but a shrinking base, often from new competition nearby
↓	↓	Real decline: look for a cause in the trade area

Dwell time: category first, store second

Dwell is the average minutes per visit. It is meaningless without a category benchmark. Typical dwell in the demo dataset ranges over two orders of magnitude:

• Under 15 minutes: fuel & convenience (~7), pharmacy (~12), quick service (~14)
• 15–35 minutes: coffee (~18), apparel (~26), grocery (~28), electronics (~30), home improvement (~34)
• 45–95 minutes: department stores (~45), full-service dining (~62), fitness (~65), shopping centers (~95)
• Long stays: entertainment (~140), hotels (overnight, measured in hours)

A café averaging 28 minutes isn’t “underperforming” against a restaurant at 62. It may be a work-from-café spot whose seats turn over slowly, which matters for capacity planning. The right comparisons are against the category average in the same city and against the store’s own history.

When dwell moves

• Dwell rising, visits flat: in dining or coffee, slower turnover. Check peak-hour capacity in the hourly heatmap. In grocery, possibly bigger baskets, a stock-up season or slower checkout.
• Dwell falling, visits rising: often a convenience shift such as a new pickup counter, a faster format or more grab-and-go trips. Good for throughput, but watch the basket size.
• Dwell falling at a shopping center: an early sign of weakening anchors. Visitors still come for one errand but no longer browse.

Pitfalls

• Averages hide mixtures. A fitness club’s 65-minute mean blends 30-minute lunchtime visits and 90-minute evening classes. Use the hourly view to separate them.
• Seasonality affects both metrics. Phoenix and Miami have strong winter seasons and Dubai peaks in the cool months. Year-over-year comparisons are safer than month-over-month.
• Dwell is not engagement. Long dwell at a pharmacy is more likely a queue than loyalty.

Practical takeaways

• Always state the window when quoting frequency, and compare equal-length ranges.
• Read visitors and frequency together. The four-quadrant table above covers most cases.
• Benchmark dwell within the category and city. Never compare it across categories.
• Pair dwell changes with the hourly heatmap to tell capacity issues from demand shifts.

The place page in Foot Traffic shows all four KPIs alongside the category benchmark. Definitions are in the core concepts docs and demo data notes.