Foot traffic and visit share: how to measure your competitors

What is visit share, and why does it matter more than the total?

Foot traffic, also called footfall or visitor traffic, is how many people come to a store or pass through an area. It is useful, but on its own it misleads: visits rise and fall with the season, paydays, or the weather, for you and for everyone else.

Visit share fixes that. It measures what portion of an area’s or a category’s visits each store captures. For example, if 32 out of every 100 pharmacy visits in an area go to your store, your visit share is 32%. That is an illustration, not real data.

The difference matters. If your visits dropped but the whole area dropped by the same proportion, you did not lose to anyone: the market shrank. If your visits held steady but your share fell, competitors are growing faster than you.

Where does foot traffic data come from?

There are several sources, and each one sees a different part of the picture. Ideally, you combine them.

Mobility data usually comes from location signals on phones whose users agreed to share their location with an app. Providers aggregate and anonymize those signals, then estimate visits to each place based on its footprint on the map (its geofence).

What are the limits of mobility data?

Knowing the limits keeps you from drawing the wrong conclusions. These are the main ones:

• It is a sample, not every person. It is designed for comparisons and trends, not for counting every visitor.
• It is not real-time data: it is processed and updated in periods. At Pulster, mobility data is updated every two weeks and business data every month.
• It does not identify individuals: it shows how groups of visitors move, not who each one is.
• Very small stores, stores right next to others, or stores on different floors of a shopping center are harder to measure.
• Coverage varies by country and city. Where the sample is smaller, figures are less stable.
• It shows what happened, not why.

Which metrics matter for competitor analysis?

The same visit data supports several indicators. These are the most useful for a retail chain:

• Visit share: what percentage of the area’s visits goes to your store and to each competitor.
• Repeat and lapsed visitors: how many visitors come back and how many have stopped coming.
• Cross-shopping: how many of your visitors also go to competitors, and which ones.
• Visitor origins: which areas your visitors come from, often shown as heat maps.
• Distance traveled: how far your visitors travel to reach your store.
• Visit patterns: which days and times bring the most visitors to your store and to competitors.
• Competitive dominance map: which areas you lead and which areas your competitors lead.
• Industry trends: how visits are shifting across your whole sector.

How do you turn the data into local marketing actions?

Data is worth something when it changes a decision. For local store marketing, it comes down to three questions: who is taking your customers, where, and when.

For example, a restaurant chain sees that a new competitor is gaining share on weekend afternoons, with visitors coming from a nearby residential area. The answer is not to cut prices at every store, but to run a campaign in that area, on those days and at those times. A simple process:

• Pick the stores that are losing share to a competitor.
• See what changed: which origin areas lost visits and which competitors gained them.
• Decide where and when to act: areas, days, and times, aligned with your marketing calendar.
• Choose the action: billboards on the routes from those areas, promotions during slow hours, or partnerships with nearby places that draw visits.
• Measure the result over the following months and compare it with similar stores that had no campaign.

The most common interpretation mistakes

The most common mistake is seeing causes where there are only coincidences. If your share drops the same month a competitor opens, that does not prove the competitor is the cause: it may be one of the drivers, and it is worth investigating together with your internal data.

Other frequent mistakes:

• Confusing visits with sales. More visits do not always mean more purchases, so check your receipts too.
• Comparing months without accounting for seasonality, paydays, or holidays such as Easter week and year-end.
• Drawing conclusions from a short period. One unusual week is not a trend.
• Comparing stores of different sizes or formats without adjusting for that difference.
• Reading mobility figures as an exact headcount instead of a sample.
• Forgetting that construction, a street closure, or a change in opening hours also moves visits.