Term explanation table

Sources

Location

The Locations source gives you a full overview of your store or building’s external traffic.

Example: If there are multiple entrances (e.g., three), this source will show the total number of visitors across all of them combined.

The Zone source is used for specific internal areas or custom combinations of sensors within your location. It also includes specialized data such as Adult/Children segmentation if this feature is enabled in your solution.

Example: If you have a sensor placed at the checkout area, it will appear under Zone sources.

Use the Entrances source to see visitor data broken down by individual entrances—ideal if your location has more than one entry point.

Example: If you have 3 entrances and you want to know which of these 3 entrances is being used the most, you need the "Entrances source" to get the individual number for each entrance.

The Groups source is used when you’ve categorized multiple locations and want to view combined data across those grouped locations.

Example: If you have 50 locations across two countries, you can create two groups—one for each country. This allows you to select all 25 locations in Country A with a single click, instead of selecting them individually under the Location source.

The Tags source is used when you’ve categorized multiple locations and want to view the individual data for each location within that category.

It functions similarly to the Groups source—with the key difference being that Groups provide a combined total, while Tags return separate results for each location in the group.

Footfall

The Visitors Inside metric shows the number of visitors present inside your store or building during the selected time period.It is calculated by subtracting the number of exits from the number of entries within the chosen timeframe.

Use Case: Generate an hourly report for a specific day to identify when your store had the highest in-store occupancy.

The Visitors Entering metric shows the number of people who entered your store or building during the selected time period.

The Visitors Exiting metric shows the number of people who left your store or building during the selected time period.

The Capture Rate metric measures the percentage of people passing by your store and who actually enter it.It requires one sensor to count passersby and another to count entries.

Example: If 80 people walk by and 20 enter, your capture rate is 25%.This metric is especially useful for evaluating the effectiveness of window displays or storefront promotions.

The Visitors per m² metric shows how many visitors entered your store or building relative to its size.To enable this metric, you’ll need to enter the total square meters (m²) of your location in the system settings.

Sales
*Requires integrated sales data*

The Turnover metric displays the total revenue generated during the selected time period, based on your integrated sales data.

The Staff Hours metric shows the total number of hours worked by staff during the selected time period.

If no data appears when this metric is selected, your integration may not include staff hour data by default.

It can help you evaluate how staffing levels relate to key performance indicators like conversion rate or turnover.

Tip: A consistently low conversion rate might indicate understaffing or a need for better customer engagement.

The Average Basket Size is calculated by dividing the total number of products sold by the total number of transactions during a given period.

Example: If 50 products were sold across 10 transactions in one day, the average basket size would be 5.

This metric provides insight into customer purchasing behavior. A consistently low basket size (e.g., 1–2 items) may signal missed opportunities for upselling or bundling complementary products.

The Conversion Rate metric shows the percentage of store visitors who completed a purchase during the selected time period.

Example: If 100 people visited your store in a day and you recorded 5 transactions, your conversion rate would be 5%.

This metric is essential for evaluating how effectively your store converts foot traffic into paying customers.

The Turnover per m² metric is calculated by dividing your total turnover by the size of the selected store or location (in square meters).

Example: If your store is 100 m² and generates €10,000 in daily revenue, the turnover per m² is €100.

This metric helps assess how efficiently your retail space is being utilized in relation to revenue performance.

The Average Turnover per Transaction metric is calculated by dividing the total turnover by the number of order transactions during the selected period.

Example: If your turnover is €10,000 and you have 100 transactions in a day, the average turnover per transaction is €100.

This metric helps assess the average value of each sale and can indicate purchasing behavior or pricing strategy effectiveness.

The Average Turnover per Visitor metric is calculated by dividing the total turnover by the number of visitors during the selected time period.

Example: If you had 50 visitors and generated €10,000 in turnover, the average turnover per visitor would be €200.

This metric provides insight into the average revenue contribution of each visitor and can help assess the overall effectiveness of your in-store customer experience and sales strategy.

Queue

The Average Wait Time metric calculates how long, on average, visitors spend in a selected area or zone during the chosen period.

Example: If two people waited for 2 minutes and one person waited for 5 minutes, the average wait time would be 3 minutes.

The interpretation of this metric depends on the sensor placement:

• At a checkout, it reflects average queue duration.

• At a product zone, it can indicate customer interest or engagement with the displayed products.

The Visitors in Queue metric shows the number of individuals currently waiting in a designated queue area, such as at a checkout or service point.

This value reflects real-time or time-specific data, depending on the selected reporting period. It helps you monitor queue build-up and assess peak traffic moments that may require additional staff allocation.

The Average Visitors in Queue metric calculates the average number of people present in a queue during the selected time period.

This is useful for understanding typical queue length throughout the day and identifying trends that may affect customer satisfaction or operational efficiency.

The Calculated Wait Time metric estimates how long, on average, visitors spend waiting in a queue.

It is based on tracking individual time spent within the defined queue area and provides a data-driven view of customer experience and service speed.

This metric can help you evaluate whether staffing levels and checkout processes are aligned with customer demand.

Demographics

The Estimated Age metric provides an approximate age range of visitors detected by sensors with demographic analysis capabilities.

This data is typically categorized into predefined age brackets (e.g., 0–17, 18–34, 35–54, 55+) and is based on anonymized visual attributes captured at entry points or specific zones.

It offers valuable insights into the age distribution of your visitor base, helping you tailor marketing strategies, product placement, and in-store experiences to better match your audience demographics.