With the increasing heterogeneity of the retail customer base, analysing loyalty cards and voucher data alone is no longer sufficient to understand customers. However, this understanding is important in order to stock the right product range and perfect the shopping experience. That is why more and more retailers are turning to comprehensive customer analyses, which offer many opportunities for better customer understanding. With the data obtained, retailers can make business decisions in real time or strategically based on data. In this article, we present the most important advantages and key figures of customer traffic analysis.
Automated analysis of customer traffic
When measuring without technology, customers were previously counted and surveyed manually. However, this was very time-consuming and costly, which meant that customer analyses could only be carried out sporadically. Furthermore, the analysis was not available in real time, but had to be generated from the data over a period of weeks. Customer traffic can now be recorded using various technologies. Wi-Fi technology, Bluetooth Low Energy (BLE), Radio Frequency Identification (RFID), cameras and 3D sensors each offer advantages and disadvantages in different areas of application.
With cameras and 3D sensors, retailers can even track customers who do not have a smartphone with them. In addition, the tracking is particularly accurate, so it works very well even in difficult lighting conditions or at high frequencies. The technology with cameras and 3D sensors is also compliant with data protection regulations. This is because no personal data is stored. It is not possible to draw conclusions about individual customers.
Customer traffic analyses for data-driven decisions
A customer traffic analysis helps to determine the number of visitors to a store, identify the age and gender of visitors, find out the busiest times, and identify the most important customer routes. Without detailed knowledge of customer traffic, retailers tend to act blindly when making management decisions. The following metrics are particularly important:
People counting
Retailers determine visitor frequency in the store by counting people in the entrance area. Visitor frequency can also be measured separately for floors or defined areas. This enables more in-depth analyses. After processing and evaluation, the data provides an important basis for deriving further key figures and for business decisions. The key figure for visitor frequency obtained must be interpreted as accurately as possible by those responsible. It is important to understand the significance of people counting.
Peak times and off-peak times
For effective staffing, retailers need to know when visitor frequency is typically above or below average. The evaluation of peak and off-peak times in terms of visitor traffic primarily concerns the different times of day. However, a differentiated analysis is also appropriate with regard to seasons, promotional days or days of the week. A detailed customer traffic analysis provides the necessary results. This enables retailers to identify deviations in visitor traffic at an early stage and adjust staffing levels to the situation.
Demographic characteristics
Customer analysis can be used to determine more than just the number of people in front of the store or on the sales floor. It is also possible to learn more about the demographic structure of visitors and systematically evaluate their behaviour in the store. This includes, above all, age and gender. Furthermore, it is possible to record the group size of customers shopping together. With this knowledge, the product range can be even better adapted to the demographic structure of the customer base. Differences in gender and age structure between locations can also explain differences in performance.
Capture Rate
Every store wants to attract as many passers-by as possible to its sales area. Retailers can therefore measure the capture rate by measuring footfall in front of and inside the store. This is the ratio between visitors and passers-by. Changes in the capture rate provide information about the attractiveness of the shop window and the entrance area. As part of a customer analysis, it can also provide information about the success of advertising measures.
Walkways
When analysing customer traffic, typical walking routes are extremely important for understanding customer behaviour. The permanent recording of walking routes can only be done automatically. Retailers can use the data to take appropriate optimisation measures. If retailers know the main walking routes, they can position product groups in a targeted manner. It also makes sense to deploy more staff for customer advice in these areas. Retailers can adjust the routing, lighting and signage to encourage more customers to visit areas that have been less frequented in the past.
Length of stay
Customer analysis also makes it possible to measure the average length of time visitors spend in a store. This measurement can refer to the total area, but also to individual floors, departments and promotional areas. Knowing the average dwell time gives managers a metric for the success of optimisation measures.
Implement digital customer analysis
Analysing customer numbers, structure and behaviour has become increasingly popular in the retail sector. Large chain stores in particular are pioneers in customer traffic analysis, using it to make management decisions based on solid foundations. But smaller retailers are also successfully using retail analytics. The key figures obtained form the basis for data-driven store management. At Crosscan, we support you in selecting the right technology and implementing digital customer analysis for your store.
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