To date, customer analysis in brick-and-mortar retail has often been limited to analysing loyalty cards and sales data, or to counting customers in the entrance area. More comprehensive customer analyses, such as those used in online retail, are uncommon, but represent a significant opportunity for brick-and-mortar retailers. With retail analytics, retailers can uncover relevant information about their customers and use this customer insight either in real time or to optimise their stores in the future. As a retailer, retail analytics means you no longer make business decisions based on gut instinct or by the book, but rather in a data-driven way. In this article, you will find out which data is measured and which technologies are best suited for implementing retail analytics.
Web analytics as a pioneer in retail analytics
Customer analytics play a pioneering role in online shopping: web analytics are now standard practice. In e-commerce, this involves analysing where visitors to an online shop come from, how they navigate the site, how long they stay, and which items are removed from their shopping baskets. Analysing customer behaviour enables comprehensive evaluations and data-driven optimisations of the online shop’s offering.
Brick-and-mortar retailers face greater challenges in this regard, but there are now sophisticated technologies and software available to analyse customer behaviour in-store, optimise the customer journey and enhance the shopping experience. In addition to quantitative metrics such as customer numbers, modern technologies also enable the identification of age and gender. Other key metrics include the length of time visitors spend in-store and their movement patterns. These metrics can then be linked to till data.
Which technologies are used for retail analytics?
In the retail sector, reliable data can be collected using modern technologies: 3D sensors, cameras, Radio Frequency Identification (RFID), Bluetooth Low Energy (BLE) or Wi-Fi technology. As optical methods, 3D sensors and cameras have the advantage that they can track all customers with high accuracy, not just those carrying a smartphone. In contrast, tracking visitors using mechanical or electromagnetic waves suffers from technical inaccuracies and generally requires customers to carry a smartphone or have a specific app installed.
Thanks to advances in image recognition, even existing camera systems in shops can now be used for more comprehensive customer analysis in brick-and-mortar retail, supplemented by 3D sensors. The data collected is analysed and visualised using specialised software.
Retail analytics comply with data protection regulations, as the data collected is anonymised and does not allow any conclusions to be drawn about individual customers. This means that no personalised data is stored. Furthermore, technologies now exist that do not store the camera and sensor data collected, but instead analyse it in real time and then delete it. Only the results of the analysis are stored.
Find out more about customer traffic
The most important aspect of retail analytics is undoubtedly the counting and analysis of footfall. The aim is to analyse how many visitors come to a store, which sales areas they spend a disproportionately large or small amount of time in, when the busiest times are, and what the age and gender breakdown of the customers is.
Number of visitors
Accurate visitor counting at the entrances helps to determine the total footfall in a shop. If you compare the footfall to the actual number of customers, you obtain the so-called conversion rate, which indicates, as a percentage, how many visitors ultimately become paying customers. At its most basic level, footfall measurement can be limited to simply recording the total number of visitors by counting at the entrances. However, by further differentiating by individual floors, aisles or promotional areas, more in-depth analyses can be produced.
Peak and off-peak times
Understanding how customer footfall varies over a defined period is essential for optimising staff deployment. This applies not only to different times of the day, but also to busier and quieter days of the week, promotional days and seasons. With retail analytics, fluctuations in customer footfall can be identified in real time, allowing staffing levels to be adjusted according to the situation.
The relationship between passers-by and visitors
Another relevant metric is the number of passers-by who enter the shop and thus become visitors or customers. The ratio of passers-by to visitors – known as the capture rate or peel-off rate – can provide insight into how well the shop front is designed, how inviting the entrance area appears, and whether advertising campaigns are achieving the desired results. In addition to the sheer number of passers-by, it is also possible to determine how many people stop in front of the shop window and how long they linger there.
Demographic customer analysis
Retail analytics also enable demographic analysis of customers, as modern technologies are now able to identify people’s age and gender. Furthermore, it is possible to analyse the number of customers shopping together (in groups). This information can be used to further tailor the product range to the demographic profile of the customer base.
Analysing customer behaviour using retail analytics
In addition to quantitative and qualitative customer counting, Retail Analytics enables the analysis of in-store footfall. This includes customers’ movement patterns and dwell times, as well as high-traffic hotspots.
Customer paths
A footfall analysis reveals how visitors move around a store and what the main customer routes look like. This highlights both high- and low-traffic areas within the sales floor. To optimise the sales floor, various measures can then be taken, such as directing customers to the low-traffic areas or increasing the amount of stock in the high-traffic areas to boost revenue per customer.
Hotspots
Furthermore, retail analytics can be used to identify hotspots with very high footfall. Knowing where these hotspots are is crucial for ensuring products are displayed effectively, particularly for new products or those from key manufacturers and brands. Identifying these hotspots also helps to pinpoint areas where there may be a greater need for staff to assist customers.
Average dwell time
It is also possible to track the average dwell time of customers: this can then be analysed for the entire store or for individual departments, floors, zones, shelves and promotional areas. By determining the average dwell time for a specific area of the sales floor, opportunities for optimisation become apparent.
Optimal product placement
Retail analytics can generally also be used to analyse how certain product categories or individual products are being viewed. Furthermore, it is possible to monitor and document the specified product placement centrally in real time, in compliance with data protection regulations.
Implementing retail analytics with a clear strategy
Retail analytics must always be guided by a specific question. Experience shows that it is not sensible to collect customer data for future use, only to analyse it in a general way at some point. Only data that can actually be meaningfully analysed later and that relates to key performance indicators critical to the success of the stores should be collected. A well-thought-out concept with simple implementation steps is therefore of paramount importance when implementing retail analytics projects. This is best achieved with a professional partner at your side who understands the most cost-effective solutions for retail analytics based on your individual needs.
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