Many management decisions in retail are still based on gut feeling and the personal experiences of managers and sales staff. But optimising a location in terms of product range, presentation, routing or shop layout should not be based on subjective impressions. An automatically generated database and the key figures derived from it form the basis for data-driven retail analyses.
Modern sensor and camera technology is now available to collect visitor data systematically and in compliance with data protection regulations. In this article, we present options for retail analytics to optimise your location.
Visitor frequency as a basis for retail analyses
Automatic measurement of visitor frequency is an indispensable basis for retail analysis. When combined with other values, it can be used to derive many relevant key figures that are important for measuring the success of optimisation measures. Retailers can determine visitor frequency not only for an entire store. To further optimise their location, retailers can also analyse visitor frequency in aisles, promotional areas or on individual floors.
Capture rate as an indicator of attractiveness for casual customers
The capture rate is a relevant indicator in retail analyses. It is the ratio of visitors in the shop to passers-by in front of the shop. The capture rate indicates how attractive a shop is to walk-in customers. The design of the shop window, the outdoor area and the entrance area therefore influences the capture rate. When redesigning the shop window, retailers can determine how this affects the capture rate. However, the success of advertising measures can also be seen in the change in the capture rate. Optimisation of a location is always necessary if the customer acquisition rate is developing negatively or if it is below average for a location compared to other locations.
Analyse conversion rate development
The conversion rate is also an important parameter for managing and optimising a store. It is calculated from the ratio of actual customers to visitors. Visitors are determined by automatic frequency measurement, while the number of customers is determined by the cash register system. The conversion rate can be analysed on a daily, weekly, monthly or annual basis, for example. Based on the development of the conversion rate, retailers can see how optimisation measures such as better signage, shorter queues or adjusted walking routes are having an effect. Without frequency measurement, however, retailers cannot clearly determine the success of optimisation measures.
Age and gender of visitors decisive for product range design
Retail analyses in conjunction with retail analytics enable the gender and age of individuals to be identified. Analysing gender and age offers great potential for further optimising the location. This is because the demographic structure enables companies to better understand how they need to adapt their product range or advertising measures. The optimisation of a location can relate to underrepresented target groups or to the existing customer base.
Increase sales with walking path analyses
Cameras and 3D sensors help to log visitors' routes through the store. Route analysis enables retailers to identify the main routes and the less frequented routes and zones. Optimising the route guidance or shelf heights can direct customers to areas that have previously been less frequented. In high-traffic areas, on the other hand, it makes sense to remove low-selling products and replace them with other goods. These include, above all, goods with high margins, goods in high demand, new additions to the range, special offers and promotional items. It is also helpful to identify high-traffic areas in order to deploy sales staff more effectively there.
Average dwell time as part of modern retail analysis
To optimise a store, the average length of time visitors spend there can also be analysed. This is because, in theory, the longer they stay, the more likely they are to make a purchase. This retail analysis can refer to the entire sales area or to specific sections of the store. If certain areas have a below-average dwell time, retailers can implement optimisations at that location. By understanding the development of the average dwell time, retailers can see how changes to the product range, routing or shop fittings are affecting customer behaviour.
Using retail analyses to optimise a location
Retail analytics are essential for businesses of all sizes and in all retail sectors. Whether you run a fashion boutique, electronics store, shoe shop or shopping centre, understanding consumer behaviour is essential for managing your business. Retail analyses in conjunction with retail analytics provide detailed data on the number, structure and behaviour of visitors. Retailers can use this data to optimise a location. Without retail analyses, it is more difficult to decide when optimisation measures are necessary and when they are successful. Crosscan will be happy to advise you on technical solutions for innovative retail analyses.
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