Today there are considerably more analytics possibilities than just measuring customer flows or peaks in customer frequency. Instore heat mapping and journey analyses are currently being discussed by many retailers. Connected data give information about which areas in the store are frequented when and to what extent. This purely quantitative analysis enriches further data at the micro level: Who visited my store when, how old is this person and what gender is he or she? How long did the person interact with which product? What mood was the person in at thiat moment? Dwell times and walking routes can be related to conversion rates and shopping baskets in order to gain insights into customer behaviour and assortment success.

There is a high demand for data, but the necessary infrastructure is often lacking. The more data I want about my customers, the more IoT systems I need: simple sensors such as light barriers, turnstiles, intelligent camera systems or haptic walking mats in the entrance area are sufficient for pure customer frequency analysis. If I want to generate heat maps and journey analyses, then I not only need the technical infrastructure. I also have to resort to solutions that can reliably track movement and duration of stay. If I also want to know who my customers are and what lets them convert, then I need a combination of many different systems:

• Cameras that capture age, gender and emotions, distinguish the customer from the employee and can anonymize all captured persons in accordance with data privacy law
• Various techniques for in-store localization and interaction measurement such as Bluetooth, WiFi, RFID or LIDAR
• ERP and CRM data (e.g. a customer card) to assign purchases to a person

In order not only to collect this valuable data, but also to analyse it effectively, it has to be merged on a single platform and thus made smart.

The most important question at the end: Do you need customer data at this depth? The answer is always: The goal determines the way. Often a lot of data is collected, but not needed. If, for example, you only want to optimize opening hours or staff resource planning, a classic customer counting can be sufficient. Heatmaps and journey analyses are needed to optimise the shop areas or the product range. Customer Journey Mappings are again the preferred choice for the significant collection of conversion rates and shopping baskets, for Omnichannel and customer analyses and for the optimisation of individual customer approaches.

Last but not least: customers react differently to different types of data collection. The stronger the tracking, even if it is anonymous, the more likely it is that customers will reject it.

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