In-store cameras and sensors for tracking the customer journey
Insights from tracking the shopper path in a store can be used for many purposes, from optimizing the store layout over deciding on secondary placement locations to time-of-day-dependent adjustments of offers.For a proof-of-concept, we developed a typology of shopping trips based on path-tracking data collected over a one-year period in a German supermarket, using UWB (ultra-wide band) antennas that monitor battery-operated tags implemented in shopping carts and baskets. Based on characteristics like distance covered on a shopping trip, speed, and the proportion of trips spent in specific areas of the store, we identified eight different shopping trip types. For instance, “unstructured refills” are characterized by a long distance, whereas, “single purpose” and “last-minute trips” by a high speed. Combined with survey and sales data, more detailed behavioral differences between segments can be carved out. Such data allows targeted recommendations for specific segments, such as reminders for frequently forgotten items for unstructured refills, optimized category management and bundled secondary placements for single purpose trips, and special product aisles for last-minute shoppers.
Observing customer decisions: Product interaction at the shelves
For more detailed information, shopper behavior in front of shelves can also be observed. The options range from relatively coarse distinctions between passing vs. stopping in front of a certain shelf to detection of hand movements and interaction with products. Ceiling- or shelf-mounted cameras combined with smart algorithms allow the identification of target behaviors. The results reveal areas that receive exceptional high or low levels of attention in terms of stopping or interaction. For example, they allow to identify products that are often grabbed but then returned to the shelf. Further analysis can reveal why customers abandon the product despite initial interest. Today, judging from our own experience with shelf interaction tracking, such data can still be extremely noisy and needs to be evaluated and analyzed very carefully. However, several tech companies are working on this problem. Amazon GO, for instance, already trusts their shelf-tracking technology enough to use it for computing the total a customer needs to pay. So, we expect technology to advance relatively soon and allow improved applications for retailers.
Recognizing customers’ faces: Customer profiles without registration
Modern POS systems enable efficient and timely tracking of sales in terms of when and where which products are purchased at what price. What is missing is information about the customer – about who buys a certain product. Even basic customer profiles based on sociodemographic features such as age group, gender, and whether the shopper is alone or in company are helpful for more targeted communication. Already, there are new smart cameras available that automatically analyze recorded faces in terms of likely age and gender. They only keep this meta data while not storing the face itself and comply with the strict EU legislation on data privacy (GDPR), as to which personal data must not be recorded without explicit consent. Equipped and synchronized with, for instance, the certified privacy-by-design solution AVARD of Fraunhofer Institute, POS systems can add customer data to each recorded sales transaction. However, even if such technologies comply with data privacy regulations, they need to be introduced carefully and consider people’s needs for transparency and control.
A German retailer’s implementation of such systems recently caused a public outrage, leading to its abandonment just a few weeks later. To mitigate consumer concerns and resistance, retailers should find and communicate ways to let customers benefit as well.