As fewer shoppers choose to frequent stores, retailers are looking for new ways to rejuvenate the shopping experience, whether through a wholesale reinvention of the retail space or by doubling down on efforts to send consumers targeted ads. To that end, retailers are increasingly turning to technologies such as artificial intelligence algorithms, messenger bots, and even robots, to gather data and improve the in-store experience for shoppers. To embrace technology, however, also means embracing data and all the challenges that come with it — namely, how to collect and store data in a way that is simple to access and easy to analyze.
Data Collection Challenges
It’s one thing to install beacons, sensors and other such technologies within a retail space, but it is quite another to actually go through the data that each piece of equipment is collecting. First, there’s the fact that different technologies measure different things: a beacon can track a customer’s movement, but a sensor placed on a shelf might be able to see which item a customer picks up and measure how long they hold it for. And because the beacon and the sensor are measuring different things, it makes it much harder to aggregate the data and get a holistic sense of what a customer is doing within the store.
The second issue is that, in most cases, the beacon and the robot (or the chatbot and the sensor, depending on the retailer) are being manufactured by two different entities and are measuring different things in different ways. The question, then, is how can retailers aggregate all of this data from different sources and bring it together so that it can be analyzed? Furthermore, how can retailers aggregate this data with the online data that they’ve already gathered from their consumers, and that is already in use for advertising campaigns?
How Amazon Is Bridging The Data Gap
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