Since the Real Time Locating Systems (RTLS) entered the market back in the 1990s, there has been several attempts to create a reliable secure system to locate objects & people nearby in indoor environments. That is not surprising given that people spend most of the time inside buildings, where space-based satellite location systems like GPS suffer from signal attenuation. There has been a large number of technology approaches after RTLS, most of them based on radio waves and radio signals. It wasn’t until Bluetooth enabled devices became more popular, that the first beacon-based Indoor Positioning Systems (IPS) like Apple’s iBeacon and its android-based homologous Datzing entered the market, allowing for indoor location, mapping, geofencing and proximity detection.
These technologies bring the possibility to acquire relevant in-store behaviour data from customers, which could become a key factor to improve the customer’s experience while, for instance, shopping; developing new marketing strategies and boost the efficiency of the spatial organization of buildings and stores. Let’s see a couple of use cases where graph database technology could be key to develop high performance solutions for indoor positioning analysis applications.
Product placement optimization
For both examples we will consider a clothing store with several collections, with each collection placed in a certain spot. Sometimes customers may find the collections they like close to each other, but more frequently they not, resulting in a loss of interest and probably with one customer walking away. A beacon-based product placement optimization application could be the solution to this problem. Given the nature of the data, using a graph database like Sparksee could make a difference on the performance of such an application. Consider the example that follows:
When a customer is browsing a collection (e.g. stands more than 30 seconds in a collection spot) it becomes a node in the graph. Every time a customer goes from one collection to another we can create a weighted edge between them. If the same pattern of behavior is repeated (by the same user or another user) the relation between these collections is stronger, increasing that weight between the two nodes. We can then discover a path that optimizes the weight between two spots or nodes, navigating through all the nodes included in the graph, because we wish to place each of the collections inside the building. This is an example of an application for the optimal placement of certain products in a store, which could be used also to predict the location of further products.
Another potential use case of graph databases and beacon-based Indoor Positioning Systems is presenting offers and ads based on prior customer behavior. From a marketing point of view, it is not efficient to advertise the same products to every client, given that they have different tastes and needs. Using the patterns that we acquired through the process described on the first use case, we could optimize the ads and offers that are presented to each customer. This would result in a better experience for the customer and in a greater probability of them purchasing the item announced. This ads could be presented via smartphone application or also through monitors placed on the walls in the store. Having a mobile graph database like Sparksee would allow the application to be updated based on the customer’s current movements in the shop and his and similar costumer’s previous behaviours on real time showing the customer ads that could trigger his attention to a particular part of the shop.
You can find more Sparksee use cases, tutorials and other useful resources in the Sparsity Technologies website, our blog or Sparsity’s social media channels. Also remember that you can download Sparksee for free and start using it for your own project.
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