Use Cases Industry: Retail
External factors such as traffic conditions, weather forecasts, etc. can be easily integrated thanks to Esplores, allowing to expand the data that dynamically contribute to updating the forecast scenarios on supply chain.
Market basket analysis
It is very important for a retail company to be able to analyze the products purchased in a single receipt. Esplores, in addition to analyzing the detailed data of the individual receipt, allows to add greater context (big data) such as the time of day, the duration of the visit, the weather conditions, and so on.
IMPROVE CUSTOMER RELATIONSHIPS
The "recommendations engine" is the most used solution in the retail sector and not just online. It is based on an algorithm able to propose to a "customer" the purchase of a product that other "similar" customers have purchased, or to address the "best next offer". The broader the context is, the more effective the model is. Esplores makes it possible and easy to invoke / integrate any "recommendation engine" algorithm by exploiting the direct and simultaneous connection to several data sources.
The data can be monetized by selling them to partners and suppliers to provide strategic information on how the products are sold and under what circumstances (e.g., the product is purchased during the day, depending on weather conditions, etc.). Esplores simplifies the merge of the various data sources and their reading through declarative (virtual) "joins" and with very low impact on the infrastructure.
Client-focused mechandising can be qualified, by Esplores, with the creation of analytics that help to identify the clients’ needs, facilitating the selection of new products based on demand.
The real-time offers respond to changes that occur at that moment. A real-time model offers can be used not only for online sales, but also in the "real world". For example, in airports, shops can offer different items for sale based on arriving or departing flights. Another example is the possibility that shops have to change their promotions according to weather forecasts or the number of people in the immediate proximity of the store, etc. Esplores allows the collection of various information, also dialoguing with external engines and/or procedures to implement both prescriptive and predictive analytics in a very effective way.