Use Cases Industry: Generic
Companies in manufacturing, services, telecommunications, etc. take advantage of data collected by sensors that track imminent malfunction or failure signals. Esplores allows to intercept these signals, to compare them with a historical series and to integrate algorithms able to predict the type of action to be implemented in more efficient times and modalities.
Esplores allows the monitoring of operations in real time by applying complex kpi (example thresholds / alert) able to help the user in "managing decisions" as prescribed (e.g., quality control of the processing phases, etc.).
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Esplores allows to cross-analyze all the data referring to consumers. Esplores integrates features that can analyze their sociodemographic, behavioral characteristics, etc. These features prove to be very useful to support marketing and sales departments in developing actions aimed at optimizing the expected results.
Today the interaction with the customer takes place both with internal company systems (CRM, ...) and with external systems (Social, etc.). Esplores allows to "match" both internal and external data, enabling analytics that can add new qualitative attributes (eg customers' judgments on the launch of a new product, etc.).
Many companies develop new products and / or services based on the information they have available in carrying out their core business. Thanks to Esplores, these companies can prepare data to be "sold" to third parties based on specific needs.
For example, a telecommunications company, which manages the data of its mobile customers (geolocation, hour / min, etc.) can "package" specific services to analyze the flow of peoples and sell these to governments, tourism companies, motorway companies, etc.
Fraud detection is a recurring theme in all sectors. Esplores is able to launch external patterns (Python, R, etc.) to intercept "hidden" potential frauds.
In fact, Esplores allows data discovery to the maximum detail and is therefore able to intercept any data (e.g., small transactions not detectable on aggregate data, data in text files, data on unstructured files). The traditional anti-fraud models flanked by Esplores, are enhanced thanks to the possibility of detecting "anomalies" both on operational data and on big data, helping to limit the so-called false positives.