Churn
Mathematical and predictive computing models based on customers’ behavior data analysis and customized to any company’s specific needs provide robust and reliable outcomes for estimating the risk of churn and predicting the reasons why customers churn, and also for estimating the customer retention within a three-month period. This gives the company the time to plan proactive customer retention strategies and to allocate budget for targeted marketing campaigns.
Unpaid
Predictive models are able to forecast the customer payment behavior and reliability, and also simulate new market scenarios and new customer targets. They also provide insights that enable the company to understand the common characteristics of the customers about their payment behavior for the company’s services, so that the company can understand the future behavior patterns of their customers and make decisions about its customer targets and future markets to explore.
Sales/Demand
Automated forecasting systems based on machine learning models, integrated with the company’s internal IT systems, enable constant balancing of supply and demand within highly variable markets. Forecasting models are able, quickly, to optimize the supply management and logistics related estimations in order to avoid financial penalties due to oversupply issues and to contain the costs of procurement.
Pricing Scenario
Thanks to Artificial Intelligence, with the aid of similar quotations and orders held in the company’s data history, it is possible to speed up and make efficient the computational steps required for making quotations and building estimation models, in a matter of minutes. The algorithms provide various rough estimation models on which the company can evaluate which are the best performing.
