Machine learning and predictive modeling utilizes statistics and mathematics in order to predict future outcomes. Through artificial intelligence and statistical modeling, we can use current and historical customer data to predict future behaviors of customers. Customer response, customer churn and attrition, credit loan default etc. are all examples of concepts involving statistical modeling. Predictive models can be used for various business activities such as customer acquisition, upsell/cross sell campaigns, customer retention/win-back campaigns, credit loan adjudication, credit risk analysis and management, collections and recovery etc.
Predictive modeling is the core technology behind key analytic processes and models such as database marketing, customer loyalty and retention, customer churn and win back, credit risk analysis and management etc. Predictive analytics let you forecast results and optimize outcomes.
For example, we leverage and analyze the historical Amazon e-Commerce data and Air B&B data for our clients, we look into the customer distribution and behaviors with respect to demographic and bizographic characteristics. We employ artificial intelligence and machine learning techniques to build time series models to forecast the sales trends and pricing fluctuations. These analytics provide our clients with valuable information in understanding customer behaviors, predicting future trends and giving data support for their fact-based decision making.
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