Customer Churn Prediction

Domain expert knowledge: given that customer churn is tied to the particular business process and, in many cases, a form of identification or prediction is already ensured through hand-designed rules, we come with the experience to extract the domain expertise while moving forward.

Data insights: through data science techniques, we gather and understand more about the available data volume and quality to further process and refine it to your best advantage. The usual process involves starting with a proposed set of features and elaborating it in several iterations with the domain experts while ensuring intermediary analysis, filtering, and processing steps.

State-of-the-art AI model design and development: the model architectures we’re proposing to our customers take into account the best practices in the specific industry and usually include research tailored to fit the data and knowledge at hand.

Adapting the framework to the specific client needs: since multiple business decisions and goals rely on identifying the future ex-customers, the results may differ from one case to another. Based on our experience, we recommended building two or more separate models with different structures and processed data that would better fit each case.

Root Cause Analysis: while the early detection of customer attrition is the main goal, another key point of interest is extracting the main reasons (data features) that led to the system prediction, thus providing insights into how to approach customer retention in the preliminary stages.

Recurrent model training: given that markets, options, and strategies change, there is a permanent need for re-training the model to account for the new data while slowly disregarding the old one. This option can be made operational after obtaining a steady model and a relevant measure of evaluation. The data period taken into consideration for training and the timespan until new coaching is needed can be extracted from a case-based scenario.

Deployments freedom – on-premise or cloud: we have experience with integrating the churn prediction model both on-premise, in a container-based solution, as well as integrating customer data sources with cloud deployments, using customized cloud-based AI pipelines. We make sure to leverage the client’s infrastructure while ensuring complete data confidentiality.