Difficulties and challenges arise for these businesses in understanding customer sentiment and the significant rates of churn/rate of customers switching between the service providers and the businesses. They are unable to determine the at-risk ones among their clientele and therefore deploying any retention units becomes difficult. Moreover, the lack of personal contact with the customer discourages engagement and satisfaction which puts churn at an even higher threshold.
In order to meet these difficulties, a predictive customer analytics solution was designed with the help of the popular Python libraries such as NLTK, SpaCy, and Scikit-learn. Customer sentiment analysis including customer comments and every interaction was utilized. Further emotion and preference analysis was done through customer sentiment analysis tools. Predictive models that pinpointed at-risk customers in advance were constructed using TensorFlow and PyTorch machine learning frameworks. Data processing was done using AWS Glue and Azure Data Lake, while Oracle was used as the database for storing customer information. This integrated approach enabled personalized communication and targeted retention strategies.
Thus, the introduction of churn prediction and sentiment analysis led to:
This solution showcased the power of Python, driving customer engagement and retention strategies within financial services.