Building Predictive And ML Models To Understand Customer Behavior For An Insurance Industry Giant
Problem Statement:
Our client in the insurance industry needed a better way to predict whether a customer would renew, upgrade, or cancel their policy. Foresight into customers’ likeliness in renewing their existing policy, upgrade to a better policy, or churn altogether gives the insurance company leverage to make business decisions that will help it increase its ROI.
Solution:
To address this problem, Stilbon’s team implemented predictive analysis to classify customers into categories based on historical trends. Using Google Cloud’s advanced machine learning capabilities, we built a classification model to categorize insurance customers into four groups:
- Those likely to renew without intervention.
- Those likely to renew only with intervention.
- Those likely to churn only if there is intervention but would renew otherwise
- Those likely to churn regardless of intervention.
Using BigQuery, we analyzed historical customer data to identify patterns and trends, and then built a machine learning model to predict future customer behavior. We leveraged Looker to visualize the results and gain insights into the factors that impact customer behavior, such as satisfaction with different products and dissatisfaction reasons.
Outcomes:
By using predictive analysis and modeling, our client was able to gain foresight into their customers’ behavior and make proactive business decisions. With the insights gained from our analysis, they could identify customers who were at risk of churning and take targeted actions to retain them. Additionally, our analysis helped them understand the factors that contribute to customer satisfaction, allowing them to make data-driven decisions to improve their products and services. With Stilbon’s expertise and Google Cloud’s powerful tools, our client was able to optimize their business processes and increase their ROI.