Qualitest provides insurance companies with a sophisticated prediction model solution that increases sales and ROI for marketing efforts.
A leading insurance company used Qualitest’s solution to predict which client had a high likelihood to sign up with the company again after leaving it. When using Qualitest’s solution, the company was able to effectively utilize sales and marketing efforts and focus them on a targeted audience.
The number of churners was over ten thousand per month. Based on the company’s service call-centre capacity, it was impossible to call each one of them and invest the time and effort to persuade them to opt for the service again.
The company used Qualitest’s machine learning technology to predict which client had a high likelihood to join the company again. The company goal was to increase the positive response ratio by direct sales calls to a targeted past clients’ list.
Based on our company’s vision of enabling data analysts without statistical and math expertise to create the prediction model, the Qualitest software tool was chosen. After a brainstorming process that led to creating a unified panel of exploratory variables, Qualitest’s system began to crunch the numbers.
Obligated to the Data Citizen vision, Qualitest provided the client’s data analysts the ability to use 40 machine learning algorithms on the targeted data. Also, the automatic mechanism took care of finetuning the algorithms to find the best explanatory variables combination. Each data analyst had access to a software tool that could be used for data exploration and prediction modelling creation without the need for a qualified Data Scientist.
The Analytics department took full ownership of the model creation and a data analyst was appointed to the mission of feeding the software with the data and review results. The software tool’s ability to crunch numbers automatically without the need to write a single line of code proved to be very efficient.
Qualitest’s solution provided a variety of methods to implement the chosen model in the production environment. The implementation process for the insurance company took less than three days end-to-end.
The best model was implemented in the production environment. Based on the data of clients online browsing, the company was able to identify with 88% accuracy which client will join the service again after leaving it one month earlier. An additional outcome from this project was the ability to find out the most important variables that explain the characteristics of churners that are willing to join back the company.
The Qualitest machine learning software tool for non-Data Scientist personnel delivered effective results and achieved the goal. The solution proved to be accurate, efficient and powerful. The solution increased sales by increasing the effectiveness of sales calls.
