Using IntegratedML to check customer churn

The Customer Churn Predictor application is a tool that demonstrates how to use IntegratedML to helps companies identify which customers are likely to leave in the near future and develop customer retention strategies. Benefits of the similar application include:

• increase the effectiveness of marketing campaigns
• improve the quality of customer service
• reduce losses associated with customer churn
• increase profit
• improve customer loyalty

In this way, companies can reduce the cost of marketing campaigns aimed at retaining all customers and instead focus on those who are most likely to leave. Understanding why customers might decide to leave allows companies to improve the quality of their services and products.
When a company keeps a customer, it keeps a source of income, and even reduces the cost of attracting new customers.
The customer churn predictor helps companies create more loyal customers who value higher quality services and products. When a company retains a customer, it creates a positive experience for the customer and improves their relationship with each other, and a relationship of trust is very important now, given the huge competition in almost all areas of the service market.

In development, I used InterSystems Cloud SQL and Integrated ML because they provide high performance and speed of data requests and ease of use IRIS database and machine learning.

Current application uses a demo dataset of the telecommunication company clients from https://www.kaggle.com/datasets/blastchar/telco-customer-churn.

It contains the characteristics of users and information about their use of the company's services. By specifying a customer profile, you can predict the churn probability.

To try the application you can use online demo or run it locally with your own Cloud SQL account.

This is an example of what it looks like:

The result is:

The application also participates in the context, you can vote for it if you find it interesting.