Customer Health Indicator

Document Type

Abstract

Publication Date

Spring 5-1-2019

Abstract

With cloud-based SaaS platforms becoming more and more popular with today’s technology companies, many who are pursuing either SaaS B2B or B2C have found themselves struggling to retain customers from one payment period to the next. This has become increasingly problematic for companies as more and more competition enters the markets and potentially takes away valuable clientele. In order to help combat this issue, many companies have turned to customer success in order to retain customers and keep them from canceling subscriptions. Customer Health Indicator, a web-based application, works to reduce churn (customers not renewing a subscription), by predicting if a customer is likely to churn in the next payment period in order to help users be more proactive with their clientele. However, Customer Health Indicator is not solely useful for predicting if a customer will churn but also allows users insight into which customers should be upsold in the next payment period. Customer Health Indicator will utilize python to pre-process data and run regressions such as gradient boosting on data provided by Telcom in order to predict if a customer will churn in the next payment period or not. Through simplistic user interface, Customer Health Indicator will allow users to easily and accurately determine whether or not any given customer will churn based on a few of the most highly correlated customer data inputs in regard to the churn attribute of the dataset. All the user must do in order to predict if a customer will churn in the next payment period is enter their customer data, click predict, and see the prediction generated all from the home page.

Comments

Completed as part of the Computer Science Senior Capstone Project.

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