Analyzing a company’s data allows us to understand why some clients are not loyal.
Knowing these reasons, we can take action to retain them.
The data science and machine learning platform Neural Designer helps banks, insurance, telecommunications, and retail companies to prevent the attrition of their customers and increase their loyalty.
Contents
Objectives
Churn prevention has become one of the biggest challenges for all companies. Indeed, it is much cheaper to retain existing clients than acquire new ones.
However, detecting a client’s dissatisfaction is not easy, and they often stop using our services without any previous warning.
By harnessing the power of big customer data sets, companies can develop predictive models that enable proactive intervention before it’s too late.
Some of the factors that influence churn are the following:
- Customer variables: Gender, age, education level, job category, marital status, nationality…
- Product/service usage: Mobile, web, physical, call center…
- Engagement variables: Recency, frequency, monetary, time…
- Technical incidents: Customer service calls,
- Stationary variables: Season, date, time…
- Competitor variables: Price, quality of services…
The objective is to develop loyalty programs and retention campaigns to keep as many customers as possible.
Developing new methods to infer who our dissatisfied clients are is essential.
Benefits
The following are some of the many benefits of machine learning for customer churn prevention.
IDENTIFY COMPANY PROBLEMS
Identify the reasons for attrition and fix them.
IMPROVE BRAND REPUTATION
Prevent unsatisfied customers from harming the image of your company.
INCREASE REVENUE
Reduce the loss of revenue from customer churn and save customer acquisition costs.
Approach
It allows us to find the more relevant factors for the churn and design the neural networks that best fit the case of the study.
The following graph illustrates an example of a neural network for churn prevention.
The data science and machine learning platform Neural Designer guides you through this process so that you can focus on your business and not on the details behind machine learning.
Conclusions
Machine learning allows companies to find early signs of desertion before it is too late using the available data.
Neural Designer uses artificial intelligence to discover the reasons for dissatisfaction, assess the churn risk, and improve clients’ loyalty.
