Customer retention is one of the most important problems in 21 century. People have a lot of different products and services to choose from. It makes every entrepreneur to fight a hard battle for the customer. Businesses spend lot of resources and money to acquire a new customer. That is why it is important to keep existing customers. It is 5 times cheaper than finding new a new one. All that to say, it is a possible to reduce customer churn and increase customer retention using machine learning.
It means that investment in retention is one of the most important factors of business growth.
Everything starts from the big data
In the era of data-driven organization, every business collects all available data about their customers. As a result in a modern world, every business` goal should be monetization of the data. Among the vast amount of data points, find the most important factors, find dependencies in the data, transform data, create machine learning algorithms and automate flow.
Nowadays, artificial intelligence algorithms speeds up the data monetization process by quick analysis of millions of customers and finding rules that can be used for prediction.
It is important to understand more about AI in the beginning.
AI & Machine Learning definition
Artificial Intelligence (AI) is a system’s ability to correctly interpret external data; extract insights from the data, and to use those findings to achieve specific goals and tasks through a flexible adaptation. 
Machine Learning is a subset of AI used to build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.  Machine Learning gives you an opportunity to create self-learning models, which will have to make an appropriate task in the future e.g. predict customer churn, predict customer lifetime value (LTV), detect anomaly or fraud etc.
That definitely differs from industry, business model, available data and the actions taken. We will look at an example of how machine learning is used for prediction of the future.
Machine Learning helps improve customer retention
Have you ever wondered what are the most important factors that influence your customers to stop using your services or stop buying your products? For example, usually, it is a mix of different factors, which very often remain unnoticed.
Finding factors (variables), which are influencing customer churn isn’t an easy task. There is a lot of customer data to process to find out which factors are the most important and have a greatest impact on your customer retention KPI.
Examples of Customer Churn analytics – cutomer retention using machine learning
Sample data, which could be used in churn model is:
Customer demographics data
Loyalty programs data
Purchases transaction history
Direct communications (emails, number of phone calls)
Type of services/products bought or used
and other available data.
Above mentioned data could be used by Machine Learning models. AI models during the training process will find correlations, uncover hidden patterns in data and create final algorithm for churn prediction.
Trained model will help you to predict churn risk for your existing customers. Model will find historical information on customers who had similar attributes and behaviors and left. That information model will use to mark existing clients, like it is shown below.
That is why it is so important to maximize and enrich your data set with different sources and combine them to make model accuracy even more higher. It means that, the more data you are collecting on your customers, the more correlations the Machine Learning model will be able to uncover.
All to say, machine learning can help you to segment your customers into different groups of churn risk and will automatically predict particular`s client his churn ratio. Those features will help you to understand which customers are likely to leave and why, so you will be able to take actions on them and try to retain them.
Automated customer retention process with machine learning
Gathered insights from machine learning model is only a part of success. You need build process behind insights and create flow, which will automatically take care of risky clients. Customers will be grouped by their churn risk, attributes and personalized offers into different segments. Personalized emailing campaigns should be set to ensure the highest chance of engagement and retention.
Artificial Intelligence (AI) model integrated into your marketing systems or CRM will automatically send personalized messages based on the case, customer, and other factors. As a result that will ensure the message is perfectly targeted for a user.
For clients that may be close to leaving, this message can make an opportunity to engage customer to stay with you. Everything depends on your discount ,loyalty campaigns and capabilities.
Machine Learning solutions for Fraud detection and LTV prediction
Customer lifetime value (LTV) prediction
Each customer segment generates different levels of revenue for a business. The goal is to identify and take care of best customers to ensure a stable stream of revenue. But the main question is
Which customer group are driving your business?
Is our best customer in the past will be our best customer in the future?
How much they are going to spend in the future?
The goal is to identify customers who drive our revenue and ensure that they are happy, satisfied and want to stay with us. Here is where Machine Learning (ML) together with predictive analytics comes into the game. We are able to train model and predict how much a particular customer will spend in the future using Machine Learning.
Fraud detection is a challenging problem for many businesses. The true is that fraudulent transactions are very common thing. The problem is that a small part of activities may quickly turn into big money losses without the right AI tools and systems in place. Here we want to know:
Which transaction are lawless?
Which transactions are anomalies?
What is potential loose?
Businesses nowadays should be able to identify, which transactions are fraudulent and are incorrect. The good news is that with advances in machine learning, systems can learn, adapt and uncover emerging patterns for preventing fraud.
Benefits of machine learning in customer retention process
You are able to increase your retention rates based on correlations that could be found in your data. As a result that would not be possible without AI, machine learning and automation all together. Churn analysis will help understand which factors influence your customers to leave, identify high risk customers and help to keep them. Finally, customer retention will be improved and profits will be higher than ever before. Key points below:
Focus on customers who are actually at risk and improve their experience
Save up on acquisition cost to replace the Churning customers
Having stable revenue helps you to growth and acquire new customers
Once you are able to save money from higher customer retention and your revenue streams are stable, you can re-invest money into new customer acquisition and let your business to grow.