In this article, we will talk about something very important to every company – how to increase customer retention. We will show you, how our customer retention dashboard works, and how can it be helpful in keeping customers loyal to your brand.
How to Increase Customer Retention using Machine Learning and Data Science – Example Dashboard
As you can see our dashboard answers simple, yet complex question – “how many customers can leave our company?”. The answer is delivered by indicating the most important issues you have to face in order to increase customer retention. Our dashboard shows which segments of customers are likely to leave and how big loss is at risk.
Customer retention is one of the most important problems in the 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 a 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 a new one. All that to say, it is 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.
If you want to learn more please read about anti churn solutions.
Everything starts with the Big Data Science
In the era of data-driven organization, every business collects all available data about its customers. As a result, in a modern world, every business’s 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 speed up the data monetization process by a quick analysis of millions of customers and finding rules that can be used for prediction.
Machine learning based on data science allows conducting many complex comparisons and analysis, which then leads to acquiring new clients and keeping current ones loyal to your brand.
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. Both AI and machine learning are based on a solid foundation of big data science. AI, in order to work, needs a big amount of data that it can work with and on.
That definitely differs from industry, business model, available data and the actions are taken. We will look at an example of how machine learning is used for prediction of the future.
You may also find it interesting – product recommendation system
Machine Learning Helps Increase 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? Usually, it is a mix of different factors, which very often remain unnoticed.
As you already know from our other blog post, customer churn might be caused by the poor customer service, lack of educating and creating engaging content, and finally – resting on your laurels, resigning from improving your product or your services.
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 the greatest impact on your customer retention KPI. This is why you need big data science for this assignment. Without it, spotting correlations and other factors that cause customer churn is very difficult or impossible even.
Examples of Customer Churn analytics – increase customer retention using Machine Learning and data science
In the era of Big Data science, you can analyze your customer’s behavior and with the usage of machine learning in marketing.
Sample data, which could be used in the 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
- Customer preferences 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 the final algorithm for churn prediction. As a result, you will be able to spot previously unnoticed factors and correlations. This knowledge can lead you to improve your company’s offer, products or services. And that will allow you to grow your business rapidly, without any obstacles.
How does our dashboard work
As depicted, our customer retention dashboard shows which segments are the most likely ones to leave. We can indicate a dozen or so segments.
The trained model will help you to predict churn risk for your existing customers. The 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.
Our dashboard indicates how many each customer has spent in your company and what is approximate churn risk. When it exceeds 50% you will see a red number, which tells you to focus your attention on this customer.
But to make that possible, it is very important to maximize and enrich your data set with different sources and combine them to make model accuracy even higher. It means that the more data you are collecting on your customers, the more correlations the Machine Learning model is uncovered, and the more accurate the prediction you get.
All to say, machine learning can help you to segment your customers into different groups of churn risk and will automatically predict particular clients 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 action on them and try to retain them.
Automated customer retention process with Machine Learning and AI
Gathered insights from the machine learning model is only a part of success. You need to build a 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 the 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 the best customers to ensure a stable stream of revenue. But the main question is
Which customer groups 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 models 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 truth is that fraudulent transactions are a very common thing. The problem is that a small part of the activities may quickly turn into big money losses without the right AI tools and systems in place. Here we want to know:
Which transactions 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 customer 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 altogether. 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 grow 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 grow.
 Andreas Kaplan, Michael Haenlein. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Nov 6, 2018. URL: https://www.sciencedirect.com/science/article/abs/pii/S0007681318301393. Accessed Jan 6, 2019.
 John R. Koza, Forrest H. Bennett III, David Andre, Martin A. Keane. Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. 1996. URL: https://link.springer.com/chapter/10.1007%2F978-94-009-0279-4_9. Accessed Jan 6, 2019.