Algorithmia Blog - Deploying AI at scale

Improving Customer Retention Analytics With Machine Learning

ML allows companies to base their product and marketing retention strategies on predictive customer analytics

Customers have an abundance of options when it comes to products for purchase. This excess of options, however, increases the risk of poor customer retention. Since acquiring new customers costs much more than keeping current customers, a higher retention rate is always better.

Customer retention represents the number of customers who continue purchasing from a company after their first purchase. This is usually measured as the customer retention rate, which is the percentage of customers your company has retained over a certain time period. The opposite of retention rate is churn rate, which represents the percentage of customers a company has lost over a given time period.

Customer retention analytics can be done through machine learning, allowing companies to base their product and marketing strategies on predictive customer analytics rather than less reliable predictions made manually.

In a survey of more than 500 business decision-makers that Algorithmia conducted in the fall of 2018, 59 percent of large companies said that customer retention was their primary use case for machine learning technology. 

What Is Customer Retention Analysis?

Customer retention analysis is the application of statistics in order to understand how long customers are retained before churning out and to identify trends in customer retention. This type of analysis discerns how long customers usually stick around, whether or not seasonality affects customer retention, and discovers behaviors and factors that differentiate retained customers from churned customers.

Why Is Customer Retention Analysis Important For Your Company?

Customer retention analysis is important for your company because it helps you understand which personas have higher retention rates and discern which features impact retention. This provides actionable insights that can help you make more effective product and marketing decisions. 

It can be difficult for a product or sales team to know how well a product is actually performing with the target audience. They may think that features and messaging is on brand and clear because acquisition numbers are growing. However, just because new customers are purchasing a product does not necessarily mean customers like the product or service enough to stick around. 

That is where customer retention analytics comes in. Every company needs data in order to make effective business and marketing decisions. Machine learning makes this easier than it has ever been before, which is great news for companies that wish to leverage this data.

How Do You Analyze Customer Retention?

Use past customer data to predict future customer behavior

Machine learning for customer retention analytics uses past customer data to predict future customer behavior. This is done using big data. In today’s data-driven world, companies can track hundreds of data points about thousands of customers. Therefore, the input data for the customer retention model could be any combination of the following:

  • Customer demographics
  • Membership/loyalty rewards
  • Transaction/purchase history
  • Email/phone call history
  • Any other relevant customer data

During the model training process, this data will be used to find correlations and patterns to create the final trained model to predict customer retention. Not only does this tell you the overall churn risk of your customer base, but it can determine churn risk down to the individual customer level. You could use this data to proactively market to those customers with higher churn risk or find ways to improve your product, customer service, messaging, etc. in order to lower your overall churn rate.

How Do You Improve Retention?

To improve retention, you have to first understand the cause of your retention issues. As discussed, machine learning models are a very efficient way to analyze customer retention to determine risks and solutions. 

Data science teams can build the machine learning models necessary for this type of predictive analytics, but there are challenges associated with developing machine learning processes. For example, deploying models written in different languages is not easy, to say the least. Algorithmia’s AI Layer solves these issues using a serverless microservice architecture, which allows each service to be deployed independently with options to pipeline them together. 

Another challenge is overcoming the cost of time lost to building, training, testing, deploying, and managing a model, let alone multiple in a machine learning program. 

Improving customer retention is one of the main uses Algorithmia’s early adopters focused on because it is one of the simpler machine learning models to build and use, and it’s even easier with the serverless microservices framework provided by the AI Layer. Our platform has built-in tools for versioning, deployment, pipelining, and integrating with customers’ current workflows. The AI Layer integrates with any technology your organization is currently using, fitting in seamlessly to make machine learning easier, getting you from data collection to model deployment and analysis much faster. 

To learn more about how the AI Layer can benefit your company, watch a demo to see how much easier your machine learning projects can be.