How Renewal Managers Can Reduce Churn Rate with Predictive Analysis

Customer churn is one of the major business problems that organizations are facing in a rapidly changing highly competitive marketplace. Churn rate represents the percentage to the customers or subscribers who cancels or don’t renew the subscription in a given period. It is an important metric for subscription based companies.

 

Statistics show that, on an average, 65% of an organization’s business comes from existing customers, and acquiring new ones is 5 to 25 times more expensive than retaining existing customers. In fact, retaining customers has historically been a lagging business indicator. It is seen that organization assess what went wrong only when organizations have lost their customers.

 

With increasing access to data, the opportunity exists for organizations to leverage predictive churn analysis capabilities to identify customers at risk and proactively prevent them from leaving.

 

Having the ability to accurately predict future churn rates is essential because it helps your business gain a better insight of future expected revenue. This ability to predict that a customer is at a high risk of churning, represents a great opportunity to increase customer loyalty, addressing the situation proactively and avoid loss of revenue.

 

When it comes to renewal deals, you cannot afford to waste time on tracking all contracts, hunting down accounts and analysing which customers will renew and which will not.

 

But there is a hope! Machine Learning allows you to retain your customer. It is a way to let Artificial Intelligence predict which customers were going to churn before it even occurred. This helps in predicting where marketing resources and sales efforts should be allocated so that churning tide can be cut down.

 

What is Machine learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and discover patterns or trends in data sets without being explicitly programmed. In short, you can train a model to predict churn through real cases based on previous churn data and then evaluate customer data for any complexities.

 

But how can this help Renewal Managers?
Screen below shows a quick snapshot of Renewal opportunities along with their churn risk profiles which can help to take actionable decisions.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and discover patterns or trends in data sets without being explicitly programmed. In short, you can train a model to predict churn through real cases based on previous churn data and then evaluate customer data for any complexities.

Screen below shows a quick snapshot of Renewal opportunities along with their churn risk profiles which can help to take actionable decisions.

Whenever you are working on a renewal deal, your focus is that the customer renews only with you. But predicting can be difficult as it is nearly impossible to dig out each customer account data for detailed analysis.

As shown in the snapshot above, based on your past data, you can predict the chances of customers opting for renewal. This information can be readily available, helping to take appropriate steps for the closures. Customers who are at high risk of churn can be focused more with these data points.

 

To help renewal managers maximise revenue & improve their efficiency, Machine Learning plays a vital role connecting huge amounts of historical client data points for focused analytics in the entire customer journey.

 

This not only helps gain confidence while interacting with customers, but also helps to close deals faster.

 

Benefits of Predictive Churn analysis using ML
● Improved ROI: Studies say an increasing retention rate of 5% can increase profit percentage to 25% to 95%. This analysis can help increase revenue stream through focus on users who are at churn risk.

● Customer Retention: Analysing customers at a 360 degree angle and understanding the impacting factors so that team members can take appropriate action and save up on acquisition costs to replace the churning users.

● Increase in Customer loyalty: Having the right set of data & analytics beforehand can help to rework on overall strategy to proactively reach high-value customers that are likely to churn thereby protecting future revenue from churning users and increasing customer loyalty.

● Increase in renewal rate: Giving teams actionable data with improved churn metrics and accurate information will speed up the process allowing them to focus on more business.

 

Get ready to know your customer’s next move with Yagna iQ’s powerful predictive churn analytics which can help you find out ways to keep more customers and grow your company. To know more connect us at customer.success@yagnaiq.com