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Customer Churn

What is customer churn?

All businesses experience some form of churn, which is when a customer leaves or discontinues using your product or service. In most cases, it costs less to keep an existing customer than it does to gain a new customer. Additionally, it’s easier to save a customer before they leave than it is to convince them to come back. Because of this, customer loyalty is something all brands strive for. As a result, understanding and preventing churn is a critical element to success.

How to Measure Customer Churn

Customer churn also referred to as customer attrition, is when a customer decides to discontinue using your company’s products or services. With each customer that decides to churn, there are usually early warning signs that could have been discovered with churn analysis. 

Churn analysis looks at both operational insights (e.g. declining repeat purchases, reduced purchase amounts) and customer feedback (e.g. Net Promoter Score) to identify potential early indicators of customer churn. For example, a customer who has declined in recent visits and gives a Net Promoter Score of 7 after their latest shopping experience, could have an increased probability of churning.

Relational feedback requests are a great way to start understanding your customer’s journey and the experiences they have. You can use these requests to assess and diagnose key drivers of customer satisfaction. Once you’ve done this, you can move on to measuring transactional experiences, such as a purchase or post-support follow-up. This will help you get a better sense of where you have detractors and build out a plan to follow up with them. Finally, you can map out the full customer journey and measure the key experiences across the journeys. This will help you identify any areas that need improvement so you can provide your customers with the best possible experience.

How to Predict Customer Churn

Once you have a complete understanding of your customer’s experience with your brand, you need to combine it with operational data, such as repeat visits or credit card usage. This will help you identify the key drivers of customer churn and begin making predictions. Using deep learning and neural networks, Qualtrics Predict iQ can help you predict individual customer behavior, so you can take action before it is too late.

Predict iQ is designed to help you accomplish four key elements of churn prediction and prevention:

1. Understand the drivers of customer churn

2. Automatically identify at-risk customers

3. Define thresholds for taking action based on the likelihood of churn

4. Easily create tickets and take immediate action for closed-loop follow-up

It all starts with building a model. You can select your outcome variable, such as churn, and then Predict iQ looks at patterns in operational data, like return visits and credit card usage, and combines those with experience data, like satisfaction or likelihood to recommend. This gives you a comprehensive view of which customers are at risk of churning so you can take action to prevent it.

How to Prevent Customer Churn

Once you’ve predicted whether a customer is at risk of churning, responding to those at-risk customers is the critical next step. Predict iQ can help you create alerts and tickets for customers in various states of unhappiness with your products or services.

For example, you can set a target that requires all tickets for customers with an 80% likelihood to churn to be resolved within 24 hours. If you get a low score on a survey and the churn threshold is triggered for a customer, Qualtrics automatically generates a ticket requiring specific attention and immediate resolution.

Qualtrics’ action-planning module helps you do more than just continuously react to customer pain – you can also use insights from the closed-loop process to drive system-wide improvements that avoid customer issues altogether. With this module, employees can collaborate with others, tag owners, set deadlines, and even supply step-by-step guidance. This way, you can avoid potential problems before they even start.