Churn is a phenomenon that affects all businesses, big or small. It refers to the loss of customers over time and can be a silent killer of revenue and growth. 

We recently hosted a webinar on how generative AI can help identify these churn risks before they become a problem. And in particular, discussed how machine learning models, particularly large language models like GPT-4, can help businesses identify the early signs of customer dissatisfaction and take action to mitigate churn.

What is Churn and Why is it a Problem?

Churn refers to the loss of customers, either through cancellation of a subscription or failure to make additional purchases. It is a primary concern for investors and directly impacts a company's valuation. The sooner you can identify churn risks, the higher your likelihood of retaining that customer. 

Churn rate is a critical metric of customer satisfaction. Low churn rates mean happy customers; high churn rates mean customers are leaving you. A small rate of monthly/quarterly churn compounds over time. 1% monthly churn quickly translates to almost 12% yearly churn. 

According to Forbes, it takes a lot more money (up to five times more) to get new customers than to keep the ones you already have. Churn tells you how many existing customers are leaving your business, so lowering churn has a big positive impact on your revenue streams.

Churn is a good indicator of growth potential. Churn rates track lost customers, and growth rates track new customers—comparing and analyzing both of these metrics tells you exactly how much your business is growing over time. If growth is higher than churn, you can say your business is growing. If churn is higher than growth, your business is getting smaller. 

Customer and revenue churn: Customer churn is simply the rate at which customers cancel their subscriptions. Also known as subscriber churn or logo churn, its value is represented in percentages. On the other hand, revenue churn is the loss in your monthly recurring revenue (MRR) at the beginning of the month. Customer churn and revenue churn aren’t always the same. You might have no customer churn, but still have revenue churn if customers are downgrading subscriptions. Negative churn is an ideal situation that only applies to revenue churn. The amount of new revenue from your existing customers (through cross-sells, upsells, and new signups) is more than the revenue you lose from cancellations and downgrades.

Voluntary and involuntary Churn: Voluntary churn is when the customer decides to cancel and takes the necessary steps to exit the service. It could be caused by dissatisfaction, or not receiving the value they expected. Involuntary churn happens due to situations such as expired payment details, server errors, insufficient funds, and other unpredictable predicaments. 

Customer satisfaction, happiness, and loyalty can be achieved to a certain degree, but churn will always be a part of the business. Churn can happen because of:

• Bad customer service (poor service quality, response rate, or overall customer experience),

• Finance issues (fees and rates),

• Customer needs change,

• Dissatisfaction (your service failed to meet expectations),

• Customers don’t see the value, 

• Customers switch to competitors,

• Long-time customers don’t feel appreciated.

0% churn rate is impossible. The trick is to keep the churn rate as low as possible at all times.

The Limitations of Traditional Machine Learning for Prediciting Churn Risk

Traditional churn prediction models often require substantial investment and are mostly accessible to big companies. These models are generally brittle and don't handle qualitative data well. Moreover, most of these models focus on numerical indicators like customer spend, engagement levels, or survey scores. They often miss out on qualitative, unstructured data like customer interactions, which could provide vital signs of an impending churn.

The Power of Conversational Data

Your customers are already giving you all the information you need to predict churn; it's embedded in the conversations they're having with your customer service agents. These conversations are rich with insights into customer dissatisfaction and potential churn risks. However, these signals are often subtle and easily missed, especially when conversations are analyzed manually or with keyword-based legacy machine learning models.

Generative AI to the Rescue

Generative AI and large language models like GPT-4 can understand the nuances of language in a way that traditional models can't. With the capability to process and deeply “understand” language, these models can identify subtle indicators of churn risk from customer conversations. 

AI Powered Churn Prediction Use Cases

1. E-commerce:

Online retailers can utilize AI to analyze browsing behavior, purchase history, and customer feedback to identify users who are at risk of abandoning their shopping carts or discontinuing their purchases. By understanding the reasons behind potential churn, e-commerce companies can implement strategies such as personalized recommendations, discounts, or loyalty programs to encourage continued engagement.

2.Subscription Services:

Companies offering subscription-based services, such as streaming platforms or software-as-a-service (SaaS) providers, can employ AI to analyze usage patterns, subscription renewal rates, and customer feedback to predict which subscribers are likely to cancel their subscriptions. This allows subscription businesses to intervene with targeted promotions or content recommendations to prevent churn and improve subscriber retention

3.Telecommunications:

Telecom companies can use AI to analyze customer usage patterns, billing history, and customer service interactions to predict which subscribers are likely to switch to a competitor. By identifying these customers early, telecom companies can offer targeted incentives or personalized offers to reduce churn.

4.Finance:

Banks and financial institutions can leverage AI to analyze transaction data, customer feedback, and account activity to predict when customers are likely to close their accounts or switch to another bank. This enables banks to offer tailored financial products or services to retain customers and improve loyalty.

‍Implementing AI-based Churn Prediction

Once generative AI flags a potential churn risk, companies can:

• Automate Campaigns: Put the identified customers into automated "save campaigns," targeting them with promotions or special communications.

• Enhance Existing Models: Feed this new qualitative data into existing churn prediction models to improve their accuracy.

Identifying churn risks as early as possible gives companies a higher chance of retaining customers. Generative AI offers an advanced, scalable way to uncover hidden churn indicators in customer conversations. By using churn prediction software that focuses on the qualitative, unstructured data that you already have, you can take proactive steps to improve customer retention and, consequently, your company's valuation.