Uncovering Churn Risks with Generative AI

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. 

The Limitations of Traditional Machine Learning

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. 

‍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 focusing on the qualitative, unstructured data that you already have, you can take proactive steps to improve customer retention and, consequently, your company's valuation.