In today’s competitive market, reducing customer churn rates is essential for businesses that want to maximize their customer lifetime value and increase profitability. Artificial intelligence (AI) can be an effective tool in helping companies achieve this goal. AI-driven solutions can help identify the factors behind high churn rates, develop strategies to prevent customer loss, target customers who are likely to churn, and track the customer’s journey.
SwissCognitive Guest Blogger: Arek Skuza – “How Artificial Intelligence can be Used to Reduce Churn Rates”
By leveraging AI-driven solutions, businesses can gain valuable insights into their customers’ behavior and preferences while also helping them form better relationships with their customers. This article will discuss how AI can be used to predict and reduce instances of customer churn.
One of the primary ways Artificial Intelligence can help reduce churn is through the use of predictive analytics. By using machine learning algorithms, companies can gain insights into customer behavior patterns and identify customers who are at risk of leaving. These AI-driven models can analyze customer data such as buying habits, subscription duration, website activity, and more in order to determine which customers are likely to churn.
Once the at-risk customers have been identified, businesses can take action to prevent them from leaving. For example, they can offer personalized promotions or discounts in order to incentivize the customer to stay. The use of predictive analytics also enables companies to target high-value customers and focus their resources on retaining those customers, thus increasing customer lifetime value.
Modeling a Churn Prediction Algorithm
There are a few inputs that can be used to model a churn prediction algorithm. These include customer demographics and psychographics, transactions, pricing, economic factors, competitor activity, customer behavior, and customer journeys. Using AI to analyze and determine trends within these areas creates an algorithm that can better predict and reduce churn. For example, analyzing transactions like product returns and purchase frequency can help businesses pinpoint which products are popular and which ones are not. Being able to understand transaction metrics can lead to strategic business decisions, which can reduce customer churn.
There are various companies that offer algorithms for businesses to better predict churn. For example, Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help businesses train and deploy machine learning models quickly. These models can then be used to increase customer retention, which increases profitability. In these situations, it’s vital to understand that a returning customer is more valuable than a one-time customer. As seen in the graphic below, returning customers generate a greater share of revenue than first-time customers.
Identifying Trigger Events
Another area where AI can be used to reduce churn is in identifying trigger events. Trigger events are reasons why a customer leaves a company, including price hikes, service outages, and customer service interactions. Identifying these events through the use of AI is useful because customers are not usually forthcoming regarding their motivations for leaving a product or service.
AI-driven triggers can also be used to segment customers in order to personalize their experiences and increase customer satisfaction. AI can help businesses understand customer dissatisfaction by listening to them on social media platforms and other online forums. The insights gained from these conversations can then be used to identify areas of improvement and develop strategies for reducing churn.
Analyzing Customer Sentiment
As seen throughout this article, a large part of reducing churn is understanding the customer. Therefore, AI can reduce churn by analyzing customer sentiment to identify the source of dissatisfaction. Natural Language Processing (NLP) is widely used to analyze customer interactions in emails, text messages, service reviews, and phone calls. NLP is a branch of computer science and linguistics that deals with how computers can process, understand, analyze, and generate natural language. Therefore, NLP is a powerful tool that companies should use to quickly sort through and determine trends within a variety of customer interactions.
The data extracted from these NLP processes can be used to update technologies, modify products, and retrain customer service, which can improve customer sentiment and reduce churn.
Explicit vs. Presumed Churn
Not all churn can be classified into specific categories. Some churn is based on presumed customer behavior. Customers often will slowly terminate using a product or service, which can be hard to interpret without the use of AI. AI systems can identify models of behavior based on gradual or presumed churn, creating risk categories to feed results.
The risk categories are created through the use of machine learning algorithms that segment clients into these categories based on subscriber history, platform usage, and customer service interactions. Being able to identify these categories allows businesses to have a 360-degree view of their churn rate. A better understanding of these risk categories will allow organizations to make better strategic decisions and take the next best action to reduce customer churn when needed.
These next best actions, also called NBAs, include personalized pricing, personalized messages, adjusted credit limits, automated messages, and best next product offerings. The name of the game is keeping customers engaged and happy. With AI, automated messages are more personalized, which lets the customer know that the company is taking their needs and wants into consideration when making decisions.
Overall, identifying presumed churn is a useful functionality of AI that can expand a company’s competitive advantage in a marketplace where technology is becoming an integral part of the business landscape.
The use of AI can help reduce churn in a variety of ways. By analyzing customer sentiment, understanding trigger events for leaving a product or service, and identifying presumptions of when clients will leave, companies are able to make better decisions that benefit both the customer and the company. Ultimately, using AI to reduce customer churn rates is an effective strategy that companies should implement to increase customer satisfaction and remain competitive. There are even platforms like Amazon SageMaker that help companies that help track and reduce churn, as seen in the graphic below.
Ultimately, AI-driven strategies can be used to create a better experience for customers and reduce overall churn rates. Companies should take advantage of these options and implement them into their business strategies in order to remain competitive in today’s technology-driven marketplace.
About the Author:
Arek Skuza is an experienced technology leader, with over 10 years experience in project management and working with technical cross-functional and cross-organizational teams. He has expertise in AI strategy design & implementation, Robotic Process Automation implementation, Machine Learning projects leading, Artificial Intelligence-powered product launch management and Go-to-Market strategies & Data Monetization strategies. Arek has consulted for major companies such as Shell Energy, Discovery Networks and IKEA, helping to monetize & leverage data to drive sales, engagement, retention & referrals.