Reducing Customer Churn with AI: Turning Insights into Retention

Imagine you organize a movie night for all your friends. Everything is going well, you have the perfect snacks, everyone is watching an amazing movie, and everyone is having a blast. But then, you make a change to your plan and switch to a wildlife documentary. One by one, your friends start leaving, and you’re left with your snacks and the wildlife documentary.

This is what customer churn is like. Initially, the product captures a large market, but over time, due to certain features or changes, users begin to leave, resulting in a higher customer churn rate and low product growth.

According to a report by Forbes acquiring a new customer can cost five times to seven times more than retaining an old one.

High churn not only affects revenue but also impacts brand reputation and growth potential. Here comes AI, AI can not only help SaaS companies to retain their users but also help them understand user preferences.

In this blog, we’ll explore the role of AI in reducing customer churn, along with real-world examples of successful implementation.

What is Customer Churn?

Customer churn is defined as the loss of customers over a given time. Typically, it can be expressed by a simple formula:

Total Churn = (total no. of customer loss/total number of customers)*100

In business terms the higher the churn rate, the higher the amount of users leaving the product. This is the time when users start exploring other companies to retain their users, companies should start making changes based on user preferences.

Why do Churns Matter?

Success in company growth is highly correlated with success in reducing churn. The lesser the amount of churn the more customer retention and customer engagement.

Churn reduction helps in:

● Increasing customer lifetime value or CLV

● Improving profitability

● Increasing brand loyalty

● Improving customer retention rate

● Helps in building product stability

The impact of customer churn is huge, it not only impacts the company financially but also impacts the company’s reputation.

How Can AI Help Reduce Customer Churn?

AI presents several strategies through which companies can spot at-risk customers and then take proactive measures to retain them.

AI can help companies in effectively predicting churn and can suggest some steps that help to prevent customer churn.

Below we have mentioned four strategies to reduce customer churn with the help of AI.

1: Know your users with the help of AI

Knowing your users means using AI tools to analyze the user’s interests, purchase frequency, etc. Use AI analytics tools to categorize data from various sources like social media, website interaction, and feedback.

This will give you a holistic profile of the user which will help you to understand their previous interactions, purchasing habits, and demographic information.

2. User Segmentation

Customer segmentation is a process of dividing a large amount of data into smaller sets. Instead of treating all users the same, companies need to break them into different segments based on preferences and interests.

This process helps companies to align their marketing campaigns with user’s preferences.

3. Personalized Marketing Strategies

Personalized marketing strategies are like giving customized invitations to your users. Instead of following the traditional and generic campaigns, you can now understand the user preferences and can create offers and messages that will attract more users and make them feel special.

Approximately 35% of Amazon’s revenue comes from personalized product recommendations: A report by McKinsey & Company.

Personalization helps to enhance user engagement and ultimately reduces the customer churn rate.

Implementation of Personalized Marketing Strategies

● Analysis of Data: Use AI algorithms to examine client data and segment them based on similarities in specific features.

● Customized Marketing: Design customized messages and offers to each segment depending on their preferences and behaviors.

● Targeted Retention Programs: Design targeted retention programs for each subgroup to address their requirements.

Companies spend so much on acquiring new users and usually overlook customer churn. However, retaining old users is as necessary as acquiring new ones.

4. Dynamic Pricing

Dynamic pricing is a process that helps companies to adjust the prices of their products based on real-time data processing.

A report by McKinsey found that companies using dynamic pricing can increase their revenue by up to 5% while simultaneously reducing churn by as much as 10%. This approach is highly effective since it is beneficial for both sides (company and users).

For example: Amazon, a leader in dynamic pricing, reportedly changes the prices of its products up to 2.5 million times daily.

This dynamic pricing is done with the help of various data points such as user activity, competitor pricing, inventory, order history, and expected margin. With the help of dynamic pricing, companies can easily understand users’ willingness to pay and adjust prices accordingly.

5. Automated Customer Support with Chatbots

User queries come up every minute of the day, and users need quick and instant replies to their queries. Human intervention to answer these queries is very costly and time-consuming. Here comes AI in the picture to make things easy. With the help of AI chatbots, companies can easily handle user queries in no time.

A survey by HubSpot revealed that 90% of customers expect an “immediate” response when they have a service question. And AI-powered Chatbots are best in the game since they are available 24*7.

Chatbots can handle the majority of user queries since many times most of the users have similar queries related to the product.It can provide immediate, personalized, and data-driven support to the users.

The Impact of AI-Driven User Strategies

Many companies have been using AI for better user retention and for giving personalized experiences to their users.

In past years we have seen many companies using AI and the results have been phenomenal, not only were they able to retain their users but also maintained their reputation.

Some of the most popular and successful examples of companies using AI in their business model are:

1: Paypal

Paypal is a platform that operates an online payment system that allows users to easily transfer money. Paypal is the most reliable and easy-to-use platform for transactions and it is greatly appreciated by users.

To be at the top, you need to use some of the best technologies to make your product more compatible. PayPal uses AI algorithms to analyze customers’ transaction histories, preferences, and behaviors. By gathering insights from these patterns, it suggests the best payment methods for seamless and convenient transactions.

2. American Express

American Express also known as AMEX is a multinational financial services corporation and bank holding company that is mostly popular as a credit card provider and for its AMEX reward points and travel perks.

American Express was one of the first financial services companies to implement Artificial Intelligence into their system, initially, it was for fraud detection but later on, it was scaled up for better user retention and for generating personalized reward points.

A report by Digital Initiative Harvard mentioned that: using AI to build strategies helped AMEX to improve their churn report metrics by 20%.

3. Intuit

Intuit is an American Finance Company, it is best known for its various finance software. Intuit emphasizes empowering small businesses to achieve their financial goals and targets, with the help of their products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp.

Intuit uses artificial intelligence to help its users to make better financial decisions and to solve their queries. The main aim of Intuit using AI is to solve their user’s most occurring financial problems. Intuit uses AI for personalized Financial Assistance, Smart Automation, and Fraud Detection.

4. Spotify

Spotify is holding a dominant position in this competitive music market but it was never easy. It was done with the help of various marketing strategies, personalized campaigns, and a perfect user interface.

Spotify used AI to analyze user’s data and develop strategies for its marketing campaign, such as “ Spotify Wrapped 2022” and “Only You”.

These marketing campaigns were such a hit that Spotify reported a 26% increase in Monthly Active Users (reaching 574 million) and a 16% increase in subscribers (totaling 226 million).

5. Netflix

Netflix is the leading subscription-based streaming service that offers a wide range of movies, documentaries, and television series to its users.

Netflix used AI which significantly improved its user engagement and helped to boost their revenue and subscribers’ growth. In 2023, Netflix had approximately 8.9 million new subscribers bringing its total to 238.4 million subscribers worldwide.

Netflix uses AI in its recommendations system which helps them to boost user engagement and reduce user churn.

Netflix uses collaborative filtering, content-based filtering, and deep learning technology to generate personalized content for individual users. This strategy not only helps the viewers by showing them content they like but also helps Netflix maintain good relationships with its audience. Since about 80% of content watched on Netflix comes from its recommendation system, it plays a crucial role in user engagement.

6.AT&T

AT&T also known as American Telephone and Telegraph is the world’s largest telecommunications company. It is the largest provider of mobile and landline telephone services in the United States.

AT&T noticed a high churn rate in 2023 in its postpaid plan. The churn rate was around 0.95%. This means that out of all the postpaid users 0.95% were leaving the service.

AT&T used AI tools to analyze the problem and the reasons why the users were leaving the postpaid plan. By using Machine Learning, Artificial Intelligence, and Data Analysis, AT&T was able to predict which users were going to leave and what particular reasons were responsible for identifying key factors such as pricing dissatisfaction, network issues, and poor customer service interactions which helped them to make appropriate changes in their plans and making the service more user-friendly.

By early 2024 their churn rate for the postpaid plan had dropped to 0.89% showing that AT&T was successful in implementing their strategies led by AI.

FAQs

1. What is Customer Churn?

Customer churn is defined as the loss of customers over a given period. Typically, it can be expressed as a proportion of total users. This has recently been characterized by significant turnover rates. This could indicate unhappiness with the product or service, as well as market shifts and greater competition from other firms. client turnover is important because businesses believe that knowing how to reduce churn is essential for client retention.

2. How is Customer Churn Prediction done?

Customer Churn prediction is done with the help of the following data points:

1. Data Collection

2. Feature Selection

3. Modeling

4. Validation

5. Implementation

3. What are the main causes of customer churn?

Some of the main causes of customer churn are mentioned below:

● Poor customer service

● Lack of engagement with the customer

● Unmet product expectations

● High prices

● Unsolved customer queries.

4. How does AI/ML technology reduce Customer Churn?

AI and machine learning help reduce user churn by identifying at-risk users, personalizing interactions, optimizing support services, and anticipating trends, ultimately increasing user satisfaction.