Imagine that one of your customers is unsubscribing from your product. The reasons they might have for unsubscribing could vary from high pricing or unfulfilled product expectations. Unfortunately, they didn’t tell you they were leaving, and by the time you realized you’d lost a subscriber, they’d already signed on with one of your competitors.
This is known as customer churn. Understanding the reasons behind churn can be the key to reducing it and remaining profitable in the long run. However, using manual approaches can be difficult and time-consuming. AI is far better suited to the task. In this article, we’ll explore why, including strategies to use AI to reduce churn and retain your customers.
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What is churn in business?
In its simplest terms, customer churn refers to the percentage of customers who stop using a company’s products or services within a particular period. On the other side of the equation, customer retention rate refers to how many customers stick with the company.
To calculate churn rate, divide the number of customers you lost during a specific time period by the number of customers you had at the start of that time period as follows:
Lost customers / Total customers at the start of the time period * 100
The negative impact of customer churn
The biggest impact churn has on businesses is reduced revenue. As customers abandon the company, the amount of income it makes from those consumers decreases. While retaining 100% of your customers is not practical, keeping as many of them with the company over the long term offers the best lifetime value per customer.
Most business owners know that it’s more expensive to acquire new customers than it is to retain existing ones—five to seven times more, to be exact. Churn, therefore, not only reduces income through lowered revenues but also costs the company money in the long run.
Aside from the financial implications, churn also affects a business’s long-term sustainability. High churn rates impact the business’s customer loyalty base, negatively affecting the company’s future growth.
Dealing with churn is necessary for any company that wants to grow and compete in a dynamic market. And learning what causes churn can be made quicker and easier with AI, with more accurate results than manual analysis.
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How to reduce customer churn with AI
To reduce churn with AI, a machine learning model is used to describe the buyer or subscriber, along with all the factors that may lead them to leave a company. Using this model, AI can reliably determine the chances of a particular consumer leaving the company at any time.
AI leverages several methods for determining whether churn is likely to happen, each building on the AI’s inputs for each of these customers.
1. AI-Driven churn prediction
Human behavior is a highly complex mesh of interactions. AI can use large datasets and pattern recognition to determine which customers will most likely leave by using parameters to model their actions and make predictions. Among the key inputs that AI can use for customer churn prediction include:
- Customer journeys: At which stage is the customer on their journey path? How can we make their transition to the next step of the customer journey easier?
- Customer satisfaction: Have you measured customer satisfaction with your product or service? What can you do to increase their satisfaction with your service or product?
- Competitor activity: Is the competitor closer to the consumer geographically? What about their pricing compared to yours? Are they advertising in different channels?
- Pricing: Is the customer on a promo pricing plan? How expensive are your shipping rates and processing fees? Have you raised your prices recently?
- Customer demographics and psychographics: How old are your consumers? What are their incomes and job types? How big are their families?
- Transaction history: How often do they buy? Have they ever returned a product? What is their subscription or purchase history with your company like?
- Economic factors: Are customers more likely to buy during a particular season? How are unemployment rates in their area? What about bank interest rates?
- Customer behavior: How often do customers use your product? Do they have preferences regarding how your service or product performs?
Each of these questions could have many answers, but they help form a visualization of which customers are most likely to stop buying from the business.
2. Identifying trigger events
A business can’t act too soon or too late when it comes to retaining a customer—the timing has to be just right. Certain events (known as trigger events) might lead to a customer considering not doing business with a company.
Consider Smugmug, a photo storage tool for professional photographers, which saw a massive dropoff in subscribers after the first month. On inspecting the customer feedback, AI was able to determine that a bug in the payment system led to customers not being charged and ending their relationship with Smugmug. A quick bug fix immediately improved the company’s churn rate.
Trigger events can happen for any number of reasons, from price hikes to bad press, to bugs. Customers react to these quickly, but with the right feedback system in place, a company could learn about why the customer left the company. However, the best way to adapt to unhappy customers is to find out they’re unhappy before they leave.
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3. Automated engagement
AI is only as good as its inputs, and these inputs must remain current to have the best chance to reduce churn with AI. For this reason, businesses should consistently gather feedback from customers while they’re active and once they leave. This can help the AI model their behavior more accurately for future cases.
Consider a subset of customers who subscribed because of a free month promo but don’t intend to stay on beyond that. The AI could potentially spot that the customers will unsubscribe at a particular date and pre-emptively offer another promo month at 50% off, saving a potential unsubscribe. AI may not be at this point just yet, but with the current rate of development, it’s only a matter of time before it gets there.
Automated engagement with an eye on the big picture is something that AI excels at providing to businesses. While it may be some time before we can have AI at the level of engaging individual customers directly, we do have AI that can use machine learning based on user feedback to determine what is the most probable behavior for a subset of consumers.
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The best time for retention is while the customer is present
A company doesn’t know a customer is going to leave them until the customer quits. However, several signs can tell a business that a consumer is unhappy and planning to end their relationship with the business. One of the most powerful metrics for this is customer feedback.
Many businesses don’t communicate with their customers in a meaningful way. Much of the communication is one-sided, with the consumer being bombarded with offers and discounts or sales that reduce the company’s prices. Yet few of them reach out to customers to ask them what they want or if they’re happy with the current relationships they have with the company.
How AI automates the customer retention process through customer feedback
Customer retention starts with developing a personalized look at these consumers. One of the most powerful reasons AI is well-suited to this process is machine learning (ML). Continual interaction with the customer (through surveys, social media interactions, help desk tickets, and more), can build a significant body of data which can then be used to train AI via machine learning.
The biggest issue businesses have with reducing churn is being too granular. Addressing customers as individual elements takes a lot of time and effort and is no more effective than taking customers as an archetype. Focusing on the big picture makes it easier for businesses to retain customers and reduce churn. AI is a key element to building that big picture.
AI may be the solution to churn
With innovations in AI technology, improvements in analytics, and machine learning becoming more prevalent, AI can help businesses understand why customers churn and make fixes accordingly to reduce it.
For example, Idiomatic’s solution helps reduce customer churn by analyzing customer feedback to understand which issues make customers churn —all based on their communication with and about a company. Idiomatic gathers customer feedback from all feedback data sources, including chatbots, support tickets, phone calls, app reviews and social media comments and analyzes it at scale to uncover the issues that made and potentially will make customers churn .In our Smugmug example above, Idiomatic identified bugs in recurring payments through the analysis of customer feedback, effectively reducing churn immediately upon fixing the issue.
Idiomatic’s robust reports also allow businesses to drill down into churn rate per customer issue to prioritize them in order of severity, as well as segment customers to only view feedback from churned customers.
Ready to start reducing churn? Request a no-obligation, free demo to learn more about how Idiomatic can help your business identify the key issues that cause churn, and fix them before you lose customers.
