Churn prediction

What is churn prediction?

Churn prediction is the process of predicting users who are likely to stop using a product or service in the near future. Here, the user group could mean customers or employees of a company. Churn prediction is done using churn prediction models, which employ historical data, behavior patterns, and AI techniques to predict the likelihood of the attrition. By predicting when they are supposed to leave, the company can take the right step, engage with the leaving customer, and make it stop, because it’s more expensive to gain a new customer than retaining an existing one.

An example of churn prediction A SaaS company uses churn prediction analysis to monitor customers who are likely to stop subscribing to their service once the plan expires for them. By enabling real-time tracking for churn predictions, they track the following data in real time.

• How much time they spend on the application?
• Lack of interaction to notifications, emails, and other communications.
• Reviews and ratings given on external websites, play store, istore, and more.
• Reported customer support issues and negative interactions.
• Payment attempts and failures.

This helps them stay ahead, sending the right content or recommendation to the user, probably a discount, a freebie, or a clubbed offer, keeping them on track with the product and preventing them from leaving.

Why churn prediction is important?

Churn prediction is important for the following reasons:

• Could retain a current customer and improve the customer lifetime value. Prevents revenue loss by reducing customer retention costs and loss of a valuable customer.
• More personalized marketing rather than blanketed and broadcast messaging.
• Gains more insights into how customers engage with a brand.
• Shows customers that they are valued. Gains their trust.
• Data analytics & AI for churn prediction and prevention.

It’s all about the early warning signs, because a customer won’t stop using a product at the drop of a hat. AI and ML could help analyze and look for these signs, where finding them at scale is not possible manually. Here’s why AI is the right technology for churn prediction and prevention.

Identifies risky behavior: Every company could have a specific pattern to be traced to find the at-risk customers. For example, usage frequency reduction, low-star ratings, less engagement to emails, pausing the auto-pay option, and the like. By identifying and assigning risk weightage score to these actions, a company can predict these set of users.

Segments customers based on risk levels: The above set of users are further divided into high-risk, mid-risk, and low-risk categories, so there could be a tailored strategy for each division.

Personalized interventions, on time: With the help of AI, the personalization could be curated for each category: rewards and discounts appreciating their stay, product tour and hack tutorials to help them if they are stuck, one-on-one support assistance, gamified personalization, etc.

Better retention campaigns: Based on what works for a user and what doesn’t, the model learns better, improving its personalization and suggesting the high impact retention strategy – what the user needs from the company.

There’s space for continuous monitoring: The monitoring continues, and the model learns better from diverse, new data. And, the business thrives, serving its customers the right way.

Churn prediction – use cases

Churn prediction works for a lot of industries, use cases, and functional areas. Here are some specific areas where it can help.

eCommerce and retail: There are many customers who don’t return for a long time after purchase, similarly eCommerce has users who check out, add products to cart, buy once and disappear. Churn prediction can predict the likelihood of a customer who won’t come back, so the company can run targeted offers.

Telecommunications: Customers who face poor quality, connection issues, or find better plan availability with other providers might leave sooner, which could be predicted through churn prediction & analytics.

SaaS: SaaS industry requires churn prediction the most, and they could navigate it better with proactive measures, as it has access to billing history, support, login frequency, and more. Streaming services: Many streaming services already have churn prediction techniques, targeting users with low watch time, less engagement with messaging, or renewal cancellations.

Banks and financial institutions: Similar to retail and SaaS, insurance companies require churn prediction to stop users from renewing their premiums and plans, so they could offer coverage adjustments, offers, and personalized care.

Travel and hospitality: Booking companies, hotels, airlines companies, and hospitality companies identify customers who haven’t booked or traveled through them.

What you need to know about churn prediction?

It’s not a one-time thing. It’s a continuous process. The more equipped you are with a better monitoring tool, the more satisfied your customers will be.

Anyone can get started with churn prediction. It doesn’t require years of data to get started with churn prediction in most cases. In many cases, even the smallest amount of data has brought huge wins to many.

It’s all about the most innocuous early warning signs. And, in most cases, they are undetectable. When tapped right, this can turn a leaving customer to a loyal one.

Churn prediction is more about knowing why a customer leave, rather than knowing who leaves and when. It’s more comprehensive than one can imagine. This is what makes it more potent and powerful, as knowing why gives you the power to curate the right intent-driving offer for the customer.