What is Churn Prediction?

 

Churn Prediction is the process of predicting which customers are most likely to cancel a subscription, i.e. “leave a company,” based on their usage of the service.

Churn quantifies the number of clients who have left your brand by canceling their subscription or stopped paying for your services. This is bad news for any business as it costs five times as much to attract a new client as it does to keep being one. A high client churn rate will hit your company’s finances hard. By using advanced artificial intelligence ways like machine learning, you’ll be capable to anticipate implicit churners who are about to abandon your services.

Why is Churn Prediction Important?

Client churn is a common problem across businesses in numerous sectors. However, you have to invest in acquiring new guests, if you want to grow as a company. Every time a customer leaves, it turns out to be a significant investment loss. Both time and trouble need to be conducted into replacing them. Being suitable to prognosticate when a customer is likely to leave, and offer them impulses to stay, can offer huge savings to a business.

As a result, understanding what keeps guests engaged is extremely precious knowledge, as it can help you to develop your retention strategies, and to roll out functional practices aimed at keeping clients from walking out the door.

Predicting churn is a fact of life for any subscription business, and indeed slight fluctuations in churn can have a significant impact on your nethermost line.

How does Churn Prediction work?

Reliable client segmentation

Churn prediction is totally dependent on the utilization of your company’s historical customer data. Customer analytics will be required to accurately estimate how customer attrition affects your business.

Begin by exporting any past data types that may have an impact on a customer’s chance of churn.

Demographics and behavioral data

Determines if any user or an individual is utilizing your product on behalf of their company?

How frequently do they utilize the product in general and the individual features that are offered to them?

How frequently does this person contact your customer service?

Earnings information for each client

Analyses the Subscription date, does this user have a long history of subscriptions or are they new?

The amount of MRR this customer is personally accountable for — of course, when implementing your churn estimate, you’ll want to prioritize high-value, at-risk clients first.

Contract terms

What plan/tier does this customer have?

How much time do they have until their plan expires? This is a very significant piece of data to have, as delinquent credit card churn is the churn that practically any SaaS firm will be most vulnerable to, and one of the most difficult to avoid and undo.

Once you have the necessary historical data, separate your clients into relevant churn prediction segments, such as the following:

Customers that receive many upgrades on a daily basis (low risk of churn)

Customers who contact on a frequent basis (through support tickets/phone calls/upgrade requests) (low risk of churn)

Customers whose usage of the product has decreased in the recent year (high risk of churn)

Customers that registered but did not complete the on-boarding process (high risk of churn)

Customers who have never submitted a support ticket or have submitted numerous comparable support tickets (high risk of churn)

How to Use a Churn Reduction Model

Once your model is created, you can integrate it with other technologies, like your client relationship operation system. Adding a score to each client profile can help your sales crew prioritize certain connections and proactively reach out to retain them.

For illustration, if your sales representative sees that a client enters the risk zone, they can call that individual and offer a promotional deal in an attempt to avoid churn.

You can also integrate the model results into your crusade supervision system. In this situation, you can proactively shoot high-threat client tickets or substantiated offers in the possibility of retaining them. This process can be fully automated, making visionary retention extremely easy.

At FutureAnalytica.ai, we build customer churn models with 1 Click ML deployment and unlimited potential use cases that aim to minimize customer churn with advanced analytics, improve network reliability, and spot network anomalies. Schedule a free demo and Get Connected at info@futureanalytica.com.