Tech Crystal Ball: How to Minimize Customer Churn with Predictive Analytics

By looking at heaps of data, your data analysis tool can pinpoint various associations which indicate a tendency to terminate a customer’s relationship with you.

Before that though, you would have to run data mining to source the required information.

Data mining means exploring in two different directions: a visual one through graphs of dependencies, and a computational one based on decision trees and clustering methods.

Both are used to identify groups of customers that are most likely to churn.

When trying to prevent churning, it is imperative to find the best indicators of such a step.

For this purpose, you can compile an exhaustive list of all the factors that could have an impact, and then use predictive analytics to select those which has the highest probability in relation to particular customers.

Using machine learning offers the advantage of uncovering complex patterns between multiple factors, such as making the connection between demographics, the type of service used, and the features of the service which are essential to the customer.

Social Propagation The concept of social media influencing has become very common by now, but there’s also its flip side as it works in both positive and negative directions.

For example, if somebody in a group of friends stops buying from a certain brand, they are very likely to influence their group to do the same.

For brands, identifying such influencers and their sentiment could be valuable.

Applying predictive analytics is what can help to uncover this critical information.

The way to do this is to analyze social media for relationships between your subscribers.

As soon as the company identifies a person with a high potential to churn, this one should be targeted with a better offer before they quit and influence their entire group.

Also, if more friends have already switched to a competitor, the peer pressure could act as a determining factor and influence your customer’s decision to switch to a competitor too.

Cost Efficiency The biggest motivation for using predictive analytics against customer churn is a game of costs.

With an unlimited budget, you could target all existing and prospective customers.

However, in a real economic setting, this is impossible, therefore, choices should be made.

First, you need to compute the profitability of each customer and focus on retaining the most valuable of them.

Then, look at the costs of maintaining communication with a customer, discount rates offered, and their lifetime value versus the opportunity to use this budget for attracting new customers, as mentioned in the previous section.

Lastly, think about how you could use your current clients as social influencers and turn them into your micro-brand ambassadors in the groups of their friends.

Voice of the Customer Sometimes you can keep your customers pleased only by listening to their voice.

That means interpreting results from surveys, reviews, comments on your brand’s social media profiles, as well as analyzing their customer service requests.

Through text analytics, you can identify factors suggesting the probability of churn and see how it applies to your company.

Also, you can see which of such factors are most likely to lead to customer drop-out.

Once you identify these signals in what your customers are saying about you, it’s time to act.

Make sure that once you solve the problem in question, you inform the customer about the steps you’ve taken  to fix it, and express your appreciation of their important contribution to your business improvement.

In addition to this function, the data retrieved from scanning the voice of the customer can also show you emerging trends and give your marketing team ideas about future promotions.

Take a Progressive Approach A good customer churn prediction model looks at the problem in consecutive steps corresponding to the customer lifecycle.

It’s also possible to create different models to address customers at various stages – such as recent customers, the ones referred by other customers, or loyal ones who start showing signs of churn.

Another way to prevent customer churn with predictive analytics is to look at a single customer’s lifecycle and compute survival rates at relevant time intervals measured in months, years or even days if your product or service is more frequently bought.

Predictive analytic models can help organizations plan ahead.

Such analysis helps to identify the most likely reasons for customer  churn as well as to detect the customers with the highest churn probability.

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