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Why you should be using customer data and predictive analytics to prevent churn

Posted by Clarity Insights

Clarity - Blog Image - 4-28-17-01-2-1.jpgCustomer data is the key to practically everything—from personalization to product development to world peace (ok, maybe not that). And coupled with predictive analytics, customer data can help you prevent churn. 

How? Being able to pinpoint the next best action to offer customers can curb attrition. One study found that reducing churn by 5 percent can result in a 25 to 95 percent increase in profits. But how do you figure out the next best action to offer? Let's take a look.

Predictive Analytics: Gathering The Right Data

"The ability to pinpoint the optimal next best action hinges upon predictive metrics."

The first step in creating this variation of predictive analytics is collecting the right data (obviously). If the appropriate information isn't factored into the analytics model, your analysis won't work. Next best action analytics should center around:

  • Customer behavior and preferences: Taking a look at past interactions and the services the customer has used previously can help map overall behavior and tastes.
  • Cross-channel communications: In addition to taking a look at the interactions themselves, it's also important to consider the channels used to support customer activity.
  • Product purchase lifecycle: Understand when the customer's relationship began with the company and when the shopper's most recent interaction took place. 

Next best offer hinges on a few things: 1) providing the right message based on the customer's specific predilections, 2) offering this action via their preferred channel, and 3) making the offer at the right time.


Timing is everything: Stages of the customer journey

Since you want to make the offer at the right time, you should also factor in the different stages of the customer journey and personal life events. Once your customer becomes a customer by purchasing a product or offering, there are a few steps they can take next on their journey from awareness to advocacy.

A problem could emerge and the customer will reach out to the company for a resolution, or the customer may love the product or service they've selected and look to engage with the company further. Another possibility here is churn, where the customer doesn't initiate any further engagement and ceases to be a customer.

journey map

The life events a customer is experiencing could greatly impact the interaction he or she has with a business.

Pinpointing where each customer is in their journey is a powerful factor to include in predictive, next-best-action analytics efforts. But it's also important to consider more individual life events that could impact a customer's decision. Milestones like getting married, buying a house, having children or retiring—among a host of other events—can be a significant element in determining how customers choose their paths.

With these metrics in hand, an organization is in a better position to understand its customers' needs and preferences, providing the next best action before churn takes place.


Analytics modeling: Length of lifecycle and time of churn 

As noted, it's critical to gather the right kind of information in order to support the analytics model. When it comes to this analysis, there are two main models organizations can utilize: 

  1. Length of lifecycle: This model hinges upon data about customer interactions, the individual stages of a shopper's customer journey and other historical information. This model is then able to predict the length of a customer's lifecycle, or how long the relationship will last with the business. 
  2. Pinpointing customer churn: This model will include data more associated with risk, including customer service contact or returns. This analysis enables a company to identify the moment at which a shopper will churn. 

The results of these models can be leveraged in different ways, depending on the merchandise or services you're providing and the industry in which you operate. There are a few questions organizations should ask as they seek to translate these insights to business outcomes:

  • Can the business provide an enticing offer that will specifically appeal to the customer?
  • Will this offer successfully prevent the customer from churning?
  • Does it make economic sense to pursue this customer? Has the shopper been loyal in the past, or would it be a better use of the company's resources to pursue another at-risk client, or attract new shoppers? 

There is also the potential to automate the process a company uses to recommend the next best action. For instance, the analytics model can be scored so that the system will automatically provide an offer or other next best action depending upon the specific behavior a customer exhibits. 

Advanced analytics like these are essential for providing an enjoyable customer experience and encouraging brand loyalty. Ready to level up? Check out our ebook, Sentiment Analysis: the Key to Customer Behavior Insights to see how best-in-class companies are learning more about their customers.

Editor's note: this blog post was originally published May 1, 2017 and has been updated to include new, relevant information.

 

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Written by Clarity Insights

Clarity Insights is a strategic partner to the nation's leading data-driven brands.


Topics: Customer Experience, Predictive Analytics

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