Clarity Blog
Clarity Insights Blog

How a deeper understanding of customer data and predictive analytics can prevent churn

Posted by Mark Lewis | May 1, 2017 9:11:13 AM
Clarity - Blog Image - 4-28-17-01-2-1.jpg

On the surface, customer data can reveal a nearly endless list of insights. However, being able to pinpoint the next best action to offer customers can be one of the most powerful tools in a company's arsenal. One study found that reducing churn by 5 percent can result in a 25 percent increase in profits. But what should be factored into the decision about the next best action to offer? Let's take a look:


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

Predictive analytics: Gathering the right data

The first step in creating this variation of predictive analytics must be collecting the right data. If the appropriate information isn't factored into the analytics model, churn analysis cannot be completed successfully. 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: It's also imperative to understand when the customer's relationship began with the company, and when the shopper's most recent interaction took place. 

Providing the right message that aligns with each customer's specific predilections and offering this action on his or her preferred channel is critical. However, the next best action must also be provided to the client at the right time.

Timing is everything: Stages of the customer journey

For this reason, analysts should also factor in the different stages of the customer journey, as well as more personal client life events. According to customer experience consultant Kerry Bodine, the typical customer journey includes a similar set of milestones. Once customers have selected the offering that matches their needs and completed their transactions, there are a few avenues that they can take next.

A problem could emerge and the client 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. The final possibility here is churn, where the customer doesn't initiate any further engagement and is no longer a customer.

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

Pinpointing where each consumer is in his or her 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 at hand, an organization is in a better position to understand its customers' needs and preferences, and provide 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 utilized in different ways, depending upon the merchandise or services a business provides and the industry in which it operates. However, there are a few questions organizations should ask themselves 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 this are essential for providing an enjoyable customer experience and encouraging brand loyalty. However, this process does require appropriate collection of the right data, specific technology and expert support. To find out more, contact Clarity Insights today.

«Previous Article
Next Article»

Written by Mark Lewis

Recent Posts


see all