Predictive Analytics to Understand (and Expand) Customer Lifetime Value

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One of the holy grails for marketers is understanding customer lifetime value – how much their customers are worth to their companies now as well as their anticipated lifetime values.

The fact is customer lifetime value is becoming a more commonly-used metric among marketers. Of course, a lynchpin to customer lifetime value is retaining customers and having them become repeat purchasers.

A study on customer lifetime value conducted by FiveStars Loyalty Inc., which offers loyalty programs to businesses, illustrates this point.

More than half of customers who visit chain restaurants such as Chili’s California Pizza Kitchen, Buffalo Wild Wings, and IHOP don’t return within six months of their visits.

But the more than 70% of restaurants and other retailers that are able to get customers to return for a second time will see those customers make even more purchases (or visits) within four months.

Additionally, loyal customers generate 10 times more revenue in their lifetimes, according to the study.

Using predictive analytics and customer data to segment customers can help a company identify the most profitable customers and enable it to focus its engagement and marketing efforts on those customers that will make the biggest impact on the company’s business.

As you can see, segmentation is a critical aspect of examining and acting on customer lifetime value.

For example, overstockART.com segmented its customers into four groups based on purchase volume and repeat purchases, according to David Sasson, the company’s president and co-founder. The four groups it looked at were the top 20%; the top 10%; the top 2%; and the top 1%.

overstockART.com discovered some interesting things about its customers’ behaviors:

• The company has about 54% repeat purchasers.
• The top 20% of its customers are responsible for 60% of its revenue.
• The typical customer purchases from the company 1.6 times.
• Only 15% of customers from one year make purchases the following year.
• Its customer lifetime value averages out at $227.59.

However, a deeper dive into each customer segment reveals more telling information about the customer lifetime value of each group.

For instance, while customers in the top 20% make 3.1 purchases on average and carry a customer lifetime value of $547.15, upper echelon customers in the top 1% make 8.9 purchases with a customer lifetime value of more than $2,600.

Companies whose most valuable customers have higher customer lifetime values and also purchase more often can use predictive analytics to determine the type of messaging and offers that tend to resonate with these customers. In addition, predictive analytics can enable these firms to take the actions that will lead to additional purchases.

In many cases, doing so can help companies extend and augment each customer’s anticipated customer lifetime value. In addition, business leaders can improve customer lifetime value by focusing on maintaining customer satisfaction.

Customers who receive consistent experiences from the companies they do business with are more likely to remain loyal and spend more with those companies as well as recommend those companies to their friends and family, according to another study.

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