Harnessing the Power of a Customer's Lifetime Value

CHICAGO — Each convenience store customer is worth more than they spend on an individual shopping trip. Retailers should strive to know their customer lifetime value (CLV), a metric that is immensely useful as it can help businesses predict how much customers are likely to spend in the future, and can help them make informed decisions regarding customer acquisition and retention. 

At its core, CLV enables businesses to understand customers as individuals, not just as a broad group. The difference may seem small, but is significant. A customer who buys coffee twice a week is "fundamentally different" than an every-two-weeks coffee buyer, Jessica Shelcusky, a marketing specialist with Paytronix Systems Inc., explained during a recent webinar hosted by Convenience Store News. 

The old-school, basic CLV calculation is average spend per customer per year multiplied by the number of years before the customer churns. However, this is a very general method that is not totally accurate and can't be used for everyone.

"We know not all customers are created equal," Sheculsky said, noting that one customer may visit once a week, while another will only come in when they are lured by a coupon.

Artificial intelligence (AI) can be used to calculate CLV and understand a customer's habits much more accurately, particularly as part of a loyalty program.

The use of "AI to IA" predictive insights — artificial intelligence to individual actions — can estimate an individual customer's likelihood to interact with a brand, when they make a store visit, when they don't, what day of the week is better for them to receive a loyalty program message, and much more. Building out such profiles is part of understanding their lifetime value, Sheculsky said. 

AI can also calculate specific, important aspects of someone's CLV, including:

• Recency: How long has the person been in the loyalty program?
• Frequency: What does the average visit cadence look like?
• Latency: When was the most recent visit?
• Spend: How much do they spend, on average, per visit?
• Predicted future value: How long is this person likely to remain active?

After building a customer's profile using these factors, retailers can plan for how best to communicate with them and motivate them to come back for another visit.

"The more data we have on a consumer, the more accurate we can be when calculating their lifetime value," said Shelcusky.

In general, the ratio of CLV to the cost to acquire that customer should be around 3:1. But even if a business sets a different target, the ratio is a good way of understanding their marketing spend. Once a baseline is established, there are a number of ways to use CLV to achieve better programs. Retailers can identify their most valuable customers, see what it takes to increase the CLV of lower-value customers, effectively segment customers into groups for future campaigns, optimize acquisitions, and realize lift.

Another potential benefit of CLV is reducing the cost to acquire a customer. According to Sheculsky, there are three key ways to achieve this: 

1. Retaining customers longer: Customers can be retained longer and turned from lapsed customers into current ones through targeted 1:1 win-back campaigns based on their profiles. For example, AI can calculate three dates for the base time to reach out to them based on their demographics and specific past behavior.
2. Reducing media/advertising expenses: Media/advertising spend can be reduced by using loyalty intelligence to inform media buys and identify those customers who are most likely to make a store visit.
3. Reduce fees associated with third-party marketplaces: Retailers that know more about their customers and have the right data can push first-party ordering, especially if they offer a smooth online ordering experience.

An on-demand replay of this webinar, "The Benefit of Knowing a Customer's Lifetime Value," is available here.