Early last month, The Wall Street Journal published an article describing how some companies use secret customer scores to determine how quickly you receive help when calling the customer service line. It makes sense that credit-card companies use scoring systems to determine what offers they send to customers who are likely to close their accounts. But what’s the benefit for other brands?

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The case for case prioritization

Case prioritization, as mentioned in the article, is one practical application. Higher customer lifetime value (CLV) scores get faster, better, and more customized service. It may help in retention offers as well—companies should be willing to dish out more aggressive offers to retain high CLV consumers that are upset or at risk of leaving than those with lower CLV scores.

”Companies should be willing to pay a premium on advertising to higher CLV consumers,” says DEG Strategic Planning Director Tony Toubia.

As the WSJ article stated, “the scores are just a high-tech version of what shopkeepers have done for generations—make judgements on a customer’s value based on how they look or behave. As far back as 20 years ago, academics were publishing models to calculate the future value of customers.”

Other practical applications for CLV scores can be calculating the risk of merchandise returns or ecommerce fraud, the likelihood of canceling subscriptions, and the chance someone may switch to a competitor (i.e. wireless carriers).

“CLV scores could also be used for targeted ads,” says DEG Strategic Planning Director Tony Toubia. “Companies should be willing to pay a premium on advertising to higher CLV consumers, and may even choose to suppress negative CLV consumers from future ads or offers. This could be for a person who returns 75 percent of what he or she purchases.”

Determining a “lifetime” score

CLV calculations should be fairly rigid. You want to look at the customer data during specific periods of time, so “lifetime” is a bit of a misnomer.

”Looking at when someone is an active customer, you should look at the distribution of a customer’s order cadence to better capture who is still a customer versus those who have actually lapsed,” says DEG Data Scientist Ashley Vincent.

The specific period of time should be custom based on the product or purchase lifecycle. An apparel retailer may only care about your “lifetime” value over the last year, as it indicates current momentum. However, a furniture or appliance company may want to look at the last three to five years because people make those investment purchases a little more infrequently.

“Looking at when someone is an active customer, you should look at the distribution of a customer’s order cadence to better capture who is still a customer versus those who have actually lapsed,” says DEG Data Scientist Ashley Vincent. “Making it more custom to each customer segment can help you better target future customers.”

Other variables to consider are returns of purchases and someone’s digital behavior. Both of these data points could help show brands the customer’s intentions, and whether future purchases are likely to be made or returned.

How DEG uses CLV scores

CLV scores are great for some of the examples mentioned in the WSJ article. But it’s just one of the many inputs DEG considers when building custom models used for prioritizing audience activation, content, and offers across any channel—social media, email, ecommerce, and assisted (i.e. clienteling, call center).

“What we attempt to do is identify a customer’s potential profitability instead of solely looking at what has happened in the past,” adds Vincent. “This gives you a more complete picture of what a customer is actually worth.”

“We approach calculating CLV using statistical modeling,” says Andy Warren, DEG ecommerce strategist. “It helps us calculate the anticipated window that a company can expect to retain a customer.”

Using additional factors helps give visibility into the customer’s intention that a CLV score alone wouldn’t provide. For example, if you have a mid-tier CLV score but are highly engaged in email and have three items in your cart, you should be recognized as a higher value customer than someone with the same CLV score but little to no engagement in email and nothing in the cart.

“We approach calculating CLV using statistical modeling,” says Andy Warren, DEG ecommerce strategist. “It helps us calculate the anticipated window that a company can expect to retain a customer. Then, it applies gross profit projections to that window to determine the overall value of the customer.”

CLV scores are typically used in a very binary manner, like subscriptions—you’re either on or off. When you start applying CLV scores to retail, you have to get creative with how those binary values are calculated, as it isn’t as clear as being subscribed or unsubscribed.

In addition, certain companies and industries naturally experience a lot of customer churn. That’s why developing a model that helps determine when the off switch should be flipped gives you a more realistic view of when a customer might lapse.

Business applications for CLV scores

One beneficial way to use a CLV score is to develop variables defining the different phases of your customer journey in your calculation. It provides you with the ability to diagnose issues that may exist within your business areas based on the phases of your customer journey.

”Your CLV can be based on a number of things and should vary by brand,” says Travis Mccan, DEG senior relationship marketing strategist.

For instance, if you look into a month that shows poor performance numbers, you may find that your retention rate slipped or that you aren’t having as many lapsed users return as in the past. This provides actionable information that you can use to fuel marketing tactics to immediately fix ailments in specific business areas.

“Your CLV can be based on a number of things and should vary by brand,” says Travis Mccan, DEG senior relationship marketing strategist. “For example, companies in need of a PR boost could consider scoring individuals who publicly support the company higher for helping draw people in and reassure past customers.”

It’s also useful to look at CLV scores as it applies to customer segmentation, which allows you to send timely communication to different segments based on what you see in the variables from their CLV calculation.

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For example, males aged 54-65 may have a great retention rate but low acquisition rates while females aged 24-35 may have low average order values. Knowing specific data allows you to fix business issues within each segment as opposed to making broader changes to your business and hoping it affects all segments.

Interested in calculating CLV scores for your business? We’d love to discuss options and provide insights into how to best approach setting up better customer targeting with you. Send us a note and we’ll set up a time to talk.

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