Customer Lifetime Value (CLV) – The new metric linked to service Levels that might get you the worst seat on the plane
As metrics relating to customers have become more sophisticated in recent years, companies that sell to consumers have begun to realize that a customer has long-term value to the organization and that not all customers are equal in that value.
CLV or Customer Lifetime Value is a formula for assessing the value of a customer over his/her lifetime. While there are a number of variations on how this CLV statistic is calculated. It most often includes factors such as:
- Age of the customer
- Income
- Zipcode
- Life stage/situation (e.g., homeowner, employed, children, married, retired, etc.)
- Items purchased, prices and margin
- Frequency of purchases
- Returns
- Level of influence with others
Some of this data is easier to get than others and wary customers are now more careful about who can access their personal data and what is done with it. However, like or not, companies like Google, Facebook, and others have massive amounts of data on most of us, and that data is sold and are used to try to sell us more stuff. What’s good about CLV is that it rewards consumers for loyalty. What’s disconcerting about it is that some customers get really bad service because they are infrequent purchasers, always buy on sale, or simply that they are senior citizens. Companies use CLV statistics as prescriptive analytics.
In other words, the CLV of an individual customer determines the level of service they receive, discounts, and even special sale announcements. What this means is that if you take one vacation a year and always fly with one airline, you will probably wait on hold a long time to book a ticket over the phone. You also will probably be the last to board the flight, and won’t be offered free drinks or snacks and are more likely to be seated next to a baby that cries out loud than some of the passengers with higher CLV value.
Banks, Airlines, and other types of companies have actually been treating their preferred customers better than others. In the past, preferred customers were identified by one or two simple metrics like cumulative $ spent, miles flown, and visits. With the advent of CLV, identification of high-value customers is based on a wider variety of sub-metrics, and hence, likely to be more useful.
Applying CLV To B2B Companies
Eleven years ago, I proposed a new metric in my book “Beyond the Balanced Scorecard” (Productivity Press, 2007) called the Customer Relationship Index that incorporated a metric similar to CLV. The relationship index is made up of two parts:
- How attractive or important the customer is
- What level of relationship we have with the customer organization
The first client to develop this metric was a large chemical company that sold pigments to companies like the big three auto companies, as well as paint companies, and others. They found that while customer satisfaction and loyalty were steadily improving, margins were declining at a faster rate. After some analysis, we discovered that some of their most loyal customers were their worst customers. These customers paid lower prices for the products, made frequent changes to orders, expected lots of perks like sporting event tickets and golf outings, and in general, were a pain to work with.
Based on this analysis of their customers, we came up with a 1-10 scale that the President called “The Ugly Stick” that allowed my client to quarterly assess the attractiveness and importance of the customer. We did this quarterly because sometimes they got more attractive and other times they got uglier. The attractiveness metric was mostly based on objective factors like volume, customers’ financial results, the price paid and margin. Subjective factors like how difficult the client was to work with also factored in. Based on ratings from 1-10, we set a relationship target (also 1-10) for each account. For accounts at the lower end of the scale, the goal was to gradually find them another supplier (in other words, divorce). For the most attractive clients, the goal was a long-term relationship where my client was their primary supplier. This metric allowed them to be much more strategic in their customer relationship management process and eventually led to an improvement trend in margins as well as improved employee satisfaction. Getting rid of the worst customers made employees lives better a well as improving the bottom line.
Customer Lifetime Value is a step in the right direction when it comes to using big data on thousands of customer transactions to determine who your most important customers are.
Measuring customer satisfaction through a survey is a crude way of tracking this important dimension of performance. It’s well known that the majority of customers don’t respond to surveys and those that do tend to be the extremes – really delighted or really mad. Customer Lifetime Value is a step in the right direction when it comes to using big data on thousands of customer transactions to determine who your most important customers are. Where companies are still struggling is finding ways to build relationships with those high-value customers. A separate phone number for customer service or getting to board the plane first is probably not enough to build loyalty.
The more forward-thinking companies understand that relationship building is not a one size fits all strategy. Tailoring your special offers and services to what the customer considers important is how to build loyalty. While this is much easier when you have 20 big corporations as your customers, it is possible with thousands of consumers as your customers to use data like prior purchases and searches to determine what people like and what they buy, and then tailor special offers and privileges to what’s important to them.
