BrainTrust Query: Is Customer Lifetime Value a Waste of Time?
Commentary by Mark Price, Managing Partner of M Squared Group
Through a special
arrangement, presented here for discussion is a summary of a current article
from Cultivating Your Customers, the M Squared
Group blog.
Should retailers measure performance based on customer lifetime
value (CLV)? CLV can be useful in certain situations, but I have found that
in many organizations CLV becomes so complex that it is rarely successful.
CLV
is a marketing measurement that combines (1) anticipated length of relationship
with (2) anticipated customer financial value (discounted to reflect the net
present value of future cash flows) to create a predicted measure of how profitable
a customer will be.
If a customer were forecast to be retained for five years
with an average spending of $1,000 per year, for example, then their total
value would be $5,000 (in a simple example, where future cash flows are not
discounted). If you add up the calculation for all customers, you would then
be able to value the business as a whole, since the value of a business is
really just the anticipated future profit from its customer base.
Given how
far-reaching the impact of CLV could be, it is not surprising to discover the
calculation frequently debated.
- Should an average length of relationship be used, or should the company
spend the time (and money) to do predictive modeling for each individual
customer?
- Should you use a customer’s past performance, or use predictive modeling
to forecast future revenue and margin instead?
- Do you use gross profit or net profit and, if it’s net profit, what
are the allocations of corporate expenses? And so on …
You can imagine how political the discussion gets, especially about the allocation
of fixed costs to different business units, customers and marketing programs. Rarely
does a company arrive at a solution that makes everybody happy. As a result,
when CLV is positioned as a corporate performance metric, it is, in fact, rarely
implemented at all. There are just too many vested interests involved. Not
only that, but evaluating performance based on predicted future revenue can
be a tough nut for non-quantitative people to swallow. “How do you know
that the prediction is going to work out that way, anyway,” they might
say?
In fact, using CLV in marketing is the only place I have seen the metric
work. Even then, it requires training of marketing management before it is
accepted.
CLV is an example of a theme that I have been focusing on recently. The value
of a metric must be driven by how understandable that metric is and what you
can do with the results of the analysis. If it’s not understood and you can’t
figure out what the action is, I recommend that you not do it. For all organizations,
except the most sophisticated, I am afraid that CLV falls in that category
— for now. That may change in the future as predictive modeling and other
statistical techniques become more widely accepted, but for now basic metrics
achieve over 80 percent of the value with less than 20 percent of the work.
Discussion Questions: Is there a feasible way to evaluate the value of
customers — either generically or individually? Do you think customer
value calculations are worth the trouble?
Calculating CLV is just a means to an end. The CLV calculations are relatively simple; the hard part is figuring our how to change firm actions (e.g., customer targeting, resource allocation, financial reporting) once the CLV estimates are available. Until firms have the right organizational structure, incentive systems, and an overall analytic mindset in place, then calculating CLV, per se, is not a very helpful exercise.
I think Mark’s point at the end is key: “The value of a metric must be driven by how understandable that metric is and what you can do with the results of the analysis.” Everyone should print that out and hang it on their wall so that they can see that every day.
What is CLV trying to measure? That’s where the debate comes from. I see CLV as a way to explore customer potential–what is a customer’s potential to spend with us, and how much of that am I capturing vs. ought to be capturing? Most people can’t hold more than a group of 7 in their heads easily, so I’d say, stick with this at a customer segment level (a whole ‘nother can of worms–getting people to agree on what the segments are). At an individual customer level, it’s better to look at RFM and trending in RFM at a micro-level. But CLV has a place–as long as you’re not using it as a base for your financial plans for the next 5 years, but as a way to make sure you’re maximizing your opportunities to serve your customer base.
I’m a fan, at least in philosophy, of what customer lifetime value (CLV) is all about, however, I’m not too sure that I buy into the formula and equation that drive the measured results. I think my comfort level is to “think” CLV but not get hung up on the metrics.
I agree, CLV is a complex measurement and how people measure it differs. That said, it is one of the most useful tools at retail and needs to be used more. Not in the back office for marketing and finance, but on the retail floor and in training programs for employees.
Stew Leonard learned the value of CLV early on when he argued with a customer about how fresh the milk was that the women wanted to return. He collected the .25 cents for the milk, but lost her as a customer for life. When he calculated the CLV (Stew called it something else 40 years ago). He realized over the next 10 years he would lose $26,000 (not including inflation).
Educating your employees to look at customers in a longer-term view can help them make better business decisions when they have someone complain or when a store department looks messy or someone wants to return an item. Loyal customers are not only constant revenue year over year, but they are also your best advertising tool.
CLV is a critical measurement for retailers. I am just not sure it is being applied in the correct department.
I don’t agree. CLV is incredibly important in training. When I was COO of a coffee franchise we made the case that it isn’t just a $4 drink. An average person visits 5x a week on their way to work. $80 a month on a yearly basis was about $1000. That drove home the fact a new customer was worth much more than $4. For retailers it can be much harder to quantify but just as important to try to determine.
I wonder if Tony Hsieh and the Zappos organization spend time and money calculating the CLV of each of their customers. Probably not, because they’re too busy building lifetime customer relationships by focusing on delivering the products people want wrapped in the best customer experience they can deliver every time.
As the late customer service guru Peter Glen posited in one of his books, retailers should endeavor to anticipate what their customers want and how they want it and deliver it just that way. Focusing company assets on delivering great product and service seems like a far better use of time and money than trying to anticipate how long a customer might stick around and how much they’ll be worth–maybe. Any customer is a terrible thing to waste.
Research has shown that customers who browse and purchase via multiple channels as a group represent a business’ most valuable. By virtue of their engagement across channels, these customers demonstrate a level of interest simply much greater than a customer who engages via fewer channels. Identifying and building this segment of cross channel customers is becoming an important step toward capturing high-value customers. Calculating LTV the traditional way helps allocate dollars to acquiring and retaining desirable customers, but not to the exclusion of targeting those customers who desire a cross channel relationship.
My problem with Customer Lifetime Value calculations is that they are based on the assumption that shopper behavior is static, e.g., 5 year value. Given the current pace of change in shopping patterns (emerging online, mobile etc.), most CLV metrics are likely to only represent a nonexistent ‘average’ shopper.
Terrific thread here. If you don’t get hung up on doing CLV “perfectly,” and make an effort to understand the basic tenets, it can certainly help. Otherwise, it’s fraught with peril for “analysis paralysis” that is just a lot of wasted motion with no impact.
Bob Phibbs has it going in the right direction. Yet, while the debate rages on in the conference room over the factors in the equation, the customer in their store right now may not be receiving the value they expect.
It’s simple, coach towards treating every single customer that walks through the door or enters a website as the most valuable customer you have. Period. In doing so, you might just make them that.
Rating, ranking, and evaluating customers takes the focus off creating and maintaining the best customers possible.
Growing up in a different retail world that was minus the barrage of consultants and mounds of data, I was taught differently. Today, it seems as if in retailing many are so focused on finding the silver bullet to the perfect customer that in the meantime they get shot in the heart.
CLV works best in the supermarket and drugstore verticals, where a customer may legitimately shop the same chain most of or their entire life. Most other verticals, however, do not offer the same type of lifetime loyalty opportunity. For example, apparel shoppers may start at youth-oriented chains like Abercrombie, switch to department stores or luxury stores depending on career path/income level, and wind up buying apparel at discount and dollar stores after retirement. That same consumer will probably buy groceries and personal care items in the same place the entire time.
A couple of things seem axiomatic. First, there is substantial value to retaining customers over a long period of time. Second, getting a “lifetime” customer comes from consistently offering a superior experience for the customer. The utility of having a complex formula to derive a specific dollar value is useful, but I’m with Marge on this–it is far more important to spend your energy on building a superior experience than arguing over how to calculate its value.
Shopper lifetime value calculations are necessarily built upon a set of assumptions about the continuity of purchase behavior over time. I don’t believe I have observed an LTV framework that adequately considers the shopper life cycle. At best, some sort of new customer acquisition and attrition factors are built in to the model–more assumptions.
To complicate matters, it is quite impossible to predict future market influences such as: the arrival of competing stores; the introduction of new products that create new or siphon old demand; population mobility; social behavior changes; and economic cycles.
Despite these challenges, LTV is useful as a relative measure. I have long advocated that companies seek to evaluate their customer relationship portfolios as one indicator of corporate equity. When best available measurements are incorporated, this transcends what accountants call “good will.”
Moving down to the tactical level, retailers with frequent shopper programs do possess a mechanism for ranking shoppers by relative current value. Known as recency-frequency-monetary analysis, this is an essential basis for target marketing.
While RFM analysis is necessarily backward-looking, it is reasonable to assume that recent purchase behavior carries greater weight in the value calculation. Longitudinal purchase data also contains clues about the shopper’s position in the life cycle, which may allow us to separate higher–and lower-potential groups for specific shopper marketing actions.
Perhaps to simplify the equation, the time frame measured could be set as a common denominator, say a 3 year horizon. That’s short, I know, but it’s also an equalizer. A three year horizon becomes more actionable as well. Just a thought.
The idea behind CLV is clearly important (yes, of course, a new coffee customer is worth more than the $2 margin on the first drink purchased!). But I agree that the numeric implementation is often flawed in two ways:
1) The assumptions are not only highly debatable but are also constantly changing (and usually for the worse). Thus, you get the illusion of acquiring a $1000/yr coffee drinker, and forget the fact that you might lose that customer to a new coffee joint tomorrow. Or, you might have to cut price–again, the value changes. Or, you are a bank, where CLV is most firmly embedded, and RegE comes along.
2) Everyone wants to claim credit for the full CLV value. A marketing program just acquired a customer–that should be worth the CLV. And, marketing will argue, they should be given leeway on acquisition costs based on that CLV. But then, if ops want to improve service to stem attrition, then they will again claim credit for saving the CLV of those customers. Which leads to terrible in-fighting and ultimately poor enterprise economics if not managed well.
The retailer’s ability to recognize customers apart from all others wandering the store and or internet site is the most important of applying CLV awareness and appreciation. Blanket visitor approach and response is a waste of time and an immeasurable means to increase shrink. Customer Lifetime Value is very easy to measure and understand for even the retail beginner. This is, however, useless if you don’t know exactly how to recognize who the customers are and how to find the new ones.
Like many here, I see the importance of CLV lying more in getting people to recognize the concept than getting bogged down in the details; there is simply too much uncertainty in the variables for a precise measure to have any meaning…what do you do with a number like $1850 +/-$1650?
For the sake of argument, let’s assume that you can agree on how to calculate CLV (or some other Customer Value measurement). The far more important question is: “Now what?”
Now that I’ve determined that this group of customers has CLVs of at least $X, what am I going to do about it? Am I going to give them special privileges? Am I going to monitor their shopping habits and call anyone who stops buying? Or am I just going to add this to the mountain of more-or-less used metrics and reports?
CLV (or any other measure of customer value) is useful if it informs action. As an example, we helped a brand send a $1 “Thank You!” coupon to their top customers (as measured using annual, not lifetime, value). Since the campaign, the shoppers who redeemed that offer have been outspending the control group by 2:1.
So I say, do a “good enough” job of measuring CLV; invest your real effort in changing your customers’ CLV for the better.
Why does one want to calculate LTV if the organization has 3, 4 or, at most, 5 ways to answer to the client? As long as we don’t have customized relationships — and at retail, we are far off — LTV is just a tool for segmenting customers and classifying them. Maybe we can do this more accurately in other ways rather than trying to the predict length of the relationship and changes in future behavior.
There may be no perfect metric for measuring the lifetime value of a customer. Developing an understanding of what a customer is worth to a business is an essential part of having the right merchandise, prices, people, shopping experience and store locations. For the last twenty three years our company has helped clients develop their own understanding while leveraging every aspect of their business to build long-term customer relationships. For every client the metric has been different and specific to their business, retail segment and customers.
I applaud any retailer who places a high priority on every customer and their value. For far too long we have been focused on generating sales by month and quarter without regard to how this focus impacts long-term customer relationships.
CLV is a great business scam/generator for consultants and serial measurers. But sometimes, you just don’t wanna’ know. It’s TMMI, Too Much Meaningless Information. Just ask political pollsters. Vague and constantly-changing so-called individual shopper/voter “data” create more questions than answers and seldom leads to service advantages for retailers. The data simply change too fast.
As I’ve written in these spaces before, shopper value equations change quickly, day to day and even minute to minute. The $40 supermarket nosh tray you’d normally bypass suddenly becomes part of your value equation on your way home from work on a Friday when you’re hosting a get-together that night. Travis Bickle might have said, “You measurin’ me? You measurin’ me? Then who the [heck] else are you measurin’? You measurin’ me? Well I’m the only one here. Who the [heck] do you think you’re measurin’?”
I’m with Travis on this one.