The Magic Formulas: Customer Lifetime Value and Customer Acquisition CostSep 25, 2015 - by Mike Dragan
Marketing is as much a science as it is an art form. Today’s leading companies are focused on increasing profitability – not just growth – by shifting focus from acquisition to customer retention. Critical to success is the ability to measure the value that retention delivers through both overall revenue increase and lift in Customer Lifetime Value (CLV). Long-term measurement ensures that you are setting a strategy that is optimized to offer your customers the best possible experience, cementing loyalty and earning more from individual relationships.
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This is the new science of marketing — check out this article for more on the formula you need. –Sailthru
A customer by any other name would spend the same. At least that’s what Shakespeare would say if he were to try to sell his plays online. But that’s not always the case and some customers are better than others. Some customers are also harder to engage and sell to.
How do we differentiate and how can we make a good medium and long-term decision about customer acquisition costs and returns? The answer lies in using two indexes well known to marketers: Customer Lifetime Value (CLV) and Customer Acquisition Cost (CAC).
You can’t put a price tag on a customer. But you can assign a value.
(Courtesy of gorillapodstrobing.blogspot.com)
Let’s take Customer Lifetime Value (CLV). The corporate world is often focused on quarterly and in best practices yearly results. That means that returns on investment have to happen quickly or they are useless. When short-term tactics prevail, customers are often mistreated. The innovation halts, marketing and customer relationship investments drop. Customer service is lousy. On short term executives may see a rise in profits but long term results are often slashed.
A lack of focus on CLV is often trouble for ecommerce companies and companies at large. Those employing the above mentioned short term tactics miss opportunities. The client base is often unimpressed, growing slowly or even decreasing. Let’s see why:
Say we get a new potential customer by encouraging him to visit our shop. John Doe likes what he sees, registers for a newsletter, but he’s not yet convinced to buy. Later on, next month, a product on a weekly personalized offer catches his eye. He clicks, goes online, buys the product – he is now a customer. But wait. John will hopefully continue buying from us, won’t he?
He’ll keep coming back, buying something every month, say for a period of two years or so, until one day – something happens. He stops receiving his weekly personalized offer. Somewhere along the chain of command somebody decided personalized offers are too expensive. The overall operational costs dropped but so did Mr. Doe’s orders. His Lifetime as a Customer is over.
In this scenario we can identify the following:
- Customer Acquisition Cost (CAC): how much did we pay to get John Doe to a) visit our website, b) register to our newsletter, c) view the personalized offers until he decided to buy?
- Customer Lifetime Value (CLV): the cashflow coming from John Doe’s purchases. In this case, if he were to spend an average of $100, monthly x 24 month = $2400. But these are not all ours. Just the margin. Oh, and there will also be discount costs and other variables, but we’ll get to that, soon enough.
- Shortsightedness: a long word to describe the fact that someone should have thought twice before deciding to cut out the personalized offers.
Customer relationship is an asset.
Any customer demands to be treated as a human being. That’s easy to say but when companies such as these handle millions of customers, that takes hard work to get done.
The Kindle Fire – both a loss and a huge win for Amazon
First, it takes a change in perspective. You have to understand and quantify probably the greatest asset any retailer has: the customer relationship. Ecommerce has made it easier for dissatisfied customers to jump boats. The leaders know this and they use it to their advantage.
For instance – Amazon is not making any profit when it sells a Kindle. The company supports costs so they can get more customers aboard. Those customers turn to happy customers and get to spend roughly $2400 during their lifetime as customers. So what Amazon loses in hardware sales, makes up in eBook sales and other product sales. Such a strategy is not possible without a clear understanding of Customer Lifetime Value and Customer Acquisition Cost, two of the most important indexes online retailers have to work with.
A formula for Customer Lifetime Value
Previously we had an example of Customer Lifetime Value and how we could better understand the concept and estimate the customer’s value. Those numbers being a crude model, we have to reevaluate and get a new perspective on this value. Here it is:
We have some variables (such as customer expenditure value or purchase cycle) and constants (such as retention rate or profit margin, which are less likely to change dramatically). But don’t worry, once you get the hang of it you’ll have a great and easy way to understand wether you’re spending too much or not enough on keeping your customers happy.
Let’s start with the variables. Feel free to adapt these to your own company metrics:
- Customer Value / Week (a) – how much does a customer spend weekly in your shop?
- Customer expenditures per visit (s) – how much are customers willing to pay each time they visit your shop?
- The purchase cycle (c) – how often do customers visit your shop each week?
These variables are defined here as weekly variables but you can change those to monthly values, if it fits your business model better. You will obtain the values above by estimating median values for all your existing customers.
When you have estimated your variables you will have to take into account some constants. They will help you predict your estimated customer lifetime value. These are:
- Average customer lifespan (t) – how long, based on your experience, do you expect customers to remain your customers?
- Discount rate (i) – no, this is not your customer’s discount. You are projecting a value, into the future, but you’ll have to adapt this value to present tense. Simply put – the value of a certain good in the future is lower than that of one you are holding in your hands today. You can study more about this topic starting with the Annual Effective Discount rate and than heading over to Intertemporal Choice. Puzzled? Maybe you’ll want to go with something like a (0.1) value.
- Customer retention rate (r) – how many of your customers come back to your store and purchase from you, compared to the previous, equal amount of time?
- Profit margin (p) – pretty self-explainatory
- Average Gross Margin per Customer Lifespan (m) – the gross profit per customer expected in the given average lifespan (Profit Margin x Expected customer lifetime expenditure)
So now we have the variables, we have the constants, let’s get busy with the equations, from simple to complex.
1. Simple Customer Lifetime Value Formula
We will be using 1 year as a reference timeframe and we will be estimating how much will we be making in a year on any given customer. There are two main variables involved – the average customer value / week (a) and the average customer lifespan (t), expressed in years.
Limitations: this is a pretty crude estimate so it will only serve as a base for further examination. It does not take into account the retention rate and attrition (loss of customers), the discount rate, not even the profit margin. It just tells us – how much would we be expecting our customers to spend with us, during their customer lifetime.
The formula is:
Basic Customer Lifetime Value Formula
2. Extended Customer Lifetime Value Formula
So now we know roughly how much will our customers will be spending with us. But that’s not actually our money, isn’t it? That’s the revenue, not our profit. So let’s step a little further and take into account our profit margin and double check the figures, by using the Customers expenditures per visit (s) and the Purchase cycle (c) value.
Remember – this is the not the final form – we will still have to think of a future projection of our customer lifetime value. However, the second formula would be this:
Extended customer lifetime value
3. Projected Customer Lifetime Value Formula
This formula has it all – Gross Margin per Customer Lifespan (m), discount rate (i), retention rate (r). It is also one of the oldest and simplest ways to estimate customer value (well, as simple as it can be).
Let’s have a look at it:
Projected Customer Lifetime Value
You can see there that this is directly proportional with two of the values. First – Gross Margin per Customer Lifespan (how much will you profit from your customers during their lifespan as customers). Second – retention rate. So do what you can to extend your customers lifespan and the retention rate.
So now we have three formulas. Each outputs a different value. Which is the right one?
Answer: all. And none. Remember – this is an estimate. The best you can do with these three is find an average and try to stick with it. Once you have a number you now know how much should you be spending on your customers. You want an example? Head over to this info-graphic and see these formulas in action with a fine aroma of roasted coffee. Starbucks has a Customer Lifetime Value of $14099 so as long as its spending less than that to turn you into a customer and keep you one – they’re profitable.
What do I do with these figures and formulas?
What is CLV good for? First off – telling you which customer to keep and which not. When it comes to ecommerce data is anything but scarce. You have the info – now use it. Find out who are your best customers. Analyze your data, split customers into marketable groups and … action! Drop the marketing on unprofitable customers (that doesn’t mean you should treat them worse – just spend less on acquisition). Engage your profitable customers.
But be advised – you have to have a long enough timeframe to analyze data. Sometimes those negative CLV’s might turn out to grow in time. Use predictive analytics and extend your search to see where are your customers going, not just where they are right now.
This article is by Mike Dragan from netonomy.net.