That it costs five to seven times more to acquire a customer than it does to retain one isn’t entirely true.
The origins of this myth can be traced back to the 1980s when the Technical Assistance Research Project published research that stated the cost of customer acquisition was significantly higher when compared to the cost of customer retention.
Soon after the research was published, other institutions like the Customer Service Institute, Consumer Connections Corp., and ITEM Group all “found” similar data.
Truth is, it was mostly propaganda designed to sell high-level executives new customer loyalty programs. If you don’t believe me, try to find a single linked source that supports the five to seven times claim.
I bring this up because time and time again, I see business owners reallocating budgets based on soundbite statistics and ending up with disastrous results. If you want to see your business grow, analyzing your own data is essential for establishing viable benchmarks and goals.
In this article, I’d like to help you understand the different metrics associated with customer lifetime value (LTV), and explore how we can use this information to make more informed, data-driven decisions as it relates to how we budget our marketing spend.
Even if you feel like you’ve got this nailed, keep reading, because there’s some very surprising research that I’m sure you’ll find useful.
Why determining your customer lifetime value (LTV) is so important
Before we get into the math, let’s talk about why figuring all of this out is so important.
In this must-read article by venture capitalist David Skok, he says that the biggest reason startups die is because their customer acquisition costs versus their customer lifetime value often look like this:
From my experience, that’s because so many businesses focus on transactional customer value, and forget to invest in the experience that happens after the conversion.
It should go without saying that you need to invest in making the product better. But if we’re not also focusing on ways to make our existing customers happy and even marketing to people who already bought from us, the cost of acquisition can greatly outweigh how much we can make from a single customer.
In David Skok’s post, he says:
Lifetime value (LTV) > cost of acquisition (CAC). (It appears that LTV should be about 3 x CAC for a viable SaaS or other form of recurring revenue model. Most of the public companies like Salesforce, ConstantContact, etc. have multiples that are more like 5 x CAC.)
CAC should be recovered in under 12 months (for subscription businesses).
In other words, if it costs you $400 to acquire a customer you should have a plan to make $400 off of that customer within the next year to have a healthy cash flow. (This rule is less important for companies with access to lots of capital.)
This is why software companies like Salesforce might have a marketplace, where they’ll make between a revenue percentage from apps sold through their service…
…or Freedom, who partners with other companies to offer members-only perks, which likely operate the same way as an affiliate program.
The point of improving your customer lifetime value, as David points out, is to ultimately create balance in your business model that allows you to offset the unavoidable high-cost factors that inevitably go along with running your business.
Seriously—read his post when you get the chance.
Determining your customer acquisition costs (CAC)
Ok, so now for the math. Let’s start with the basics. The simple way of finding your your customer acquisition cost is to divide the total amount of marketing and sales dollars by the amount of actual customers that come from those efforts. (Total marketing + sales expenses / number of new customers = customer acquisition cost.)
Of course, this is an overly simplistic view of the entire process, but if you’re not currently measuring anything, I’d recommend you at least start there.
If you want to get deeper than than, Brian Balfour (HubSpot’s former VP of growth) wrote about CAC in-depth and covered some myths and potential mistakes when calculating your customer acquisition cost.
Brian claims that the above CAC formula is correct on the surface, but lacks details and definitions.
He then makes calculations with some sample data…
…and adds the following caveats:
- It takes about 60 days for a lead to become a customer.
- Not all customers are new—some are returning.
- This is a freemium product; there are costs of supporting those users before they start paying.
From here, he poses some key questions you should ask yourself (and adds key examples to help you answer them):
- How long does it take between your marketing/sales touchpoints and when someone becomes a customer?
- What expenses do you include in your marketing and sales costs? Most common mistakes are not including salaries, overhead, and money spent on tools.
For example, Spotify has millions of freemium users, which means there are certain costs to support them. HubSpot has a large customer success team, purely devoted to churn prevention, and it’s important to decide whether to include this in CAC calculations.
Similar dilemma goes for a subscription business like Dollar Shave Club. For example, their starter set promotion costs $15 while the full subscription for those same products is $75. Dollar Shave Club still has to pay for packaging, shipping, support, and more, despite not having the customer on the full plan yet.
Calculating your customer retention rate
This is where it starts to get fun.
If you’ve been doing business longer than a few months, pay attention to your customer retention rates. Before you can determine the lifetime value of your customers, you should have some idea of how long they’re going to be sticking around.
In order to calculate your customer retention rate, you need to know:
- Number of customers at the end of the period – E;
- Number of new customers acquired during that period – N;
- Number of customers at the start of the period – S.
Once you have those, the formula is pretty straight-forward:
CRR = ((E-N)/S)*100
So to make things simple, let’s say you started the quarter with 200 customers (S), you lose 20 customers but gained 40 customers (N) so when the period was over you had 220(E).
Using the formula we’ve got ((220-40)/200)*100=90 or in other words, a 90% retention rate. Good for you!
Word of caution: Before we go any further, I must stress that you should not be running this calculation to find the average across your entire customer base! Broad sweeping averages like this could provide you with potentially damaging figures if you’re not careful.
The issue is that blending customers into an “average” significantly distorts reality. If you gain two customers—one with a retention rate of 100% and another with a retention rate of 0%—you can imagine how the situation would play out. The first customer would pay forever, and the second would leave right away.
However, if you first average the two retention rates together, you’ll have two customers with a 50% retention rate.
From Corey Pierson on Custora
An example of what an averaging all customers together might look like is this:
Pretty bleak, right? In eight months you will have no customers. “Invest everything you can into acquisition, because they’re all going to be gone!”
Corey goes on to show what calculating retention rates the right way over three customer groups (Group Awesome, Group OK, and Group Sad) might look like:
This is a far more realistic and, more importantly, this data makes it easier to make projections, budget allocations, and have a baseline to build strategies.
If you haven’t already segmented your customer base, I recommend reading this guide by Christopher Gillespie on Mixpanel, as it will give you some ideas around how and why to segment your customers.
Calculating your churn
On the opposite side of retention, you’ve got churn—the rate of which customers (naturally) stop buying from you.
In an ideal world, figuring out your customer churn looks as simple as this:
Now, if you’re not currently measuring churn, this is an alright place to start. At least you’ll have something to benchmark so you can reduce your churn rate later.
But like Steven H. Noble talks about in this blog post on Shopify, it is not always that simple.
Variables like customers gained during the period, how long the timeframe you’re measuring, and whether or not churns are occurring evenly over the period, all factor into what your actual churn rate is.
If you want a realistic view of your churn rate for predictive analysis purposes, you need a formula that looks more like this:
with the weights being
What this metric is useful for is keeping track of changes in customer churn behavior while giving a rough estimate of what percentage of your customers will leave in the next 30 days.
If this kind of math scares you (it sure scares me), don’t worry, Steven gave us an interactive spreadsheet you can download to plug your own numbers into. (Be sure to stop by Steven’s blog and say thank you!)
It’s important you keep this data as accurate as possible in to measure the impact various retention strategies have on your customer base.
Calculating a more accurate customer lifetime value
There are several different ways to measure customer lifetime value and the infographic from Kissmetrics will cover a few in the next section.
But for now, we’ll use this really basic LTV equation:
(Average value of a sale) X (Number of repeat transactions) X (Average retention time in months or years)
Brad Sugars on Entrepreneur.com offers a very simple example of a gym member who spends $20/month for their membership for three years.
$20 x 12 months x 3 years = $720 in total revenue (or $240/year)
Now you could use this, but if you’re a gym owner thinking a large portion of your customers would be with you for three years, you’re delusional.
Averages lie. Again, I can not stress enough: segment your customers if you want to get an accurate picture of the data.
If you really wanted to know the lifetime value of your members, you’d have to consider the customer segments that:
- Pay for personal training and group coaching;
- Buy supplements;
- Pay for additional classes;
- Buys t-shirts, gear, refreshments.
You’d also need to analyze this data to find correlations between the members who stay and those who churn quickly.
- Do people who sign up for classes have a tendency to have longer retention rates?
- Is there any relation to people enrolling in personal training and supplements?
- Do high churn customers also buy more gear?
Finding the lifetime value of these individual customer segments will give you a very clear idea about the value each type of customer will bring to your business. Once you know that, you can make data driven decisions about how much to invest in acquiring each customer type.
Beyond that, you can use what you learn to create upsells and cross-sells to increase the lifetime value of each customer segment (e.g., 10% off supplements when you sign up with a personal trainer, half off t-shirts when you sign up in January).
A hypothetical scenario: Starbucks
In this infographic by Kissmetrics, they illustrate various ways to calculate customer lifetime value.
Disclaimer: Commenters on the original post did point out a few issues (detailed after the graphic), but there is still a lot to be learned here so please take it all in:
Even though there are some fundamental flaws, I wanted to share this with you because it is a perfect example of why you have to keep working with the data until you get something accurate.
What did the commenters find wrong?
- Averaging revenues + profits (like they did with the total average) ultimately breaks the equation.
- It doesn’t consider the costs associated with delivering the product.
- It should factor in discounts and discounted profits.
- The sample size (five customers) is probably too low for a company like Starbucks.
Of all the commenters, Yosh gave what appears to be a viable solution:
Imagine how much money you’d lose if the projections were wrong.
I know it’s a lot to take in, but it’s important you realize that data is unique every business and situation. It’s never as neat as using one catch-all formula and applying it across the board.
That’s why there are several different methods of calculating customer lifetime value.
Realistically, these formulas should only be used as a starting point to understand your customer behavior and then tweaked to fit your business.
This is why you need a good CFO, even if it’s just in the interim.
The effects of increasing customer lifetime value (micro case studies)
You’ve already come a long way and I don’t want to take up too much more of your time. But I wanted to share these three case studies of companies seeing explosive results after nailing their customer lifetime value:
- IBM increases revenue by 10x by correctly identifying and marketing to high spending customers
- SurveyMonkey increases LTV by 150% by identifying most profitable geo-demographics
- HubSpot reduces churn by 57% and increases LTV by 215.72% in a year and a half
This quote on Forbes perfectly sums up everything we’ve been talking about:
Brad Coffey, head of corporate development for [HubSpot], likens the formula to a machine: Put a dollar in at the top and the LTV:CAC ratio will tell you roughly how many dollars come out at the bottom. If your money isn’t multiplying, you’re going to want to spend some time tuning that machine.
Bonus: Six quick tips to improve customer lifetime value
- Use email to preemptively answer common questions, upsell, and provide customer education.
- Cross-market and provide lead generation for companies with similar customer bases.
- View every customer interaction as an opportunity to improve customer loyalty.
- Find ways to build habits around your product.
- Make customer service easy. According to Harris Interactive, 56% of customers will switch brands if the alternative offered more ways to connect.
- Incorporate customer feedback to improve everything from user experience to product features and design.