Average Order Value (AOV) or Customer Lifetime Value (CLTV) – which one is more important? How do we calculate them? What are the best ways to use AOV and CLTV insights?
As the largest independent marketing platform company in the world, Emarsys has been working on solving these challenges with more than 2,000 brands and tens of thousands of marketers by analyzing more than 2.5 billion consumer records and purchase histories.
Many businesses are looking for a winning formula to determine their optimal Customer Lifetime Value and using Average Order Value analysis to understand what’s working well and how to set a benchmark.
But this can be a confusing topic. If you’re unfamiliar with this kind of data analysis or if you’re looking to take your current analysis to the next level and actually make meaningful business and marketing decisions based on customer data, this month’s benchmark report is for you. Here we’ll share some hard-won insights based on our experiences with our clients and the common challenges and pitfalls to watch out for.
Which Value Is the Most Valuable: Customer Lifetime or Average Order?
One of the most common questions we get from new clients is: What’s more important when you’re defining what good looks like: Average Order Value or Customer Lifetime Value?
This murky topic is exactly why we provide monthly benchmarking data, because the true answer is often buried down in the data.
To Acquire or Not to Acquire, That Is the Question That CLTV Answers
Customer Lifetime Value is a key metric within your business because it enables you to understand the relationship between your Customer Acquisition Costs (CAC) and your ability to maximize the return on that initial investment over time. Essentially, CLTV feeds CAC, especially when it comes down to the specific amount to spend on customer acquisition.
Many e-commerce businesses try to make acquisition and budget decisions in the most simplistic way: If the customer’s first transaction costs the company more in CAC than they will make in AOV, the company thinks that acquiring this customer is not profitable and therefore not worth the effort. But that’s without taking into account that your marketing plan will monetize that customer over time and drive repeat purchases at an ever-lower CAC.
Reviewing a customer’s CLTV (quarterly or semi-annually) enables you to devote more budget to your Google AdWords, Display, and Facebook/Instagram budgets. You’ll also pay a higher percentage of sales to your affiliate and retargeting partners on that first purchase, but you’ll make this up as customers stay with you for the long run.
Within our own client base as shown in Chart 1, we see that, on average, only 37.8% of monthly revenue comes from leads making their first purchase (new acquisitions) — that means 62.2% comes from existing customers.
AOV: How Much They Spend
Critical for calculating revenue, Average Order Value informs your keyword bidding and display budgets, but the vast majority of businesses, particularly high-growth or early-stage businesses, choose CLTV over AOV as their key metric for acquisition because it allows them to spend more and increase their reach and exposure to get new business.
As we saw in Chart 1, our clients can afford to spend more on advertising because each month there is more revenue coming from existing customers than there is coming from new customers. This means that they can pay higher costs to acquire customers and be safe knowing that they can monetize that contact over time by getting more purchases from them.
But before we completely disregard AOV, let’s take a look at AOV and CLTV combined.
Chart 2 shows the distribution of Average Order Value (also called Average Basket Value) based on buyer status (leads, first-timers, actives, defectors, and inactives). The key thing to note is the differences between the Average Order Value of a lead making his first purchase and an active buyer making her third, fourth, or even tenth purchase.
The median numbers for our clients show that the defecting customers spend the most, not leads or first-timers. However, the numbers are very close to each other, which leads to this conclusion: Average Order Value is an important metric, but, statistically, it’s a lot easier to influence someone to purchase again than it is to influence the value of their purchases within that transaction.
In fact, in a given month, an existing customer is up to 8 times more likely to purchase than a lead as shown in Chart 3.
We should also look at the difference between the Average Order Value of the lowest spenders (bottom 25% of our clients) versus the highest spenders (top 25%). For leads making their first purchase, on average, there are €109 between the €45 (1.6%) at the bottom and €154 (11%) at the top – that’s more than 300% difference between their basket values.
Why Using a Single Metric Is Dangerous
What we see rising to the top in our benchmarking and performance analysis is that when it comes to AOV, we aren’t using a single metric — at the very least, we always look at three metrics:
- Bottom (average metric for the bottom 25%)
- Median (average metric for the median results)
- High (average metric for the top 25%)
A super simplistic metric would be to add up the averages of these three groups and divide by 3 and that’s the AOV for a particular lifecycle status. For example, let’s say we want to determine the Average Order Value for leads making their first purchase within e-commerce businesses in Germany, Austria, and Switzerland (DACH) as shown in Chart 4. The low-spending customers average €53, the median averages 96.7, and the high-spending customers have an average of €164. We do the math and come up with something like €105.
That €105 stands for all the leads in that category, as if they all want the same thing, respond to the same incentives, and purchase through the exact same channels as all the others in that group. But we know that is not true.
If I base my marketing plan for CAC, discounts, incentives, or future purchases on just one number, one average representing thousands and thousands of customers, I can make a lot of mistakes by relying on inaccurate assumptions, and I’ll miss the mark for a lot of my customers.
That’s the danger of a single metric. It’s too broad to be of any real value to the marketer. What we need here are more data points. Specifically, we need groups for comparison because you get a clearer picture of a particular objective when you provide multiple points of context and relationship.
Why Emarsys Recommends Using 5 Buyer Status Groups
As a best practice, we recommend using at least three buyer status groups but strive for a maximum of five. For example, Low, Normal, Silver, Gold, and Platinum buyers will give you a better understanding of your whole customer population than just one AOV. It also tiers your customers into logical groups so that you can treat them based on their value to your brand.
By default, at Emarsys, when we help our clients understand their customers, we use these five groups, which are all based on Customer Lifetime Value (CLTV).
Chart 5 shows a benchmark of average product value for Emarsys e-commerce clients in the Clothing & Accessories industry. For the average value, we use Bottom 25%, Median, and Top 25% to understand spending habits from a benchmark perspective, but we also look across our five buyer status groups in terms of Customer Lifetime Value.
Low spenders aside, the value data is very similar for the other four groups. When there doesn’t seem to be a difference in the average product value across these buyer groups, what is the value in having them?
Chart 6 contains data based on average anonymized data sets, and when we look at the number of customers across these groups, Low spenders and Normal spenders are the most numerous and, therefore, perhaps the most important, especially given the data in Chart 5 about the very small differences in average product value across these groups.
Using the data set from Chart 6, Chart 7 shows this same group of customers in terms of their Customer Lifetime Value, which presents a very different story.
Low-value customers have spent less than our Gold buyers and significantly less than our Silver buyers, and while we only have a small number of Platinum buyers, their CLTV per customer is huge compared to the others. And this is not unusual for the clients Emarsys works with.
The challenge here is that there is no such thing as a “typical” customer, and often it’s very dangerous to base business or marketing decisions on a single metric such as Average Order Value or Customer Lifetime Value because your customers aren’t all the same and a small number of metrics doesn’t give you a detailed view of them.
Multiple Metrics Give You the Best Customer View
Some customers buy big but only once in a while. Some customers spend small amounts, but they buy often – they might have the same Customer Lifetime Value, but the path that they took to purchase is very different.
Similarly, as we’ve seen from the data, Average Order Value is an important metric to understand, but it’s as nuanced as your customers are.
What we have learned from working in different geographies, verticals, and business models is that there are some highly predictable patterns and some very important insights to be had from your customer data.
But if you don’t have a tried-and-true test model or experience in working with data, the “outliers” (those customers with really big Customer Lifetime Values or really big single transaction values) can easily skew your data up or down.
When you want to understand your customer data, more is better than less. Create a series of groups or personas that include at the very least, low, average, and high and give yourself a platform to discover that point where a customer becomes “valuable” so you can make them feel “valued.”
Not all customers are created equally. The trick is to understand that, put the insights into practice, and appreciate that a single metric might not do justice to the hundreds or thousands or millions of engagements you have with your customers.
If you’d like to know more about some of the models we use at Emarsys to better understand customer data, please check out these related resources: