🌵What AOV Stands For, and 3 Kinds You Need to Know

AOV is one of the most high-leverage metrics an ecommerce brand can look at when trying to increase revenue. While most Shopify stores look at metrics like ROAS, CPA, and bottom line revenue to determine efficacy, AOV is the unsung hero that can level up performance like none other.

But what exactly does AOV mean, and how can you use it to drive more online sales this year? 

Read on to get a deep dive into AOV, including what it stands for, how to increase it, and the three amigos of AOV that are so critical to measure when gauging ecommerce success.

What does AOV stand for?

AOV stands for Average Order Value. Average Order Volume is the average amount of dough a customer spends when placing an order online. You may think that a high AOV is “good,” but that’s not necessarily the case. AOV is influenced by a number offactors, including manufacturing costs, shipping costs, product bundling and much more

All DTC businesses love a big cart size. Here are the top five AOVs in ecommerce, based on our platform data.

1. Cars and truck parts — $482 per customer
2. Home and garden — $385 per customer
3. Electronics — $296 per customer
4. Office products — $291 per customer
5. Digital products — $271 per customer

As you might imagine, many of these industries also lead the way when it comes to driving revenue. That's because generally speaking, if you can increase AOV, you can increase revenue.

To better understand this phenomenon, let's talk about the three amigos of AOV: mean, median, and mode.

‍🌵 The three amigos of AOV

‍

First off, great flick. Steve Martin is the best.

Alright, let’s jump in. What are mean, median & mode and why do they matter to me as a marketer? Two incredibly prescient questions that I aim to answer by the end.

But first, let’s start at the beginning.

Mean, Median and Mode are statistics used to represent/predict centralized tendencies of a data set.

That is a fancy way of saying, they show you where the middle of the data is. Now why is this important to you as a Whale Mail subscriber?

Because the ability to synthesize data from different perspectives into a coherent thesis is the show.

That is how the big girls and boys operate s/o Taylor Holiday. And after consuming this Whale Mail, that is how you will operate. Time to level up your stats game. #LetsGo.

😡 Mean AOV

The mean is what is most people call the average. If we are being pedantic, it is technically known as the arithmetic mean.

There are tons of other types of means, but remember I promised you this would be fun; so we won’t get into all that 🤣.

Ok where was I? Thats right, the mean.

Fun fact about the mean: it is the only one out of the three amigos to need a formula for calculation. Talk about high maintenance.

The average (I’ll refer to the mean as the average from here on to decrease cognitive load) is calculated by the following formula:

Average = Sum/Count of Data Set

For the following data set: 10, 20, 20, 50, 100

Average calculated would be:

Average = (10+20+20+50+100)/5 = 40

How does this transfer over to Ecom?

Average Order Value (AOV) or Mean AOV.

Mean AOV or AOV is important to us because it provides the first piece of the puzzle about consumers’ buying tendencies.

The formula for AOV is:

AOV = Total Purchase Value/Total Purchases

There is a lot of weight put behind AOV, rightfully so. However, you want differing perspectives before constructing a thesis. Like an OG detective, hawking down every lead. Mean AOV is just the first witness you want to talk to in the customer tendencies investigation.

**Side note: Mean AOV and AOV are the exact same thing.

🍜 Median AOV

Now let’s look at the median. The median is not calculated by a formula, rather you rank order the data set. From that you derive the middle value. If the data set count (number of values) is an even number, you average the two “middle values” to calculate the median. Sounds like a lot, but it’s actually super simple.

Let’s look at a data set.

For the following data set: 1000, 1999, 420, 69, 22

First rank order data set:

Ordered Data Set: 22, 69, 420, 1000, 1999

Now choose the one in the middle: 22, 69, 420, 1000, 1999

Median = 420

Now if we had another number in the data set, you would just average the middle two numbers.

For the following data set: 1000, 1999, 420, 69, 22, 808

Ordered Data Set: 22, 69, 88, 420, 1000, 1999

Now choose the one in the middle: 22, 69, 88, 420, 1000, 1999

Now average middle two numbers: (88+420)/2=254

Median = 254

It is not uncommon for the mean and median to be the same for a data set. The median is the literal middle of the data set.

The median is the value in the middle of a data set, meaning that 50% of data points have a value smaller or equal to the median and 50% of data points have a value higher or equal to the median.

Median AOV is calculated the exact same way as above. You would take the purchase values for Date X to Date Y as your data set. Then rank order them; the one in the middle is the Median AOV. If the data set count is an even number; average the middle two numbers.

The Median AOV helps you determine what the mid-point is in terms of purchase value.

Jumping back to our detective metaphor; we have talked to two out of the three witnesses (Mean AOV & Median AOV); let’s find out what the last witness, Mode AOV has to say about the data.

**Side Note: We realize Median Average Order Value is a cacophony of words, and doesn’t really make sense in mathematical parlance. And TBH neither does Mean AOV or Mode AOV but we are marketers, not mathematicians :)

📳 Mode AOV

You made it to the final boss, Mode AOV. Congrats 👏. The mode is the most common value in the data set. Let’s walk through an example.

For the following data set: 3, 19, 420, 69, 420 22, 8, 420, 22

Mode = 3, 19, 420, 69, 420 22, 8, 420, 22

Mode = 420

420 is the most common value in this data set, and therefore recognized as the mode. The mode is a function of frequency, in the sense that this is the most “selected option” or in our case the most common purchase value.

The reason Mode AOV is important is because probabilistically speaking, you are going to receive this amount per purchase versus the Mean AOV or Median AOV.

Fantastic! Now that you have interviewed all three witnesses; you can start to construct a thesis. Let’s go through two examples together to drill the main points home of Mean AOV, Median AOV & Mode AOV.

👨🔬 Let's Build An AOV Thesis

Alright, on to the fun part (I told you it would be fun). What we are going to do now is run an AOV Analysis using Triple Whale for two different clients, Client X and Client Y.

Using what we just learned about Mean AOV, Median AOV and Mode AOV, we are going to build out a thesis around the central tendencies of each clients’ consumers. Let’s get started!

🩳 Client X's AOV Analysis

From the following visualization we can tell our client with confidence that:

On average, customers spent $144.12 per purchase in the last six months. (Mean AOV)

In the last 6 months, 50% of customers spent equal or less than & 50% of customers spent equal or more than $115.85 per purchase. (Median AOV)

The most frequent purchase amount the last 6 months was $117.90. (Mode AOV)

Three Amigos for Client X

From this you also want to calculate the Mode to Mean Ratio.

Mode to Mean Ratio is a % representation of the the most frequent purchase value (Mode AOV) over the average purchase value (Mean AOV).

MMR = Mode/Mean*100

MMR = $117.90/$144.12 * 100

MMR = 81.8%

82% is strong, but without the context of the business it really doesn’t mean much. Said a different way, there isn’t a “right” or “wrong” MMR, but with MMR you add another witness to the customer tendencies investigation.

How to use MMR? Well loosely speaking, when it is high (67-100), Mean AOV is a better representation of consumer tendencies. When it is low (0-66), Mode AOV is a better representation, especially in terms of paid media. With that being said, alway use a mix of the three AOVs.

Splendid. You are killing it. Now let’s look at Client Y.

🪀 Client Y's AOV Analysis

From the following visualization we can tell our client with confidence that:

On average, a customer spent $47.33 per purchase in the last six months. (Mean AOV)

In the last 6 months, 50% of customers spent equal or less than & 50% of customers spent equal or more than $40.32 per purchase. (Median AOV)

The most frequent purchase value during the last 6 months was $19.90 (Mode AOV)

Three Amigos for Client Y

From this you also want to calculate the Mode to Mean Ratio %.

MMR = Mode/Mean*100

MMR = $19.90/$47.33 * 100

MMR = 42%

What the data is telling us is that the most common purchase value is only 42% of the average purchase value. Again, not “good” or “bad” just another data point to synthesize.

From our analysis of the central tendencies of Client Y’s data, we can surmise that Mode AOV is a better representation than Mean AOV or Median AOV. This doesn’t mean disregard the other two, but know if you are running paid media for a client like this, expect $20 orders not $40 orders.

Pro-Tip: Use Triple Whale’s 60/90 Day LTV & Product Journeys to identify what products are driving the best economics; then run paid behind those and let organic support the rest of the product portfolio. Then print money.

It's all about AOV

You made it all the way through a stats lesson. Noice!

In today’s essay we covered mean, median and mode; which are statistics used to measure the central tendencies of a data set.

Mean is the average (Sum/Count).

Median is the midpoint of a data set.

Mode is the most common value in a data set.

After that, we explained how these statistics are calculated and useful in understanding the distributions of purchase value.

Lastly, we learned how to synthesize the Three Amigos of AOV into an educated thesis about purchasing tendencies. All that from a free newsletter…Pretty f'ing cool!

Alright that’s all we got. Thanks again for being a Whale Mail Subscriber. We will see you all on the flip!

P.S. If you are a Triple Whaler you can access Mean, Median & Mode AOV in the mobile and desktop app at Analytics->AOV. Plus, you can change the Purchase Value Bucket Size. Super slick.