You got the AOV wrong ( and how to fix it)

In this thread, Jayden shares how most store owners are calculating their AOV in the wrong manner (and how to do it right).

Why your AOV doesn’t necessarily matter…

Thread below 🧵

— Jayden (@jayden_dummett) January 9, 2022

Often times e-commerce businesses have a goal of raising their average order value.

While I don’t disagree with the fact that raising the average order value should always be a goal, I think the way it’s being done and thought about is wrong.

In order to raise your AOV you have to understand what AOV actually means.

It’s the total revenue from the period of time (minus discounts) divided by the total number of orders. It’s an average.

Now, averages are useful when looking at large data sets to tell a story about multiple different metrics but when you are trying to influence that average it’s pretty useless.

This is especially true for AOV.

Let’s setup a scenario:

We are running an apparel company. For the sake of simplicity they have 2 skus and prices are the following.

Hoodie – $60
T-Shirts – $20

10 orders occur with the following values.

$20, $20, $20, $20, $40, $60, $60, $60, $80, $120

Do some quick math and you’ll get these answers.

AOV: $50
Median: $50
Mode: $20

Now what’s interesting about that AOV? It never actually occurs as an order value.

Precisely why it can be misleading as a number that we look at.

Often times businesses will look AOV as a sign to set their free shipping threshold or cost caps on Facebook campaigns.

But it’s fundamentally flawed thinking because you are optimizing campaigns and thresholds against orders that don’t exist or happen.

So what’s the answer?

Continuing with the example above we want to figure out the ranges of order values that occurs the most.

Once we find that range we want to figure out what products those orders consist of.

These are the orders that you want influence. This is how you shift the game.

You continuously shift this range of most frequent orders higher and higher. That’s how you do it.

Now let’s talk about how you can do this for your brand outside of this hypothetical.

Take your time frame of that you would like to look at. 6-12 months for example.

Export the purchase data for that time frame to an excel sheet.

Sort by order value so all of in ascending order.

Cut out really high/low purchase value orders that will distort the data.

Now find out what ranges of order values consist of what products.

These are your AOVs that you should be looking at.

This is what should influence your free shipping threshold and your bid/cost caps on paid.

If you need a better visual representation and steps on how to find your own mean, median, and mode order values:

https://commonthreadco.com/blogs/coachs-corner/ecommerce-analytics

If you enjoyed this post, Do thank Jayden.