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How Different AOV Meanings Teach You New Things About Your Customers

how-different-aov-meanings-teach-you-new-things-about-your-customers

Average Order Value (AOV) is a metric mentioned a lot when it comes to ecommerce metrics.

It’s easy enough to calculate your AOV, you’re just adding a bunch of order totals together and then dividing to find the average.

The fun part of comes from giving the AOV meaning. What does that result actually represent?

Overall AOV will show you your typical order sizes. It’s fine for ball-parking decisions (e.g. “can I spend $10 or $100 per customer acquisition?”) but there a lot of limitations to it.

The New Customer AOV means how much new customers are spending. This is a much better metric for customer acquisition (as it’s only counting new customers) but tells you nothing about repeat or loyal customers.

So there’s Repeat Customer AOV which covers the repeat buyers. This version will tell you a bit about your customer loyalty and can guide how you build loyalty programs.

As you can start to see, you can calculate the AOV for a lot of segments. All that matters is figuring out which customers belong in each segment and then applying a purpose to the measurement. Without that meaning, you’re just measuring things for fun.

For example, it’s probably worthless to calculate the AOV of customers who ordered at 11pm with rush shipping to Toledo. That is, unless you’re based on Toledo and are measuring that on the last day of shipping for Christmas.

Thus, meaning is subjective.

Looking at what the AOV means in the broad sense is important. Thinking about that will help determine which AOV measurements are great for you vs worthless.

From what I’ve seen, customer segment and acquisition-channel based AOVs are great for most stores. Those are common segments that frequently have a lot of ways to take advantage of. That’s why they’ve included in Repeat Customer Insights.

Time-based AOVs are also useful but only when you compare your store to itself. e.g. 2022’s AOV vs 2021’s vs 2020’s.

Product-based AOVs can also be useful but they get tricky to calculate depending on how you segment the data. The one I like and have in my app measures which customers bought a specific product and those customer’s AOV. That’s a level of behavior analysis that can get really deep understanding of how product purchases influence the customer.

Try these out, segment your customers and orders, and see which other types of AOV work well for your store. Calculating the AOV isn’t that difficult but could be tedious at times.

Eric Davis

This originally appeared on the LittleStream Software Blog and is made available here to cast a wider net of discovery.
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