There are about 1,030,000,000 webpages about increasing your store's Average Order Value.
(Thanks Google)
Pretty popular topic right?
It's a good activity if you have nothing else to do, but in reality there are 1,000s of other things you could be doing instead of increasing your Average Order Value.
So how much Average Order Value is enough? When you should shift your focus?
An easy way to know is to compare your AOV to the industry or similar stores.
Benchmarking like this is done all the time (Repeat Customer Insights does this too). It can help you notice any major problems like if you're 50% of the rest of the industry.
But unless your business is exactly the same as others, you're going to have to settle for getting close to the averages.
Then again... do you merely want to be just average or would you rather be extraordinary?
Another approach is to become best buds with your finance department and accountant and have them run some models to see what changes in your AOV mean to the rest of your financial picture. It's uncommon, but sometimes an increase in your AOV ends up increasing your costs so much that you actually lose net profit.
There's two rules of thumb you can use:
- Set growth targets over a year, e.g. 5% AOV growth. This can counter inflation and act as one growth lever.
- Make sure your AOV grows from first order to second order to third order, etc. This is a measure of your repeat customers spending more and more over time.
Go ahead and read a few of those 1,030,000,000 webpages about growing your AOV, but don't constantly stress about it. Aim for enough growth and focus on the other important parts of your business.
Eric Davis
Segment your customers to find the diamonds in the rough
Not all customers are equal but it is difficult to dig through all of your data to find the best customers.
Repeat Customer Insights will automatically analyze your Shopify customers to find the best ones. With over 150 segments applied automatically, it gives your store the analytics power of the big stores but without requiring a data scientist on staff.