Purchase Frequency Of Your Customers: Like all entrepreneurs on this earth, your main goal should also be to increase the turnover of your online store.
To do this, it is necessary to take into account 3 fundamental variables:
- f = purchase frequency
- AOV = Average Order Value
- c = number of customers
A common mistake is to dwell only on the third variable c, leaving out the other two.
Increasing the number of your customers is undoubtedly a good and right thing, but it presents problems if the average order value and purchase frequency remain low.
In some cases, it could even lead to working at a loss: for example, if you spend 30 euros on AdWords advertising to acquire a customer who places a single order for you for 50 euros, after removing the margins and expenses, you will be left with nothing!
So, before you obsessively focus on growing customers (who buy little and only once), allocating your resources while trying to increase f and AOV simultaneously would be smarter.
For the increase in the average order value (AOV), we have already seen how through marketing automation, it is possible to create marketing rules in real-time to push upselling ( the first thing to do is to insert related products with receipt average higher under the product sheets).
For the increase in the purchase frequency (f), there are several ways to retain and create loyalty among your users, keeping the dialogue alive even after their first purchase. (I talk about it in depth here ).
But even before finding a way to increase these metrics, it is necessary to understand how to measure them and keep them monitored before, during, and after a marketing campaign.
The number of customers (c) is easy to calculate. Go to analytics, select a time frame of 1 year, then create the advanced segment “users who have made at least one purchase” (transactions per user > 0):
Calculating Average Order Value (AOV) is even easier.
Always with the same time frame, go to the Conversions > E-commerce > Overview report, and you will have the metric ready under the heading “transactions”:
The f- value (purchase frequency) remains to be calculated.
It’s the last term in the conversion lift equation and perhaps the most important.
Knowing the average time between one purchase and another allows you, among other things, to set up repurchase campaigns with the right timing.
We often hear (I’m the first to say it!) that an email should be sent 30 days after the last order, with an incentive to encourage the next purchase.
But why exactly 30 days? On what basis??
If you sell monthly contact lenses, the choice may be right. But if you sell decorative lamps? Or trolley and suitcases?
In these cases, you first need to understand how often your customers tend to return to the site to buy again.
To calculate the f- value, we need a few simple data:
PF (PURCHASE FREQUENCY) = NUMBER OF ORDERS (YEAR) / NUMBER OF UNIQUE CUSTOMERS (YEAR)
You can find this data on Google Analytics for a quick calculation, but for a more accurate analysis, I suggest you consult your eCommerce database directly.
In any case, we calculated the number of unique customers (c) previously, creating the segment “users with transactions > 0 (figure 1).
In the example above, the result is 6.313.
The number of annual orders can be easily found in the Conversions > Ecommerce > Overview report.
Still, in my example, the number of orders I found is 7,126 .
So PF = 7.126 / 6.313 = 1.12
At this point, you know that a user buys 1.12 times in an average year.
To calculate the time between one purchase and another, do 365 / 1.12 = 325.6
On average, users purchase every 325 days.
Well, this is the so-called “textbook” explanation, but personally, I’m not very convinced by this equation.
The reason is that all users who have made a purchase are counted indiscriminately, including the vast majority who, as is known, only make one purchase, and that’s it.
To get a more accurate estimate, you should exclude users who have purchased only once and take into consideration only those who have already made at least one repurchase:
From 6313 total customers, the number of those who made at least two purchases dropped drastically to 503.
At this point, keep the segment you just created and go back to the Conversions > E-commerce > Overview report to see how many total orders have been placed by users who have purchased more than once:
So 1190 total transactions / 503 users = 2.4.
365 / 2.4 = 152 days.
Users who have made more than one purchase, on average, purchase once every 152 days.
Sounds better.
For example, we should continue to narrow the segment by counting only users who have made at least three purchases. In that case, the frequency will also improve, demonstrating that loyal users tend to buy more often.
Now that you know how to calculate repurchase frequency, you can use this metric to monitor your marketing campaigns and assess whether your customer retention strategy is right.
Also Read : 7 Advanced Google Analytics Segments To Understand Traffic
