We create direct-to-consumer stores with Shopify to own the customer relationship. Without that, we might as well be selling commodity products on Amazon, competing with every third-party Amazon seller. By owning the customer relationship, we capture more value from sales, driving ever-increasing profits and valuations.
Returning customers behave differently from new customers. They have lower acquisition costs, increased lifetime values, and higher conversion rates. Understanding that difference reveals the strength of our relationships and how to improve them.
The most common way I see shop owners calculating new and returning customers is to assume that the number of returning customers must be the total number of customers minus new customers.
returning customers = total customers - new customers
That’s a mistake because a customer can be both a new and a returning customer in the same time frame. When we ask the question “How many new customers did we have this month?” we want to know how many people placed their first order this month.
Similarly, if we ask “How many returning customers did we have this month?”, we want to know how many people place their second, third, nth order.
What if someone placed their first and second order this month? They’re both a new and a returning customer.
Furthermore, defining returning customers from the top-down as above only lets us compare the number of new customers and returning customers. To strengthen our customer relationships, we need to do more.
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New and returning customers should be defined as an attribute of the order, not the customer. When a customer places their first order, they are a new customer for that order. When a customer places their second or third order, they are a returning customer for those orders.
Defining them this way not only answers the questions above correctly but also lets us investigate the differences between new and returning customers so we can discover how to convert more of them from new to returning.
Shopify uses email addresses to define a single customer. When a customer places an order in a Shopify store, they enter an email address (or phone number).
If the entered email address matches a previous order, a newsletter subscriber, or a user account, Shopify attaches the order to the existing customer with that email address. If not, it creates a new customer.
When a customer places a second order with the same email address, Shopify attaches the second order to the same customer.
Finding a single customer’s first order in the Shopify UI is easy. The earliest order in the order history is the first order.
Go to the customer and select View all Orders and you’ll see this:
Unfortunately, there’s no quick way to find the first order for every customer in Shopify directly. Fortunately, we’ve built this Google Sheet to do exactly that.
To use it:
- Copy the Google Sheet template to your Google Drive.
- Download your store’s complete order history.
- Import it into the Google Sheet, replacing the order_history sheet with your own data.
- The results sheet will automatically categorize every order as either coming from a new or returning customer.
If the Google Sheet becomes too cumbersome, you can use a Google Data Studio connector to find new and returning customers automatically.
With our new and returning customers properly defined, we can examine the differences between them.
First, comparing new and returning customer accounts over time with a chart like the one below is worse than useless.
This chart actually hides what’s happening. In August, did the ratio of new to returning customers go up because we had more new customers or because we had fewer returning customers?
Furthermore, there’s no golden ratio of new and returning customers. If the number of new customers goes up, that’s a good thing. If the number of returning customers go up, that’s also a good thing. It just doesn’t make sense to compare these two.
What does make sense is to compare the acquisition channels of new and returning customers because, ideally, we’re not selling to returning customers the same way we’re selling to new customers.
For instance, with a helpful newsletter or robust email marketing, we should expect a higher percentage of returning customers to come from our email channel.
With strong customer relationships, it should be cheaper to acquire returning customers than new customers. With accurate, order-level acquisition costs, we can compare them.
Hopefully, we’ll see a big drop-off like this. That may come from our (free) email marketing example above, cheaper Facebook retargeting campaigns, or even direct traffic for a very strong brand.
Similarly, returning customers ideally spend more. After creating an accurate order value measurement, we can compare new and returning order values like this:
Note: If you sell big expensive items, like mattresses, you would expect returning customers to actually spend less on their second order. For example, I suspect most people’s first order from Purple is a mattress and their later orders are pillows and sheets. However, that still indicates a strong, valuable customer relationship. Purple may be able to sell new products to these returning customers.
We’ve already gained the trust of returning customers, so we naturally expect them to convert at a higher rate.
Here we can find opportunities to make our store more sticky. For instance, Bonobos lets me save and filter by my clothing measurements. Because I know it’ll fit, the next time I need a shirt, I’m buying from them.
Of course, it’s not just trust that increases returning customers’ conversion rates, but lower friction. To see the impact of friction, look at returning customers’ conversion funnels.
My favorite example is Dirty Lemon, which, after your first order, lets you reorder with just a text message.
The difference between first and second orders —when a new customer becomes a returning customer —is particularly interesting. It’s worth examining customers individually to find any consistencies that aren’t easily quantified.
Be careful about how you handle upsells with your returning customer analysis. Upsell apps in Shopify create a new order. When evaluating the performance of returning customers, you should filter out upsell orders or include them in your order. Better yet, include them in your order value, as discussed here.
If a customer returns their first order, how should you treat their second order? It’s probably best to still treat them as a returning customer in our analysis because their acquisition path is likely different. For instance, if you sell clothing, the refunded customer may really like your clothes —they just need a different size.
If the same person places an order with the same mailing address, but with a different email (say, using their personal email instead of their work email), Shopify will create a new customer. As such, our new and returning customer numbers will never be perfect.
Finally, Shopify does let you delete customers. Doing so deletes the customers’ email addresses and customer profiles in Shopify. You’ll just have to filter these out of your analysis.
Understanding the difference between new and returning customers is essential to understanding the strength of our relationships with them. Finding ways to build that relationship results in lower acquisition costs, higher lifetime values, and increased conversion rates, making our store more profitable in the short and long term.