Data Analytics

DTC Metric Feature: Customers per Number of Orders

What is the Metric?

When you look at your retention metrics, some of the most compelling data comes in the form of which customers are making subsequent purchases and how often. This is exactly where your Customer per Number of Orders metric comes in.

Customers per Number of Orders is a Cohorts Retention metric that looks at the subsequent number of orders made by your customers in each cohort. When you’re looking at the cohort chart, rather than months in its columns, you’ll see the number of orders. This metric helps you see how many of your customers from each cohort make their 2nd purchase and beyond. 

This is a cumulative metric, so it gives you a quick look at the overall performance of each cohort.

How Can You Use the Metric? 

Customers per Number of Orders is going to be a tool that helps you understand which products, discount strategies, or other marketing campaigns are driving in customers who are making multiple purchases.

Remember: The more your customers are purchasing, and the more often, the better for your growth. In fact, studies show that an increase of even just 5% in customer retention can translate to anywhere from 25% to 95% increase in profit. So, refining your retention strategy is crucial to your brand’s growth.

Viewing trends in your Customers per Number of Orders can help you identify which of your efforts is most effective for retention – that can include anything from product offerings to specific campaigns. 

When you see outliers in your data – months where you acquired customers who are repurchasing often, or months where your customers aren’t really coming back – you can dig deeper and figure out what caused these trends.

Ask yourself these questions:

  • Do my products have built-in retention drivers, such as the need to replenish, the need for supplemental products, etc.?
  • What products are customers purchasing that keep them coming back? When they repurchase, are they buying the same product again or different products?
  • Did certain months’ cohorts receive specific email or SMS campaigns that contributed to their return?
  • How are your discounts playing a role here? Are customers enticed by one-off promotions or possibly customer loyalty benefits?
  • What targeted ads were customers exposed to before returning to repurchase? Is your ad spend improving retention?

As you find the answers to these questions and see how they correlate with your Customers per Number of Orders metric, you can work on improving your product offerings, repeating successful marketing campaigns and ad campaigns, and creating new discount offerings and product bundles based on your customer behavior.

Customers Returning Rate Use Case

Let’s jump into an example provided by our friends at the Fruit Stand.

As summer approaches and they start ramping up business for their busy Q2, they wanted to see the results of last year’s discount strategy that they used to push their purchasing momentum through Q3 & Q4 with more repurchases.

Here’s a snapshot of their Cohort Customers per Number of Orders:

As you can see, their repurchase is strong from April - November with high numbers of customers acquired and plenty of those customers coming back for subsequent purchases.

Heading into Q3, they started pushing their “FarmersFriend” promotion, where customers could use the code on their second purchase for 20% off. 

Their strategy was twofold. They sent out retention emails to their customers who made their first purchase promoting the 2nd purchase discount, and they used call-to-action (CTA) banners and pop-ups on their site to capture new customer info with the incentive to learn more about their products and stay in the loop for discounts. 

For those who didn’t make a purchase, but dropped their info, they sent out an email campaign that included some knowledge about which of their products were best together, with a blog post linked to expand on that, along with a plug for the “FarmersFriend” discount. The idea here being that if they could entice the customer to make their first purchase, and they had the seed planted for which products might be good follow-up purchases, they’d be more likely to use the 20% code.

By choosing to segment their Customers per Number of Orders metric by their “FarmersFriend” code, here’s what they got:

As you can see, they were able to get a high number of customers to come back with that code in late Q3/early Q4 to keep their repurchase momentum going that they naturally experience in Q2 and early Q3. 

If you want to learn more about how they segmented their data, check out our Customer Tags article. It gives a full walkthrough of how to segment and the same steps can be applied to segmenting by Discount Codes!

The most important thing to remember is that this metric is just a piece of the puzzle. To create and execute a well-rounded marketing strategy, our friends at the Fruit Stand would be looking at their Customers Returning Rate to have a better idea of the timing of their retention marketing.

They’d also be looking at things like lifetime value (LTV) of their cohorts and average order value (AOV) to make sure they are attracting and retaining high value customers who are spending a good amount on each of their purchases. 

The lesson here is that one metric in a silo is not going to solve all your problems or elevate your growth strategy. It’s about looking at a suite of metrics and segmenting strategically to get the full story within your data. 

You can learn more about the ideal method and the metrics that will serve your DTC brand the most in our full Guide to E-Commerce Analytics.

Bonus DTC Metric! Cohort Customers Per Count of Days with Orders

While you’re in your Cohort Customers per Count of Orders metric, you can use the “For” button to swap to a similar metric: Cohort Customer per Count of Days with Order.

If you checked out the video above, Randy went over why you’d want to swap to this. Basically, we’ve had customers who’ve requested wanting their columns in this cohort chart to represent the number of days with orders instead of number of orders. That means that if customers come back on the same day to make another purchase, it’s only counted as 1 for that day rather than the number of individual orders. This supports the use case when customers placed multiple orders in a single day because of a promotion.

This gives you a better idea of how many customers are making subsequent orders - more “volume of repurchasing customers” rather than “sheer volume of orders” if you need that level of granularity!

If you want to try out these cohort retention metrics for your DTC brand, or want to get a feel for Peel’s full suite of automated e-commerce analytics, start a free 15-day trial with just a single click on the Shopify app store.