Customers reports
With the following reports, you can gain helpful insights about your customers, including their average order count, average order totals, and expected purchase value:
- Customers over time
- First-time vs returning customer sales
- Customers by location
- Returning customers
- One-time customers
- Customer cohort analysis
- Predicted spend tier
Because of the way that the customer reports are generated, they might not display all the activity on your store from the past 12 hours. However, when you open the First-time vs returning customer sales report report, the data is up to date, give or take a few seconds. You can reopen or refresh the report to display newer data.
The data in customer reports is based on the entire order history of the new customers in the report, not only the orders that were placed during the selected timeframe. For example, if you access a report for November only, then a new customer from that month still displays as a repeat customer, even if they made their second purchase in December.
On this page
- Access your customer reports
- Customers over time
- First-time vs returning customer sales
- Customers by location
- Returning customers
- One-time customers
- Customer cohort analysis
- Predicted spend tier
- At-risk customers
- Loyal customers
- RFM customer analysis
- RFM customer list
- Customize the Customers reports
Access your customer reports
Steps:
Desktop
- From your Shopify admin, go to Analytics > Reports.
- Click Categories.
- Click Customers to filter the reports to display only customers reports.
iPhone
- From the Shopify app, tap the … button, and then tap Analytics.
- Tap Reports.
- Tap Categories.
- Tap Customers to filter the reports to display only customers reports.
Android
- From the Shopify app, tap the … button, and then tap Analytics.
- Tap Reports.
- Tap Categories.
- Tap Customers to filter the reports to display only customers reports.
Customers over time
The Customers over time report displays how many customers placed orders with your store.
You can select a unit of time in the Group by drop-down menu to control how the data is grouped.
The report table displays two rows for each time unit when there are both types of customer: one for first-time customers, and one for returning customers. A first-time customer is a customer who placed their first order with your store. A returning customer is a customer who placed an order, and whose order history already includes at least one order.
For each time unit, you can find the following data:
- The number of new (first-time) customers who placed an order during that time. Such a group of customers is often called a new cohort of customers.
- The number of returning customers who placed an order during that time.
First-time vs returning customer sales
The First-time vs returning customer sales report displays the value of orders placed by first-time and returning customers.
You can click Group by to select the time unit that you want to display the total sales by in the graph: hour, day, week, month, quarter, year, hour of day, day of week, or month of year. The time unit specifies how the total sales are grouped.
The report table display two rows for each time unit when there are both types of customer: one for first-time customers, and one for returning customers. A first-time customer is a customer who placed their first order with your store. A returning customer is a customer who placed an order, and whose order history already includes at least one order.
For each time unit, you can find the following data:
- the number of orders placed by each group of customers
- the value of orders (total sales) placed by each group of customers
Customers by location
The Customers by location report displays data for new customers organized by geographical location. New customers are organized according to the geographical location in their default address in your Shopify admin.
For each geographical region, you can find:
- the number of new customers who placed their first order during the selected timeframe
- the total number of orders that those customers have placed as of their first order
- the total amount that they have spent, including taxes, discounts, shipping, and any refunds
The Orders and Amount spent to date totals are based on the entire order history of the new customers in the report, not only the orders that were placed during the selected timeframe.
Returning customers
The Returning customers report displays data about all your customers whose order history includes two or more orders.
You can find the following details for each customer:
- their name
- their email address
- whether they agreed to accept marketing when they placed their most recent order
- the date of their first order
- the date of their most recent order
- the number of orders that they have placed
- the total amount that they have spent, including taxes, discounts, shipping, and any refunds
One-time customers
The One-time customers report displays data about all your customers whose order history includes only one order.
You can find the following details for each customer:
- their name
- their email address
- whether they agreed to accept marketing when they placed their most recent order
- the date of their first order.
- the number of orders that they have placed, which is 1
- the value of their order, including taxes, discounts, shipping, and any refunds
Customer cohort analysis
The Customer cohort analysis report displays data about your customer acquisition and retention. A cohort is defined as a group of customers that have similar characteristics. For the Customer cohort analysis report, customers are grouped into cohorts based on the date that they placed their first order.
You can use this report to find out which customers have made repeat purchases to identify your most valuable customers. You can use this information to help you make decisions about when to retarget customers, which customers to retarget, and which customers are lower value.
The Customer cohort analysis contains the following elements:
Cohort analysis table
The cohort analysis table displays data about repeat purchases by customers based on when they made their first purchase. Each row represents a cohort of your customers that made their first purchase in the same time period. The first column displays the name of the cohort, based on the week, month, or quarter of their first purchase. The second column displays the sum of the selected metric for each cohort row. The third column displays the selected metric for the cohort's first orders. The rest of the columns display the selected metric over the weeks, months, or quarters as of their first order. The period 0 column captures returning orders by the cohort's customers in the same period as their first order.
For example, your customer John made their first purchase in February 2022. John then made another purchase in February 2022, June 2022 and in September 2022. In a monthly cohort analysis for 2022, John would be in the February cohort and would be counted as a repeat customer for Month 0, Month 4, and Month 7.
You can customize the report in the following ways:
- change the time period that cohorts are grouped by
- change the time period that the report displays
- change which metric is displayed, including number of customers, customer retention rate, gross sales, net sales, or average order value
- include predicted values for the Amount spent per customer metric
- apply filters to determine which customers are included in the cohort analysis (the cohort inclusion criteria). There are two types of filters: first order filters and customer filters (about the customers in the cohort)
- view cumulative and non-cumulative sales metrics
Retention rate chart
The Retention rate chart displays the retention rate of all first-time customers during the time period that the report displays. You can also display the following comparisons:
- Comparison to previous period
- Comparison to previous year
- Comparison between cohorts
You can also display the retention rate for customers in all cohorts for the selected time period, or select a single cohort to display the retention rate for.
Cohort analysis details
You can access the cohort analysis details by clicking All, or a specific time period in the Cohort column. You can find the following details for each cohort:
- gross, net, and total sales for the selected cohort
- average order value and average number of orders per customer in the cohort
- amount spent per customer
- total new or returning customers and their total number of orders
- the top products baskets sold to the customers in the selected cohort
- the top marketing channels responsible for directing the cohort’s customers to your business
- the top sales channels that processed the cohort’s orders
- the predicted spend tier overview for the cohort
- a ratio of orders containing one-time vs subscription purchases
- the top geographic locations of customers in this cohort
Predicted values
The Predicted values option displays predictions about the future amount spent per customer cohort. To activate the feature in the Customer cohort analysis report, select Amount spent per customer as the displayed metric and then activate the Predicted values toggle. The prediction data displays with a purple highlight in the future months in the table, and as a purple line in the line chart.
These predictions are based on the data from your store, compiled over the previous 24 months for each cohort, in order to give an estimate for the anticipated average amount spent per customer in each cohort. If 24 months of data aren't available, then the Predicted values toggle won't display and you won't have the ability to view prediction data. The data isn't based on information from any other stores or averages for your industry, and data isn't shared with any other stores.
Cohort inclusion filters
There are two types of filters you can apply that determine which customers are included in the cohort analysis:
- First order filters (characteristics about the customers’ first order): sales channel, marketing channel, marketing type, product name, subscription
- Customer filters (characteristics about the customers within a cohort): country, region, city, email subscription status
Using the cohort analysis report for customer segmentation
You can use the data from the Customer cohort analysis report to create customer segments out of high-value customer cohorts.
For example, if the customer cohort for June of 2022 indicates high retention, then you can create a customer segment by using the First_order_date BETWEEN 2022-06-01 AND 2022-06-30 .
Learn more about customer segmentation.
Predicted spend tier
The Predicted spend tier report displays the predicted value of each customer in the selected cohort. This report can help you target customers that are part of the highest value cohorts. You can find the following details for each customer in the cohort:
- customer name
- the predicted spend tier
- email subscription status
- the date the customer placed their last order
- the number of orders that they have placed
- the total amount that they have spent, including taxes, discounts, shipping, and any refunds
Learn more about how the predicted spend tier is determined.
At-risk customers
Loyal customers
RFM customer analysis
The recency, frequency, and monetary value (RFM) analysis lets you deep-dive into customer behavior so you can focus on building retention, loyalty, and customer relationships with existing customers based on their existing shopping habits.
RFM analysis applies a 3-digit score to each customer, where each digit ranges from 1 to 5, and relates to the days from a customer's most recent purchase (recency), the total number of orders (frequency), and the total amount spent (monetary value), in order. RFM analysis then categorizes all customers into 11 RFM groups based on their scores, which you can use to plan effective sales goals, marketing strategies, and loyalty programs.
RFM scores are based only on data from your store, and not on industry standards or third-party data. For example, a score of 5 indicates that the customer is in the top 20% of the dimension for your store, and a score of 1 indicates that the customer is in the bottom 20% of the dimension for your store. Specific RFM scores for individual customers aren't displayed anywhere in your Shopify admin, as the most valuable insight is the customer's overall RFM group.
For each RFM group, the report's data table lists the following metrics by default:
- Percentage of your total customers
- New customer records
- Average days since last order
- Total number of orders
- Total amount spent
RFM groups
RFM groups are 11 predefined customer categories based on order recency, frequency, and monetary value criteria. A customer's RFM group is determined based on a customer's recency score (R) and the average between their frequency score and their monetary value score using the formula (F + M)/2. The end result is represented as FM.
For example, a customer used to place higher-value orders quite often, but hasn't made a purchase in a long time. This means they might have an RFM score of 154, which indicates that they have a low recency score (1), but a decently high frequency (5) and monetary value (4) score. Their R value is 1, and their FM value would be 4.5, using the formula (5 + 4)/2. Overall, based on how the groups are calculated, these values would categorize this customer as Previously loyal.
Refer to the following table for definitions for each RFM group, as well as the general goal when engaging with that group.
| RFM group and description | Recency (R) | Average of Frequency and Monetary value (FM) | RFM goal and examples of ways to engage |
|---|---|---|---|
|
Prospects Customers with no orders yet. | - | - | Move customers to New:
|
|
Dormant Customers without recent purchases, with infrequent orders, and with low spend. | R ≤ 2 | FM ≤ 2 | Move customers to Almost lost:
|
|
At risk Customers without recent purchases, but with a strong history of orders and spend. | R ≤ 2 | 2 < FM ≤ 4 | Move customers to Loyal or Needs attention:
|
|
Previously loyal Customers without recent purchases, but with a very strong history of orders and spend. | R ≤ 2 | 4 < FM | Move customers to Loyal:
|
|
Needs attention Customers with recent purchases, but some orders, and moderate spend. | R = 3 | FM ≤ 2 | Move customers to Loyal or Active:
|
|
Almost lost Customers without recent purchases, fewer orders, and lower spend. | R = 3 | FM = 3 | Move customers to Active or Promising:
|
|
Loyal Customers with recent purchases, many orders, and the most spend. | 3 ≤ R ≤ 4 | 3 < FM | Move customers to Champions:
|
|
Promising Customers with recent purchases, fewer orders, and low spend. | R = 4 | FM ≤ 1 | Move customers to Active:
|
|
Active Customers with recent purchases, some orders, and moderate spend. | 4 ≤ R | 1 < FM ≤ 3 | Move customers to Loyal or Champions:
|
|
New Customers with very recent purchases, few orders, and low spend. | R = 5 | FM ≤ 1 | Move customers to Active:
|
|
Champions Customers with very recent purchases, many orders, and the most spend. | R = 5 | 3 < FM | Retain as brand advocates:
|
RFM group customer segmentation
You can click an RFM group name in the report's data table to do any of the following actions:
- View report: Redirects you to the RFM customer list and automatically applies the selected RFM group as the filter.
- Preview segment: Redirects you to the segment editor and automatically applies a customer segment based on the selected RFM group and any additional filters you've applied to the report.
You can also manually apply the rfm_group attribute as a filter when building segments. Learn more about customer segmentation.
RFM customer list
The recency, frequency, and monetary value (RFM) customer list is a complete list of customers who aren't in the Prospects RFM group, and is displayed with the following data columns:
- Average days since last order
- Total number of orders
- Total amount spent
Learn more about how RFM groups are calculated and categorized from the RFM customer analysis report.
You can apply additional dimensions and filters to customize a new exploration. Click Preview segment to navigate to the segment editor and automatically apply a customer segment based on the selected RFM groups and any additional filters you've applied to the report.
Customize the Customers reports
You can use the filtering and editing features to customize the reports about your customers.
The following is a sample of some of the filters and columns that are available, where applicable.
Filters for the Customers reports
| Filter category | Filter name - Definition |
|---|---|
| Customer |
|
| Customer attributes |
|
| Customer segment |
|
| Location |
|
Columns for the Customers reports
| Column category | Column name - Definition |
|---|---|
| Customer |
|
| Customer attributes |
|
| Customer segment |
|
| First order |
|
| Last order |
|
| Location |
|
| Orders |
|
| Time |
|
Example customization: Target an email campaign towards returning customers
If you want to use an email campaign to encourage returning customers to make another purchase, then you could customize your Returning customers report so that it displays only the returning customers who agreed to accept marketing.
To create the report for this example:
Desktop
- From your Shopify admin, go to Analytics > Reports.
- Click Categories.
- Click Customers to filter the reports to display only customers reports.
- Click Returning customers.
- From the Returning customers report, click Manage filters.
- Click Add filter.
- Select Accepts marketing, and then in Search, select Yes.
- Click Apply filters.
iPhone
- From the Shopify app, tap the … button, and then tap Analytics.
- Tap Reports.
- Tap Categories.
- Tap Customers to filter the reports to display only customers reports.
- Tap Returning customers.
- From the Returning customers report, tap Manage filters.
- Tap Add filter.
- Select Accepts marketing, and then in Search, select Yes.
- Tap Apply filters.
Android
- From the Shopify app, tap the … button, and then tap Analytics.
- Tap Reports.
- Tap Categories.
- Tap Customers to filter the reports to display only customers reports.
- Tap Returning customers.
- From the Returning customers report, tap Manage filters.
- Tap Add filter.
- Select Accepts marketing, and then in Search, select Yes.
- Tap Apply filters.
The report is now limited to returning customers who accept marketing.
You can then export the report to a CSV file, and you can use all the email addresses in the file for your email campaign.