Customers reports

With your Customer reports, you can gain helpful insights about your customers, including their average order count, average order totals, and expected purchase value.

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 New vs returning customer 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.

Access your customer reports

You can filter your Reports list to display only Customers reports.

Steps:

Desktop

1. From your Shopify admin, go to Analytics > Reports.

2. Click the Category filter.

3. Click Customers to filter the reports to display only Customers reports.

iPhone

1. From the Shopify app, tap the … button, and then tap Analytics.
2. Tap Reports.
3. Tap the filter icon, and then tap Category.
4. Tap Customers to filter the reports to display only Customers reports.
5. Tap < Filter by and then X to navigate back to the filtered Reports list.

Android

1. From the Shopify app, tap the ☰ button, and then tap Analytics.
2. Tap Reports.
3. Tap the filter icon, and then tap Category.
4. Tap Customers to filter the reports to display only Customers reports.
5. Tap ← and then X to navigate back to the filtered Reports list.

New customers over time

The New customers over time report displays how many new 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.

New vs returning customers

The New vs returning customers report displays the number of first-time and returning customers for a given period of time.

You can click Group by to select the time unit that you want to display the number of customers 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 customers 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.

Customers by location

The Customers by location report displays data for new customers organized by geographical location. New customers are organized according to their most recent shipping location.

For each geographical region, you can find the number of new customers who placed their first order 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 average amount that they have spent per order
• 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. For the default Customer cohort analysis report, customers are grouped into cohorts based on the date that they placed their first order. You can use the report's configuration panel to customize the metrics and cohort definition included in the report, and you can apply filters to display cohort information based on matching criteria.

You can use this report to find out which customers have made repeat purchases to identify your most valuable customers. This information can help you make decisions about when and how to re-engage which customers.

Tip

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 in your segment query to limit the segment to that cohort.

Learn more about customer segmentation.

The Customer cohort analysis contains the following elements:

• Report visualization
• Cohort analysis table
• Cohort analysis details

Report visualization

The visualization for the Customer cohort analysis displays as a heatmap by default. You can use the Visualizations menu in the configuration panel to change the type of visualization for the report, including the following types:

• Cohort grid (heatmap)
• Retention curve

Cohort analysis table

By default, 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 month 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 repeat purchases in February 2022 again, and in 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 use the report's configuration panel to customize the metrics and cohort definition included in the cohort analysis table, and you can apply filters to display cohort information based on matching criteria.

Cohort analysis details

You can access the cohort analysis details by clicking any interval cell in the cohort analysis table (for example, the cell in the Sep 2024 row and the Month 2 column). You can find the following details for each cohort interval:

• 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 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

Note

Some of the metrics might not display for a specific cohort or specific time period. For example, if there were no subscription sales in the selected cohort or time period, then the subscription versus one-time metric won’t display.

Projections

The Show projections option displays predictions about the future amount spent per customer cohort. To activate the feature in the Customer cohort analysis report, use the configuration panel's Metric menu to select Amount spent per customer as the displayed primary metric, and then activate the Show projections toggle. The projection data displays in the future months in the table and visualizations.

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 Show projections toggle won't display and you won't have the ability to view projection 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.

Caution

Projections aren't a guarantee of future sales. Although these projections can be helpful for identifying trends in cohort spending over time, be aware that the predicted values can be higher or lower than the true values actually end up being for that time period. Use the data in the projections section with care.

Customizing the customer cohort analysis report

You can use the report's configuration panel to customize the report in the following ways:

• Use the Metric menu to change which metric is displayed, including number of customers, customer retention rate, gross sales, net sales, or average order value.
  • If the primary metric selected is Amount spent per customer, then you can also include projections in the report.
• Use the Visualization menu to change the type of visualization used in the report.
• Use the Cohort definition menu to apply filters to determine which customers are included in the cohort analysis based on their first order.
  • Click the First order icon to apply filters regarding the customer's first order. For example, you can filter to include only customers whose first order was placed from the Shop sales channel. Available first order filters include the following types:
    • Sales channel
    • Marketing channel
    • Marketing type
    • Product name
    • Subscription
• Use the Intervals menu to change the time period that cohorts are grouped by.
• Use the Comparison menu to display comparative data, such as comparisons to the previous period, the previous year, or display a comparison between cohorts.
• Use the Filters menu to apply the following types of changes:
  • Change the Date range that the report displays.
  • Click ⊕ to apply customer filters to limit the types of customer data included in each cohort. For example, you can filter to include only customers based in Canada. Available customer filters include the following types:
    • Customer country
    • Customer region
    • Customer city
    • Customer email subscription status

When you customize the default customer cohort analysis report, you can save it as a custom data exploration.

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.

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

Note

You might observe a discrepancy between the total number of customers and the number of customers with an assigned RFM group. This is due to a brief delay in when new customer data is synced from the store.

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.

Table listing the RFM groups along with their calculation criteria and general RFM goals.

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: Send a welcome email with a first-time customer offer. Highlight your bestsellers and testimonials to emphasize brand value.
Dormant Customers without recent purchases, with infrequent orders, and with low spend.	R ≤ 2	FM ≤ 2	Move customers to Almost lost: Incorporate them into your newsletters. Revive interest with a reach-out campaign highlighting your brand value.
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: Personalize communication at the highest level possible. Send them an offer too good to miss.
Previously loyal Customers without recent purchases, but with a very strong history of orders and spend.	R ≤ 2	4 < FM	Move customers to Loyal: Introduce them to new products or drops. Win them back with renewals or new product offerings.
Needs attention Customers with recent purchases, but some orders, and moderate spend.	R = 3	FM ≤ 2	Move customers to Loyal or Active: Make limited-time offers based on prior purchasing behavior. Reactivate them by engaging with personalized communication.
Almost lost Customers without recent purchases, fewer orders, and lower spend.	R = 3	FM = 3	Move customers to Active or Promising: Reconnect by sharing valuable resources. Offer exclusive discounts on popular products.
Loyal Customers with recent purchases, many orders, and the most spend.	3 ≤ R ≤ 4	3 < FM	Move customers to Champions: Upsell higher-value products. Ask them for reviews.
Promising Customers with recent purchases, fewer orders, and low spend.	R = 4	FM ≤ 1	Move customers to Active: Check in to remind them to replenish their supply. Share more about your brand and educate on your products.
Active Customers with recent purchases, some orders, and moderate spend.	4 ≤ R	1 < FM ≤ 3	Move customers to Loyal or Champions: Send recommendations on top-performing products. Offer memberships or loyalty programs.
New Customers with very recent purchases, few orders, and low spend.	R = 5	FM ≤ 1	Move customers to Active: Set them up for success with onboarding support. Connect and start building relationships.
Champions Customers with very recent purchases, many orders, and the most spend.	R = 5	3 < FM	Retain as brand advocates: Offer exclusive deals and early access to new products. Implement a referral program.

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.

Tip

Despite RFM scores for individual customers not being accessible in your Shopify admin, you can filter the RFM customer list or a customer segment down by adding extra filters that represent specific differences in RFM score.

For example, customers with RFM scores of 525, 534, 544, or 555 would all be considered Champions and are all valuable customers to retain despite the difference in their scores. However, if you wanted to identify your highest-spending champions (that is, those who scored highest in monetary value) to offer them exclusive perks as 'brand ambassadors', you could review the average customer spend for the Champions group as a whole using the RFM customer analysis report, and then filter your customer list to display only champions (rfm_group = 'CHAMPIONS') with a Customer amount spent of 50% higher than that average value. This lists customers with higher monetary value scores of 4 or 5, without needing to identify specific or complete RFM scores for individuals.

Customize the Customers reports

You can use the filtering and editing features to customize the reports about your customers.

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.

Steps:

1. From your Shopify admin, go to Analytics > Reports.

2. Click Categories.

3. Click Customers to filter the reports to display only customers reports.

4. Click Returning customers.

5. From the Returning customers report's configuration panel, click ⊕ in the Filters menu.

6. Select Customer subscription email status.

7. In the new filter, click Select value, and then click Subscribed.

8. Click Apply.

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.