Repeat Customers: How to Identify Them in Shopify and Know When They'll Buy Again
Loyalty programs and rewards are excellent ways to retain customers, but they aren't the only ways to keep customers engaged and encourage repeat purchases. Sometimes, reaching the right customers at the right time just when they're most likely to buy again can be just as important.
Shopify already offers extensive analytics reports to help merchants understand customer behavior, including tools for identifying repeat customers. Its free RFM (Recency, Frequency, Monetary Value) analysis provides valuable insights into purchasing habits, customer loyalty, and potential churn risks.
1. How to Identify Repeat Customers in Shopify
Shopify provides customer reports that help merchants understand who buys from their store and how purchasing behavior changes over time.
A repeat customer is generally someone who has placed more than one order. Shopify's customer reports can help you identify these customers, compare new and returning customer activity, and examine purchasing patterns.
To explore this data, navigate to Shopify Admin / Analytics / Reports and look for customer reports available on your plan.
These reports are useful for understanding your existing customer base, but knowing who has purchased repeatedly doesn't necessarily tell you when they are likely to purchase again.
2. Understanding Shopify's RFM Customer Analysis
One of Shopify's more advanced customer analytics features is RFM analysis. RFM stands for Recency, Frequency, and Monetary value.
It evaluates customers using three dimensions:
- Recency: How recently a customer placed an order.
- Frequency: How many orders a customer has placed.
- Monetary value: How much a customer has spent.
Shopify assigns a score from 1 to 5 for each dimension, based on how customers compare with others in the same store. It then uses these scores to classify customers into 11 groups. For example, a customer who has purchased recently, ordered frequently, and spent a relatively large amount may be classified as a Champion. Someone with a strong purchasing history who hasn't ordered recently may be classified as Previously loyal or At risk.
These groups help merchants understand customer behavior and decide where to focus their retention efforts.
Shopify's 11 RFM customer groups. The groups provide different perspectives on customer engagement:
- Champions: Recent customers with frequent purchases and high spending.
- Loyal: Customers with a strong history of purchasing and spending.
- Active: Customers who have purchased recently and show moderate engagement.
- Promising: Relatively recent customers who have not yet developed strong purchasing habits.
- New: Customers who have purchased recently but have limited purchasing history.
- Needs attention: Customers whose engagement and purchasing history suggest that additional attention may be worthwhile.
- At risk: Previously valuable customers who haven't purchased recently.
- Previously loyal: Customers with a strong history of purchasing who have become inactive.
- Almost lost: Customers with limited purchasing activity who may be disengaging.
- Dormant: Customers with little recent activity, infrequent orders, and low spending.
- Prospects: Customer records without completed orders.
Shopify also provides an RFM customer list and allows merchants to filter customers and preview segments based on RFM groups.
What can merchants do with RFM analysis?
RFM analysis can help merchants identify their most valuable customers, recognize declining engagement, plan loyalty campaigns, and create targeted marketing segments. For example, a merchant could target Champions with early access to new products or contact At risk customers with a personalized offer. However, RFM groups are based on relative customer scores and historical behavior. They are not designed to calculate the expected next order date for every customer. That distinction matters for stores selling products customers replenish regularly.
3. The Difference Between Customer Segmentation and Reorder Timing
Customer segmentation answers the question: Who are my most valuable customers, and who might need attention?
Reorder timing answers a different question: Which customers are approaching the time when they would normally buy again?
Consider a customer who purchases pet food approximately every 30 days. They placed their last order 24 days ago. Their purchasing history might qualify them as a Loyal or Active customer in Shopify's RFM analysis, but that classification doesn't itself tell the merchant that the customer may be due to reorder in about six days.
Similarly, a customer who usually buys skincare products every 60 days might be overdue for a reorder even if their overall customer value is relatively low. Both customers could represent opportunities for a timely reminder. The important difference is that reorder timing considers the interval between purchases, rather than relying exclusively on how a customer's historical behavior compares with that of other customers.
4. How RepeatFlow Helps Shopify Merchants Identify Reorder Opportunities
RepeatFlow is designed to help merchants turn customer order history into a practical workflow for repeat purchases. Instead of requiring merchants to interpret customer scores and construct multiple segments, RepeatFlow organizes customers according to their estimated reorder timing.
Depending on their purchasing history, RepeatFlow groups customers into 5 different segments
- Due Soon: Customers approaching their expected reorder date.
- Due Now: Customers who have reached their estimated reorder date.
- Overdue: Customers who have passed their expected reorder date.
- Churn Risk: Customers whose usual purchasing interval has been exceeded significantly.
- Not enough history: Customers how have placed less than 2 orders.
This gives merchants a straightforward way to see which customers may need attention without manually analyzing each customer's order history. RepeatFlow is particularly relevant for stores selling replenishable products, such as pet supplies, supplements, cosmetics, coffee, and household essentials.
The more consistent a customer's purchasing cycle, the more useful an estimated reorder date can be. Predictions are estimates, however, and not every customer will follow the same pattern.
Since different products have different repurchase cycles, RepeatFlow lets you define basic rules based on how often customers are likely to buy again. For example, customers might purchase food every week, while medical clothing might be purchased only every six months. These settings help the app estimate when customers are due to reorder and identify the right time to reach out.
5. Shopify RFM vs. RepeatFlow: Which Should You Use?
Shopify's RFM analysis and RepeatFlow address related but different needs.
Use Shopify RFM analysis when you want to:
- Understand the composition and value of your customer base.
- Identify high-value and at-risk customer groups.
- Analyze purchasing behavior and customer retention.
- Build segments for targeted marketing campaigns.
Use RepeatFlow when you want to:
- Identify customers approaching their next expected purchase.
- See who is due to reorder, due now, or overdue.
- Prioritize customer follow-up based on estimated purchasing cycles.
- Build a workflow around timely reorder reminders.
These approaches can complement each other. RFM analysis helps you understand the value and engagement of your customers, while reorder timing helps you identify potential opportunities to bring them back.
How to Use RepeatFlow Customer Data in Klaviyo
RepeatFlow helps Shopify merchants identify customers who are approaching their next purchase, are due to reorder, or may be at risk of becoming inactive. By integrating with Klaviyo, RepeatFlow makes this information available for personalized email marketing and automated customer retention campaigns.
What Data Does RepeatFlow Send to Klaviyo?
RepeatFlow sends a Repeat Purchase Status event containing customer information and purchasing behavior, including:
Customer segment: Whether the customer is Due Soon, Due Now, Overdue, or in another RepeatFlow segment.
Order history: The number of orders and the date of the last purchase.
Expected order date: When the customer is estimated to make their next purchase.
Purchase cycle: The customer's median interval between orders.
Days until due or overdue: How close the customer is to their expected reorder date, or how many days late they are.
Segment reason: An explanation of why the customer belongs to a particular segment.
This data helps you create more relevant marketing campaigns based on each customer's purchasing behavior rather than sending the same message to everyone.
6. How to Use RepeatFlow Data in Klaviyo
1. Create targeted customer segments
Use the Repeat Purchase Status event and its properties to identify customers who need different types of communication.
For example:
- Due Soon: Customers who may benefit from a reminder before their next expected purchase.
- Due Now: Customers who have reached their estimated reorder date.
- Overdue: Customers who have passed their expected reorder date without placing another order.
- Churn Risk: Customers whose purchasing cycle suggests they may be becoming inactive.
The exact segment conditions should match the property values sent by RepeatFlow.
2. Build automated email flows
In Klaviyo, create flows triggered by the Repeat Purchase Status metric. Add conditional splits based on the event properties to tailor messages to each customer segment. For example, you could send a friendly replenishment reminder to customers who are due to reorder, followed by a follow-up email if they haven't purchased after a few days.
For overdue customers, you could send personalized product recommendations or a special offer to encourage another purchase.
3. Personalize your email content
Use the event properties to make messages more relevant to individual customers. For example, you can reference their previous purchasing activity or use their expected reorder date to time a reminder. You can also use the median purchase interval to help plan follow-up timing around each customer's usual buying habits.
4. Measure the results
Track purchases and revenue associated with your Klaviyo flows to understand whether timely reminders help increase repeat purchases. Compare campaign performance across customer segments to identify which messages and timing work best.
Turn Customer Insights Into Repeat Purchases
Shopify analytics can help you understand your customers, while RepeatFlow identifies potential reorder opportunities based on their purchasing cycles. By sending this data to Klaviyo, you can use those insights to create targeted campaigns and automated reminders that encourage customers to return at the right time.
RepeatFlow + Klaviyo helps you turn customer purchasing patterns into actionable retention campaigns.
Conclusion
Shopify already provides useful tools for understanding repeat customers, including customer reports and RFM analysis. Merchants don't necessarily need another tool simply to count repeat purchases or divide customers into more segments.
The next challenge is deciding when to act.
For stores with recurring or replenishable products, identifying customers who are approaching their next expected purchase can make customer retention more actionable.
RepeatFlow helps Shopify merchants focus on that next step: identifying reorder opportunities and reminding customers when they may be ready to buy again.