Loyalty
Shopify hosts over 4 million stores worldwide. It is a go-to marketing platform for all kinds of sellers, including solo founders selling their first products, established brands doing 8 figures a year, growing companies, and more.
The setup speed and the app ecosystem are definitely the main draws for merchants. People appreciate the possibility of building and running a sustainable business without spending big sums on a technical team.
Still, creating and launching an online store is just half of success. Another half is attracting prospects, converting them into loyal customers, and encouraging them to return again and again. Shopify alone cannot solve that for you.
That is why running in-depth customer loyalty analytics is a must. The system brings together customer data, including purchase history, information about your returning customers, their purchase habits and customer preferences, risk spots and potential churn rates, as well as the overall efficiency of your loyalty program.
Such information is crucial for building a sustainable loyalty marketing strategy. And this blog article explores the topic in detail, covering numbers you should measure, the setup process, possible ways to understand whether your loyalty campaign runs as intended, and best practices to retain existing customers.
What Is Customer Loyalty Analytics?
Customer loyalty analytics is the process of gathering data about your customers, so that you can further act on it. In other words, it is a readable peek into the behavior of people buying goods from you and the probability that they will want more. Customer analytics maps the entire customer journey, from acquisition to conversion. Retail loyalty analytics narrows that scope to post-purchase behavior alone, e.g., how much people spend and which products they favor.
Customer loyalty data analytics helps you understand:
Whether members are worth more than non-members;
Which rewards yield the best visit results;
When customers usually disengage;
Whether there is a realistic window to bring them back;
If a loyalty program truly affects the buying behavior or just rewards it.
The last question is probably the most important one, though not all Shopify stores treat it responsibly. Besides, it is the one that defines whether your loyalty spend becomes an investment or a discount scheme with little impact on the pool of returning clients.
Customer loyalty analytics has a forward-looking scope. By scanning historical data, you can see what happened, but by adding behavioral patterns and predictive analytics, it is easier to understand changing customer behavior and deliver personalized experiences that align with evolving market trends. In case a pattern breaks, you still have a good window to reach out to a person and bring them back.
What You Can Understand from Shopify’s Built-In Reports
Shopify has embedded reporting tools that you can use to get basic info. It is enough to stay informed but too little for running a successful loyalty program.
Shopify report | What it tells you | What it doesn't tell you |
Cohort analysis | Repeat purchases by first-purchase month, retention rate over time | Whether your loyalty program caused the return |
Customer reports (RFM) | Recency, frequency, and monetary value per customer | Program enrollment status or reward redemption history |
Purchase frequency | Average orders per customer over a period | Which customers are program members |
Repeat customer rate | % of customers who bought more than once | Incremental lift from the program vs organic behavior |
Sessions by referral | Traffic source before purchase | Post-purchase engagement or reward usage |
Shopify brands that want a clearer picture of their loyalty program effectiveness opt for a cohort analysis. The report shows customers acquired on a monthly basis and how many of them return in the following months. So, it becomes evident whether the retention rate is sound quarter over quarter.
Still, much like other approaches, cohort analysis treats all returning customers as equivalent. In other words, people who have enrolled in your Shopify loyalty program and those who neglected it fall in the same category.
To connect program activity to purchase behavior, you need an analytics layer that sits on top of Shopify's transaction data and joins it with loyalty events.
Customer Loyalty Metrics Every Shopify Brand Should Track
8 key engagement metrics make up the foundation of any serious custom loyalty program analytics setup. Effective customer segmentation helps you understand why your customer loyalty campaign lags and what can be fixed.
Customer Loyalty Metric | Formula | Where the data comes from in Shopify | What it shows |
|---|---|---|---|
Repeat purchase rate (RPR) | Customers with 2+ orders ÷ total customers × 100 | Customers report, or orders exported and grouped by customer | Share of buyers who came back at least once |
Customer retention rate (CRR) | ((Customers at end − new customers) ÷ customers at start) × 100 | Customer cohort analysis | Share of an existing customer base still buying |
Purchase frequency | Total orders ÷ unique customers, over a period | Orders report, Customers over time | How often the average customer orders |
Time between orders | Average days between a customer's consecutive orders | Order timestamps, exported and calculated | How fast customers cycle back; useful for timing win-backs |
Customer lifetime value (CLV) | Average order value × purchase frequency × average customer lifespan | Customer lifetime value report (plan-dependent) | Total revenue a customer is worth over the relationship |
Average order value, members vs. rest | Revenue ÷ orders, calculated separately per group | Orders report, filtered by customer tag or segment | Whether enrolled customers spend more per order |
Churn rate | Customers lost in a period ÷ customers at the start × 100 | Derived from customer cohort data | How fast you're losing customers who used to buy |
Net Promoter Score (NPS) | % Promoters (score 9-10) minus % Detractors (score 0-6) | Not in Shopify; collected via a separate survey tool | Likelihood customers will recommend your store |
Metrics without action are just numbers. To increase customer loyalty with analytics, you need to review those figures regularly and adapt your loyalty strategy based on them. If CLV is climbing, double down on tactics that work.
For example, if you notice that a repeat purchase rate is slipping, it is high time to rethink your post-purchase customer interaction strategy.
Wondering How to Track These Metrics Inside Your Loyalty Platform?
Book a free call with our team, and we'll show you exactly how to do it using the Enable3 Analytical Dashboard.
Loyalty Program Analytics: Is Your Program Working?
After you have calculated your store metrics according to the listed formulas, you should focus on the loyalty program-specific data. Incorporate customer feedback and track your customer satisfaction score to understand whether your current marketing efforts bring the desired results. This data also helps identify potential brand advocates within your base.
Enrollment Rate
Low enrollment usually means that customers do not know about your loyalty program when making a purchase, or the perks you offer aren’t persuasive enough to keep customers engaged and motivated to act. Enrollment is the foundation of a marketing strategy, so fix it in the first place before optimizing anything else in your customer loyalty programs analytics setup.
Active Member Rate
This is one of the most important health metrics in customer loyalty programs analytics. Strangely enough, some Shopify stores under-track it.
“Active” means someone who earned or redeemed points in the last 90 days. Even if you have a large enrolled base, but people remain passive, this is not your loyalty asset.
40% is a good benchmark. Loyalty programs centered on missions or time-limited challenges usually target above 60% because such customers are more motivated to return and make another purchase.
Redemption Rate
This metric shows how many earned rewards were claimed once customers are enrolled. The number can fluctuate, but extreme values call for through watching.
A redemption rate under 15% is considered very low. It either means that customers do not like the rewards offered or the process of getting them is inconvenient for people.
Redemption that exceeds 80% is very high. It can also cause problems, squeezing margins, particularly if you did not expect such a level of use and your budget can not support it.
The safest value is between 25% and 50%.
Points Liability
Every unspent point is a future discount you’ve already committed to.
Make it a habit to track points liability every month. If the number gets high, it typically means that more customers are actively engaged. But if you notice that points liability exceeds your redemption rate month over month, you may need to reconsider your rewards economics. Even established Shopify brands try to avoid cost pressure before it becomes a pressing issue.
Tier Distribution
If you have several loyalty tiers, but most customers occupy the lowest one, something is definitely wrong. Primarily, check whether thresholds are realistic to cross for average customers. Next, verify that offered goodies, including exclusive access to premium rewards, are lucrative enough so that people want to get to upper tiers. Your most loyal customers should feel genuinely rewarded for reaching the highest level.
Referral Conversion Rate
Referral conversion rate shows how many new people who came through shared referral links became your new customers. Referral customers demonstrate higher CLV and lower churn than paid acquisition. There are 2 main reasons for a low referral conversion rate: a poor landing experience for the referred customer or a confusing incentive structure for the person doing the referring. You can solve both problems.
The numbers read differently in practice. For example, a fashion brand with 40% enrollment and a 35% redemption rate looks like a healthy program. But if its active member rate is 15%, those redemptions are concentrated in a small cohort, and the program is not moving the middle of its customer base.
A subscription coffee store with 25% enrollment and 55% redemption tells a different story. There are fewer members, but almost all of them use their rewards regularly. That's a smaller, more deliberate program that's working well within its scope.
Most Shopify stores start with the earn and burn loyalty model based on points that translate directly into discounts. Generally, it's the first model to show program fatigue once the initial novelty wears off.
Is a Customer Loyalty Program Successful if Customers Spend More?
Elevated member spending is often the first metric loyalty managers point to when demonstrating program value. However, that figure alone tells an incomplete story. The more precise and important question is whether the program itself generated that behavior, or whether your most valuable customers simply self-selected into membership because they were already inclined to shop frequently.
Use the methods below to do so.
Holdout group testing
The most analytically sound method is a structured holdout test. Prior to launching any new program mechanic or campaign, assess your entire customer base and divide eligible customers into two randomly assigned groups. One group receives the initiative; the other does not. By measuring purchase frequency and average order value across both groups after 60 to 90 days, you receive a defensible, data-backed assessment of program-driven lift rather than assumed correlation.
Cohort-based retention analysis
A more refined alternative to broad member-versus-non-member comparisons is cohort analysis. Using customer segmentation, you can identify customers by purchase date and divide them into distinct customer groups (enrolled vs. non-enrolled) to track behavioral differences over a defined period. A meaningful divergence in retention or repurchase rates indicates true program influence. However, curves that remain largely parallel suggest the program is recognizing pre-existing loyalty rather than cultivating new behavior.
Matched pre-enrollment benchmarking
Where controlled testing is operationally difficult, matched benchmarking offers a credible middle ground. Pair each enrolled member with a non-member whose purchasing activity in the 90 days prior to enrollment was comparable. Track behavioral divergence from that point forward to see the impact of your customer loyalty campaign.
The result you're looking for is called incremental lift. It is the behavior change that wouldn't have happened without the loyalty program. A customer who was going to repurchase anyway is valuable, but they're not evidence that your loyalty investment is working. Through careful data analysis, you can identify the customers who accelerated their repurchase cycle, increased their basket size, or returned after a break, building meaningful relationships and proving your program's true impact. Customer loyalty analytics tracking should be focused on these people.
How to Set Up Loyalty Analytics on Shopify?
You don’t need a data team for this. Generating data-driven insights through loyalty analytics is an ongoing process that follows several manageable steps.
Let's dive deeper into each one.
Step 1: Capture your baseline before you change anything
Track repeat purchase rate, AOV, purchase frequency, and churn rate. Record these before launching anything. They are your “before” numbers needed to measure progress.
Step 2: Pull what Shopify already gives you
Go to Shopify Analytics under Reports. Check “New vs. returning customers” and “Sales by customer.” Export monthly. Cross-reference with enrollment data. Build a simple comparison table.
Step 3: Connect loyalty events to your analytics platform
Shopify doesn’t track loyalty events like point redemptions or tier changes. Connect your loyalty platform to Firebase, Amplitude, etc. Thus, transaction and engagement data will be trackable in one space.
Step 4: Build a focused dashboard
Track active member rate, redemption rate, purchase frequency, points liability, mission completion rates, and tier distribution weekly.
Step 5: Define your intervention window
Use historical data to find when customers typically stop returning. Set an automated trigger just before that point. A timely win-back message helps you retain loyal customers before they drift entirely.
Step 6: Commit to a monthly program review
Schedule a fixed monthly review. Check shifts in active member rate, tier distribution, and mission completion. Each session is your opportunity to leverage insights and decide what to test, adjust, or remove.
How to Increase Customer Loyalty with Analytics
McKinsey research shows that businesses using data-driven personalization generate 40 percent more revenue than competitors who don’t. Loyalty analytics is a primary driver of that advantage. Here are 5 practical ways to turn your program data into stronger customer behavior.
Pinpoint your second-purchase drop-off
Most customers leave after one order. Pull purchase timing data for buyers who placed 3 or more orders. Find the median gap between their first and second purchase. Build targeted marketing campaigns around that exact window. Reaching customers before they drift is more effective than sending blanket emails afterward.
Measure engagement depth
High spenders who ignore program mechanics are not truly loyal. Segment your members by missions completed, rewards redeemed, and tier progression. Research consistently shows that engaged members deliver stronger customer lifetime value than passive high-spenders. Behavioral engagement, including social media interactions across key social media platforms, sustains relationships between purchases.
Intervene before churn becomes permanent
Every customer segment has a predictable drop-off point. Once you identify it, act before the problem gets worse. Create a well-timed, segment-specific campaign based on the mechanic that segment already responds to. Timing and relevance matter more than the size of the incentive.
Let purchase data define your tier structure
Don’t set tier thresholds arbitrarily. Look at where spending naturally accelerates in your data. If AOV climbs noticeably at $300 and again at $700, build your tiers around those existing behavior shifts. Thresholds placed just below natural spending jumps feel achievable.
Break down performance by individual mechanic
If you measure your entire program as one composite number, you cannot see what’s truly working. Separate your point earners from your mission completers. Compare their 90-day retention curves, using purpose-made loyalty program analytics software. That single comparison reveals whether your behavioral mechanics add lift or simply reward customers who were already loyal.
eSIM Plus is a global mobile data provider, not a Shopify store, but the measurement approach applies directly to retail loyalty analytics. When eSIM Plus shifted from static discount tiers to mission-driven, gamified engagement with Enable3, they tracked repeat purchase rate, average order value, and paid user growth as distinct signals rather than measuring the program as a single composite metric.
The result was a 28.8% increase in repeat purchases, a 7.9% lift in average order value, and 56.2% growth in average paid users, all measured against the pre-program baseline. All details are available in the eSIM Plus case study.
What to Look for in Loyalty Program Analytics Software for Shopify
When you choose loyalty program analytics software for your Shopify store, look for tools that deliver clear engagement metrics and make sure the platform meets the following criteria.
Transaction data flows directly from Shopify.
Member vs. non-member segmentation works as a built-in filter.
All program events appear as timestamped entries alongside purchase data.
Retention data breaks down by enrollment month.
Campaign results for a specific segment are viewable in isolation.
Loyalty events feed directly and automatically into your existing analytics stack.
Revenue attribution links specific program mechanics to actual purchase revenue.
Point liability and redemption rates are visible in the dashboard.
Data refreshes automatically at predefined intervals.
See Enable3's Shopify Loyalty Analytics
Our platform is design to show your loyalty program's ROI.
FAQ
Does Shopify have built-in loyalty analytics?
Yes, Shopify provides sellers with basic analytics tools. Using them, you can measure returning customer rate, AOV, sales by customer, and some other general metrics. Still, there are no advanced features to track and evaluate customer loyalty analytics numbers that affect your relationships with returning clients. You need a dedicated solution to calculate active member rate, redemption rate, points liability, mission completion, and member vs. non-member revenue attribution, etc.
What repeat purchase rate should I aim for on Shopify?
It depends on the category. 40-60% is a good range for consumables like skincare and supplements. Apparel and accessories range from 20-35%. Low-frequency categories like furniture or large appliances can sit below 15% without triggering any concerns. When measuring your repeat purchase rate, do not chase global benchmarks. Concentrate on the number for your specific niche.
How fast can I see results from a loyalty program?
Early indicators, namely, enrollment trends, mission completion rates, and initial redemption patterns, appear within 2-3 months. Statistically meaningful shifts in repeat purchase rate and CLV usually need 6 to 12 months of data to read confidently. Typically, customer loyalty programs that involve mission mechanics and streaks work better than points-only counterparts. Customers use them more enthusiastically because they get rewards only if they make a purchase within a predefined time.
Are customer retention rate and repeat purchase rate the same?
Retention rate tracks the share of customers you keep over a defined period regardless of how many times they buy. Repeat purchase rate tracks the share of buyers who made multiple purchases within a given period. A customer can technically be “retained” without ever transacting again. Using customer segmentation to break your base into distinct customer groups is a proven way to see which cohorts drive each metric. Both metrics matter in customer loyalty analytics retail reports. Retention gives you the broad view of customer persistence, and repeat purchase rate tells you whether customers are actively engaging.
How do you calculate loyalty program ROI?
There is a straightforward formula: Loyalty program ROI = (Incremental revenue from the program - Total program costs) / Total program costs x 100
A positive ROI confirms that your program is financially sound because the revenue goes beyond your investment.
What is points liability in a loyalty program?
Points liability is the total financial value of points issued but not yet redeemed. Every outstanding point is a future reward you’ve committed to. Monitor it as part of your regular customer loyalty program analytics routine to catch financial issues before they strain your budget.
Does Shopify analytics require coding?
Shopify analytics is generally user-friendly. The built-in dashboard lets you track sales, traffic, and customer behavior in several clicks. Still, to level up your marketing efforts, you may want to connect third-party tools. For example, APIs from Enable3 will come in handy for more meticulous tracking tasks. However, most extensions require basic coding knowledge.
