Customer retention refers to your ability to keep customers coming back to your store over time. In ecommerce, retention is just as important as acquisition – if not more – because repeat customers drive sustainable growth.
Retained customers tend to spend more and cost less: for example, returning shoppers spend 67% more on average than first-timers. Whereas acquiring new customers can be expensive – about 5x the cost of retaining an existing one.
It’s no surprise that even a small improvement in retention can have big payoffs (in one analysis, a mere 5% increase in retention boosted profits by 25–95%).
To improve customer retention, you have to be able to measure it. And that’s what we’re talking about in this guide.
Customer retention rate (CRR)
What it is
Customer retention rate is the percentage of customers your business retains over a given time period. In simple terms, it tells you how many of your customers stick around and continue to buy, as opposed to leaving (“churning”).
It’s a broad indicator of loyalty and customer satisfaction. A high retention rate means a large share of customers keep coming back; a low rate means you’re losing customers faster than you’d probablylike.
How to calculate it
To figure retention rate, pick a time frame (e.g. a quarter or year) and identify three numbers:
- S = the number of customers you had at the start
- E = the number of customers you had at the end
- N = the number of new customers acquired during that period.
Plug these numbers into the following formula:
Customer Retention Rate = E−N / S × 100.
For example, if you started the quarter with 200 customers and ended with 250, having acquired 70 new customers in that time, your retention rate would be ((250 – 70) / 200) × 100 = 90%. This means 90% of your starting customers stayed with you during the quarter, a very strong retention rate.
What “good” looks like
Retention rates vary widely, but in ecommerce they are often lower than many other industries.
One study found the average retention rate in ecommerce is around 30%, reflecting the challenge of getting one-time shoppers to return.
However, with deliberate retention efforts, many brands achieve 60%+ retention of their customers. In fact, product-based companies average about 63% retention.
In general, if your annual retention rate is much below 30%, it’s a sign you need to improve, whereas pushing it into the 50–60% range or higher puts you among the top performers.
How to act on it
Improving retention rate means increasing the likelihood that each customer will buy again. This typically requires excelling in customer experience and loyalty incentives.
Focus on making every interaction great, from product quality to website experience to customer support.
Consider investing in loyalty programs, personalized offers, and repeat purchase discounts to entice customers back.
Little things can have big effects: for example, fast, easy checkout and good follow-up service encourage people to stick with your store.
Repeat purchase rate (RPR)
What it is
Repeat purchase rate – also called repeat customer rate or returning customer rate – is the percentage of your customers who have made more than one purchase. Essentially, it answers: out of all customers who bought from you, how many came back for another purchase?
This metric directly measures customer loyalty in a retail context (especially for non-subscription businesses). A higher RPR means a larger share of customers didn’t just buy once – they liked your product/store enough to return.
How to calculate it
It’s straightforward: in a given period, take the number of customers who are “returning” (they’ve made a purchase before) and divide by the total number of customers who made a purchase in that period, then multiply by 100.
For example, if 1,000 customers bought something in the last month and 500 of them had purchased from you before, your repeat purchase rate is 500 / 1,000 = 50%. In formula form, we can describe it as:
R = number of returning customers
T = total number of customers
And then the repeat purchase rate is R / T * 100
What it tells you
RPR gives insight into customer loyalty and the effectiveness of your retention efforts. If your RPR is 10%, that means 90% of customers only bought once, which could indicate lots of one-off bargain hunters or issues preventing repeat business.
If your RPR is, say, 40%, that’s a strong signal that customers are sticking around and finding value in repeat purchases. This metric is particularly useful for ecommerce stores that don’t offer a subscription product, because these stores can’t just assume a customer will be back.
What “good” looks like
Benchmarks for repeat purchase rate vary by industry and product type, but most ecommerce businesses see around 25–30% of customers coming back for more. In other words, roughly a quarter of customers tend to be repeat purchasers for a typical store.
Some categories naturally see higher repeat rates. Consumables like pet food or groceries can have repeat customer rates well above 30% (since customers need to restock frequently).
By contrast, industries like luxury furniture or high-end electronics see lower repeat rates because purchases are infrequent.
Overall, an RPR in the 20–40% range is healthy for many ecommerce brands. If you’re below 20%, you’re likely missing opportunities to retain customers. If you’re approaching 50% or more, that means you have a very loyal customer base (and you might shift some focus to acquiring new customers to supplement that loyalty).
It’s also useful to consider repeat rate in context: often, getting a customer to make that second purchase is the biggest hurdle. Industry analyses have shown that after a customer’s first purchase, there may be only about a 27% chance they’ll return at all – but if you can get them to make a second and third purchase, their likelihood of coming back again jumps to 54% or higher.
How to act on it
To boost your repeat purchase rate, focus on converting one-time buyers into repeat buyers.
Tactically, this might include:
- engaging one-time customers with email campaigns or retargeting ads offering a reason to come back (like a personalized discount or relevant product recommendations)
- providing exceptional post-purchase support (so that first-time buyer is delighted and trusts your brand)
- implementing a loyalty or rewards program that incentivizes the next purchase
Keep an eye on your RPR over time and by cohort (e.g. customers acquired during a holiday promotion vs. regular customers) to see how changes you make to your retention marketing affect the RPR. The goal is to steadily raise that percentage of customers who come back, as even a few points’ increase in RPR can significantly lift your revenue.
Purchase frequency
What it is: Purchase frequency measures how often an average customer makes a purchase in a given time period. In other words, among your customer base, how many orders does each customer place on average per month, per quarter, or per year?
This metric complements the repeat purchase rate: RPR tells you what percentage of customers are repeats, while purchase frequency tells you how often your repeat customers buy.
A higher purchase frequency means customers are ordering from you more frequently (e.g. the same customer buys monthly rather than just twice a year).
How to calculate it
A simple way to calculate purchase frequency for a period is:
Purchase Frequency = Total Orders in Period / Total Unique Customers in Period
For example, suppose over the last quarter you had 500 orders placed by 350 unique customers. Purchase frequency = 500 / 350 ≈ 1.43.
That means, on average, each customer made 1.43 orders in the quarter (or roughly one and a half purchases each). You can also calculate it on an annual basis (total orders per year divided by total customers that year) to get the average number of orders per customer per year.
What it tells you
Purchase frequency indicates the engagement level of your customer base. A higher number means customers are purchasing more often (which could mean they rely on your products regularly, or that you’re doing well in prompting repeat sales).
A lower number means most customers aren’t coming back often.
This metric can reveal purchasing patterns. For instance, you might find your average customer orders 3 times a year, which might highlight seasonal behavior or the lifecycle of your product.
It can also help in forecasting and planning inventory, marketing cadences, etc. If you know customers typically order every 90 days, you might schedule your retention marketing around that cycle.
What “good” looks like
Purchase frequency varies by product type. Some industries naturally lend themselves to frequent purchases (e.g. daily consumables, food, personal care items), while others are infrequent (e.g. mattresses or high-end electronics).
That said, across ecommerce as a whole, studies have found a surprisingly consistent pattern: loyal customers tend to buy on the order of 3 to 4 times per year on average. One analysis of hundreds of online stores showed that, regardless of industry, loyal shoppers (those who make more than one purchase) averaged about 3.58 purchases per year.
Of course, this is a broad generalization. The best way to use purchase frequency is to compare against your own history and goals. If your average customer bought 1.2 times last year and you manage to increase that to 1.5 times this year, that’s a significant improvement.
How to act on it
To increase purchase frequency, look for ways to encourage more frequent ordering without being pushy. This often comes down to smart marketing and product strategy.
Some approaches include:
- launching new complementary products or new collections regularly (so there’s always something fresh for customers to come back and buy)
- sending well-timed reminders (for products that expire or get used up, remind customers when it might be time to reorder)
- offering subscriptions or bundles for regularly needed items
For example, if you sell coffee beans, you might offer a subscription service to send a new bag every month, converting a one-time sale into a monthly repeat by convenience.
Personalized outreach can help too: analyze the typical interval between purchases for your customers and consider an email or ad campaign just before that interval ends, to prompt the next purchase.
Additionally, ensure that your customer experience remains excellent consistently. If someone had a great first purchase, a smooth second purchase, etc., they’ll be more inclined to keep buying from you frequently.
If you notice some customers buying very infrequently, you might conduct a survey or look at their behavior to understand why. Maybe they only needed your product for a one-time event, or maybe they didn’t realize you offer other items.
Customer churn rate
What it is
Churn rate is essentially the flipside of retention rate: it measures the percentage of customers who stop buying from you (or drop off) in a given time period. If retention rate tells you how many stayed, churn rate tells you how many left.
A customer is considered “churned” if they were active (buying from you) and then, over the period in question, they did not make another purchase (and presumably won’t in the future unless reactivated).
High churn means you’re losing customers quickly; low churn means most customers stick around.
How to calculate it
Churn rate is often calculated in tandem with retention. For a given period, you can calculate churn as:
Churn Rate = (Customers at start − Customers at end) / Customers at start × 100
(Important: when doing this, exclude new customers acquired during the period, as churn is about the loss of existing customers.)
Generally, you can calculate churn for any interval (monthly, quarterly, yearly) depending on your business cycle. Just be sure to define what counts as a “churned” customer in your context (for many ecommerce stores, it could be no purchase in the last X months, with X depending on your typical purchase frequency).
What it tells you
Churn rate is a direct indicator of customer attrition. A high churn rate signals that many customers are not coming back to your store after their initial or recent purchases.
It can be an alarm bell for underlying issues: maybe customers were dissatisfied, or perhaps you attract a lot of one-time bargain shoppers who never intended to be loyal.
Tracking churn helps you quantify the leaky bucket problem: how many customers are you losing, and how fast? For instance, if you have an annual churn rate of 70%, that means out of every 100 customers you had at the start of the year, 70 didn’t purchase again during the year. You’d need to replace them with new customers just to stay even – which is costly and inefficient in the long run.
Low churn, on the other hand, means you are retaining a good chunk of customers, which generally leads to more recurring revenue and higher lifetime value.
What “good” looks like
Naturally, you want churn to be as low as possible. In practice, some churn is unavoidable. Not every customer will become loyal for various reasons.
What counts as a “good” churn rate can depend on your industry and whether you measure it monthly or annually. As a point of reference, a monthly churn rate above ~5% is cause for concern in many businesses.
For example, a 5% monthly churn means over half your customers will churn in a year if that rate persists each month (since churn compounds). One source notes that a 6–8% monthly churn should be considered a red flag that needs immediate attention.
On an annual basis, if your churn is, say, 70%, that corresponds to a 30% retention – which, as noted earlier, is roughly average in ecommerce but certainly has room for improvement.
Best-in-class ecommerce companies that focus on retention might get annual churn down to 40% or 30% (meaning they retain 60–70%).
In subscription models (like subscription boxes or services), churn benchmarks are often looked at monthly and anything in the single digits monthly is decent, while anything above 10% monthly is usually poor.
How to act on it
Reducing churn starts with understanding why customers leave. Common culprits in ecommerce include:
- poor user experience
- unmet expectations
- lack of engagement
- better offers from competitors
To tackle churn, work on thesethings. As with other metrics, providing a great experience, a fast, easy-to-use website, top-class customer service and post-purchase follow-ups can significantly improve things.
Customer lifetime value (CLV)
What it is
Customer lifetime value (CLV) is the total revenue or profit you can expect to earn from a customer over the entire time they do business with you. In plain terms, it’s how much an average customer is “worth” to your store from their first purchase to their last.
CLV takes into account how many purchases a customer makes, how often they purchase, and how much they spend each time. It’s a holistic metric that combines purchase frequency, average order value, and retention period into one number.
How to calculate it
There are different methods to calculate CLV, ranging from simple to very complex. A common simple approach for ecommerce is:
CLV = Average Order Value × Purchase Frequency × Average Customer Lifespan
Here, average order value (AOV) is how much a customer spends on a typical purchase, purchase frequency is how many orders they place per period (often per year), and customer lifespan is how long (in years, for instance) they continue to be an active customer.
For example, if your store’s typical customer spends $50 per order, buys 4 times a year, and continues to shop with you for 3 years on average, then CLV ≈ $50 × 4 × 3 = $600.
This means the average customer brings in $600 in revenue over their lifetime with your business.
You can refine CLV by considering profit rather than revenue (sometimes called customer lifetime profit), or by doing cohort analyses to compute actual observed CLV for groups of customers. But the above formula gives a solid estimate.
(Note: Some formulas break it down further: e.g. Customer Value per year = AOV × purchase frequency, then CLV = Customer Value × lifespan.)
What it tells you
CLV is incredibly important for guiding your marketing and customer retention strategies. It essentially quantifies the payoff of retaining customers.
When you know your CLV, you can judge how much you can spend to acquire a customer (CAC) and still be profitable.
For instance, if your average CLV is $600 (as above), and your profit margins are, say, 30%, then the profit per customer is $180 over their lifetime. You’d want to acquire customers for significantly less than $180 in marketing cost on average to make money.
CLV also helps you identify your best customer segments: often, a small percentage of customers account for a big chunk of your revenue. Many companies will calculate CLV for different segments (by acquisition channel, by customer cohort, etc.) rather than one single number.
If you find, for example, that customers acquired via Instagram have a CLV of $300 while those acquired via email referrals have a CLV of $600, that insight can shape where you focus your efforts.
In short, CLV is a lens to view the long-term value of keeping a customer versus constantly needing new ones.
What “good” looks like
Unlike the percentage metrics we’ve discussed, CLV is an absolute number, and it’s highly specific to your business (and your pricing).
There isn’t a universal “good CLV” number. A high-end retailer might have a CLV in the thousands, whereas a low-cost accessory store might have a CLV of $50.
However, one common way to assess your CLV is by comparing it to your Customer Acquisition Cost (CAC). A classic rule of thumb is that CLV should be about 3 times higher than CAC (or more) for a sustainable business model.
For example, if it costs you $50 to acquire a customer via ads and marketing, that customer should ideally generate $150+ in revenue over their lifetime to be worth it.
Ratios above 3:1 (say 5:1) are even better; a ratio below 1:1 (i.e. you spend more to get a customer than they ever spend with you) is obviously bad. So, “good” CLV is relative: it’s “good” if it comfortably exceeds your acquisition cost and is growing over time as you improve retention.
Also, consider whether you are increasing CLV by improving retention strategies: if your CLV was $300 last year and through loyalty efforts it’s $400 this year, that’s a positive trend.
How to act on it
CLV is like the cumulative report card of all your retention efforts. To increase CLV, you can work on three levers:
- get customers to buy more often
- get them to spend more per order
- keep them as a customer for longer.
Many of the metrics we’re covering feed into CLV. For instance, increasing purchase frequency or improving your repeat purchase rate will naturally raise CLV.
Net Promoter Score (NPS)
What it is
Net Promoter Score is a popular metric for gauging customer loyalty and satisfaction (which ultimately dictates customer retention by asking a simple question: “How likely are you to recommend our company/product to a friend or colleague?”
Customers respond on a 0–10 scale. NPS then classifies responses into Detractors (0–6 rating), Passives (7–8), and Promoters (9–10). Your NPS is derived from the percentage of Promoters minus the percentage of Detractors, yielding a score between -100 and +100. Essentially, it measures the balance of loyal enthusiasts versus unhappy customers.
How to calculate it
After surveying customers with the recommendation question, you compute the percentages in each group. For example, say 50% of respondents are Promoters (9 or 10 out of 10), 30% are Passives (7–8) and 20% are Detractors (0–6). Your NPS would be 50% – 20% = +30. (Passives count toward total respondents but don’t directly affect the score.)
Companies often conduct NPS surveys periodically via email or after customer interactions. The result is one number, e.g. NPS = +30, which you can track over time.
What it tells you
NPS is regarded as an indicator of customer loyalty and propensity to generate positive word-of-mouth. A high NPS means you have more promoters – those who love your brand and will recommend it – and relatively few detractors.
A low or negative NPS means you have a lot of unhappy customers who could be harming your reputation (and likely won’t return). The reason NPS is powerful is that it correlates with future growth: customers who promote your brand will not only come back themselves but bring others along, whereas detractors are at high risk of churning and may dissuade others.
NPS is also a leading indicator for retention/churn. For instance, if a customer gives you a very low score, that’s a red flag, and they might leave if you don’t address their issues. By monitoring NPS and reading the follow-up feedback (customers usually explain why they gave the score), you get qualitative insight into what drives loyalty or dissatisfaction.
What “good” looks like
NPS can vary by industry, but generally a positive NPS is good, and anything above, say, +50 is excellent.
According to some benchmark data, the average NPS across companies is around +32. Ecommerce retail often sees NPS averages in the 40s – one source suggests eCommerce brands average between +40 and +55 NPS, while top performers (think of exceptionally beloved brands like certain pet supply or apparel companies) can score above +70.
In fact, scores in the 70–80+ range are world-class and usually indicate a cult-like customer following. On the flip side, an NPS in the negatives (meaning you have more detractors than promoters) is a glaring sign of trouble; scores in the 0–20 range show room for improvement.
It’s important to compare yourself to your niche. For example, consumer tech might have higher NPS than, say, airlines (which historically often have low NPS). But as a rule of thumb: NPS > 50 is great, ~30–50 is decent/average, and anything below 20 means you should investigate what’s driving customers to be detractors.
How to act on it
The true value of NPS comes from the follow-up question “Why did you give that score?” and then acting on that feedback.
If your NPS isn’t as high as you’d like, look at the common themes in detractors’ comments. Maybe customers are unhappy with shipping times, or they found customer service unhelpful, or the product didn’t meet expectations.
These insights are gold: they highlight exactly what to fix to improve loyalty.
Reach out to detractors individually if possible, because turning a detractor into a satisfied customer can not only prevent churn but might convert them into a promoter. For promoters, see what you’re doing right and do more of it. You can also encourage promoters to spread the word (referral programs, reviews, etc., since they already indicated they’re likely to recommend you).
Many companies set up an NPS “loop” where detractors get immediate follow-up from a support rep to address their concerns, which can boost retention.
Treat NPS not just as a score to monitor but as a feedback mechanism. When you implement changes (better return policy, new customer service training, improved product descriptions – whatever the feedback suggests), you should see NPS rise over time, reflecting growing customer love for your brand.
Keep in mind that improving NPS often is improving retention, since happier customers stick around and unhappy ones leave.
Existing customer revenue growth rate
What it is
This metric looks at how much revenue from your existing customers grows over a certain period. In other words, it measures revenue expansion among the customers you already have (through repeat purchases, upsells, cross-sells), excluding revenue from new customer acquisition.
It’s a powerful way to see if your current customers are increasing their spend with you over time. This is sometimes thought of in subscription businesses as “net revenue retention” or expansion rate, but it applies to ecommerce too. It captures the impact of loyalty, upselling and cross-selling on your revenue.
How to calculate it
Typically, you measure this as a growth rate per period (monthly, quarterly, etc.). The formula is:
Existing Customer Revenue Growth Rate = (Revenue from existing customers (current period) − Revenue from existing customers (prior period)) / Revenue from existing customers (prior period) × 100
For example, suppose last month your store made $4,000 in revenue from repeat customers (those who had bought before), and this month it made $5,000 from repeat customers. The growth rate in existing-customer revenue is $(5,000 – 4,000) / 4,000 × 100% = 25%.
That means revenue from your established customer base grew by 25% month-over-month.
(Note: When calculating, you exclude revenue from brand new customers in both periods to isolate growth driven by the existing customer base.)
What it tells you
This metric answers the question: Are my existing customers spending more with me over time? A positive growth rate indicates that your retention and upselling efforts are paying off. Not only are you keeping customers, but they are increasing their value (buying more frequently, or higher-ticket items, etc.).
It can capture things like customers adding more items to their orders, moving into new product categories you offer, or responding to your cross-sell campaigns.
A negative growth rate (or zero growth) would mean your existing customers are contributing the same or less revenue than before, perhaps due to churn or reduced spending. This could signal that even though you have loyal customers, they’re not expanding their relationship with your brand.
Many businesses strive to have a healthy expansion revenue from existing customers, because it’s usually more cost-effective than acquiring the equivalent revenue from new customers.
What “good” looks like
There aren’t universal benchmarks in percentage form, since this will depend on how mature your business is and your strategies.
Generally, any positive growth in existing-customer revenue is a good sign, because it means your current customers are contributing more and more. If you can consistently get, say, double-digit growth from your existing base (e.g. +10% or +20% per quarter purely from repeats), that’s excellent, because it shows strong loyalty and successful upselling.
If this metric is flat or declining, that’s a warning sign: maybe you are not capitalizing on your customer base’s full potential.
Another way to gauge it: look at the percentage of total revenue coming from repeat customers and see if that is rising. For example, perhaps last year 40% of your sales came from existing customers and this year it’s 50%: that implies good revenue growth among your customer base.
Many thriving ecommerce brands report a majority of their revenue coming from repeat buyers.
How to act on it
To grow revenue from existing customers, think upsell and cross-sell. Upselling means convincing a customer to buy a higher-end product or add-on than they initially might, and cross-selling means getting them to buy additional, related products.
Both techniques will boost the revenue per customer.
For example, if a customer usually buys a basic version of your product, can you develop a premium version that some will upgrade to? Or if you sell, say, cameras, can you cross-sell lenses, bags, and accessories to those camera buyers?
It’s all about expanding wallet share.
Practical steps include:
- using personalized product recommendations (“Customers who bought X also like Y”) on your website and in follow-up emails
- bundling products
- offering volume discounts (buy 3 get 1 free) to increase basket size
- launching new product lines that your existing customers would naturally be interested in
Customer segmentation can help here too: identify your high-value customers and consider a VIP program or exclusive offers to encourage even more spending (e.g., early access to new products, which might entice them to purchase more).
Also, pay attention to customer feedback and requests: if many customers wish you sold a related product that complements your line, adding that could immediately generate more sales from your base.
Another tactic is subscription or reorder programs for consumable products. If you sell something that runs out, get customers on an auto-refill program, which increases their lifetime spend.
Marketing automation can target customers with specific upsell offers based on their purchase history (for instance, a shopper who bought a printer could later get an email offer for a discounted package of ink cartridges).
The key is to provide additional value to the customer, not just squeeze money. If done right, both you and the customer benefit (they get a product that adds value, you get higher revenue).
Keep measuring this metric; if you implement an upsell strategy and see your existing-customer revenue growth jump from, say, 5% to 15% quarter-over-quarter, that’s a clear success.
Customer Satisfaction Score (CSAT)
What it is
Customer Satisfaction Score (CSAT) is a metric that directly asks customers how satisfied they are with a recent interaction or purchase. It’s usually measured via a survey question such as “How would you rate your overall satisfaction with your experience?” and customers respond on a scale (often 1 to 5, where 5 is “very satisfied”).
CSAT is expressed as a percentage of respondents who are satisfied. It’s more granular and immediate than NPS, focusing on current satisfaction with a specific transaction or support interaction, rather than overall loyalty.
How to calculate it
Typically, after a purchase is delivered or after a customer support ticket is resolved, you might send a CSAT survey. If using a 1–5 scale, you might count 4s and 5s as “satisfied” and calculate the percentage of respondents who gave those top scores.
For example, if 200 customers responded to a survey and 160 of them gave a 4 or 5, then your CSAT would be 160/200 = 80% (meaning 80% of customers were satisfied with that experience).
Some companies also average the score (e.g., an average of 4.2 out of 5), but the percentage satisfied is common for benchmarking. The key is consistency in how you measure (and typically focusing on the high-end responses as “satisfied”).
CSAT can also be asked as a yes/no (“Were you satisfied with your experience? Yes or No”), in which case it’s simply the percentage who said “Yes.”
What it tells you
CSAT provides a direct pulse on customer happiness at specific touchpoints. It’s an important retention metric because if customers aren’t satisfied, they are unlikely to come back. By monitoring CSAT, you can identify issues in your customer experience. For example, if your post-purchase CSAT drops after you change a shipping provider, it may indicate problems with delivery times or package condition.
Or if your CSAT for a customer support interaction is low, it signals your service didn’t meet expectations.
High CSAT scores, on the other hand, mean you are meeting or exceeding customer expectations. CSAT is often considered a leading indicator for churn as well: consistently low satisfaction will eventually show up as lost customers churn.
Another valuable aspect of CSAT is that you can measure it at many stages: website experience, checkout process, product satisfaction, customer service, etc. This helps pinpoint where in the journey you might need improvements.
What “good” looks like
CSAT tends to be fairly high for businesses that are doing well, because truly dissatisfied customers often are a minority (but a very important minority to pay attention to).
In ecommerce, most companies see CSAT in the range of 75% to 85% satisfied. According to industry benchmarks, an 80% CSAT (meaning 4 out of 5 customers on average are satisfied) is a solid goal for retail and ecommerce.
Scores above 90% are exemplary and indicate you’re delighting customers at an exceptional level.
If your CSAT is dipping into the 60s or below, that is a sign of significant issues: very few companies would consider that acceptable for long-term success.
It’s also useful to compare against your own performance over time: if you were at 85% last quarter and 78% this quarter, something may have gone wrong that needs correction.
Additionally, CSAT can vary by channel: for instance, your CSAT might be 90% for in-store pickup experience but 70% for online delivery, which tells you where to focus.
How to act on it
Use CSAT as an early warning system and a continuous improvement guide. Whenever you collect CSAT, pair it with a follow-up question asking why the customer gave that rating (similar to NPS feedback).
If customers indicate they’re not satisfied, find out the reasons: Was the product not as expected? Was shipping slow? Did they have an issue with support? Then take concrete actions based on those insights.
Product return rate
What it is
Product return rate is the percentage of products (or orders) that customers return after purchase. It’s a metric that reflects how often customers are unhappy with a product to the point of sending it back.
Return rate is especially crucial in ecommerce because customers can’t physically inspect items before buying, so returns are common and costly. A high return rate might indicate issues like product quality problems, misleading descriptions, poor fit (for apparel), or simply customer buying habits (like “bracketing” – ordering multiple variants with the intention to return some).
Monitoring return rate can reveal problems in merchandising and set expectations for revenue that might be lost to refunds.
How to calculate it
Return rate can be calculated as:
Return Rate = Number of units (or orders) returned / Number of units (or orders) sold × 100
For instance, if you sold 1,000 products this month and 150 of those were returned by customers, your product return rate is 150/1000 = 15%.
You can calculate by units or by orders (sometimes one order contains multiple items, which may or may not all be returned), just be clear in your definition: many retailers track “item return rate” as a percentage of items sold.
It’s also useful to track return rate by category or product type because it can vary greatly (e.g., fashion tends to have higher returns than electronics).
What it tells you
Return rate is a measure of customer satisfaction and expectation management. Customers usually return items if they are dissatisfied: perhaps the item didn’t fit, didn’t match the description or images, arrived defective, or they simply changed their mind.
A high return rate can highlight mismatches between customer expectations and what you delivered. For example, if a dress is consistently returned due to size issues, maybe the sizing chart or product description is off. If a gadget has a high return rate, maybe it’s too complex or not meeting its advertised claims.
Return rate also has a direct financial impact: processing returns costs money (shipping, handling, restocking) and refunded sales hit your revenue. So lower return rates are generally better for both customer happiness and your profitability (as long as they’re not low because customers are too discouraged to bother returning, but this is usually not the case if the return process is easy).
What “good” looks like
Return rates vary widely by industry. The average ecommerce return rate is around 20–30%, which is significantly higher than brick-and-mortar retail (where customers see items before buying).
Within that, fashion and footwear are on the higher end. For example, online apparel purchases have an average return rate roughly in the mid-20s. Some fashion sub-categories or brands even see return rates of 40% or more, especially if customers order multiple sizes or colors to try (a behavior encouraged by free returns).
Electronics might have return rates in the 10–15% range on average, and categories like beauty or consumables often have quite low return rates (since you can’t return opened cosmetics in many cases, etc., and if people consume the product there’s no return).
If your overall return rate is, say, 10%, that’s very good for ecommerce. If your return rate is 50%, that’s a big red flag: it means half of what you sell gets sent back, which is unsustainable.
Most healthy ecommerce businesses aim to keep return rates as low as feasible while still having a customer-friendly policy.
Also watch changes in return rate: if it spikes after a product launch or a change in description, something might be wrong.
How to act on it
Start by diagnosing why customers are returning items. The information is often available via return reasons (customers might fill out a form or survey when returning).
Products are often returned because the item:
- didn’t fit
- was not as described or expected
- arrived damaged
If fit issues are causing returns (common in fashion), consider improving your sizing guides, providing more measurements, using virtual try-on tech, or featuring reviews where customers mention fit (“Runs large/small”). Including multiple photos – like on different models for clothing – can set correct expectations.
If “item not as described” is a frequent reason, that flags a need to improve product descriptions, images, and perhaps videos. Make sure color accuracy is good in photos, list materials, dimensions, and any quirks of the product clearly.
For “damaged on arrival”, you may need to beef up packaging or switch carriers.
Analyze if certain products have much higher return rates. It might be wise to discontinue or re-engineer products that are returned at very high rates, as they may not be suitable for online sale without modification.
But don’t ditch the customer-friendly return policy (easy returns, preferably free return shipping if you can afford it). This might seem counterintuitive to reducing returns, but a good return policy builds trust and can actually encourage more sales (customers buy knowing they can return).
Engage with serial returners carefully – sometimes personalized help can reduce their need to return (e.g., suggest the right product variant for them up front).
Also, monitor your return rate after changes: if you implement a new sizing chart and return rate for that category drops from 30% to 20%, that’s a huge win.
Lastly, remember returns aren’t just a cost, they’re an opportunity: a customer who returns something isn’t automatically lost. If you handle the return smoothly and maybe suggest an alternative product, you can still salvage the relationship.
A seamless, no-hassle return process can actually increase the likelihood that a customer gives you another chance, which is a retention play in itself.
Companies that excel in retention often turn returns into exchanges or store credits, keeping customers engaged. So, use return rate data to improve product info and quality upfront, and ensure your after-sale processes (returns handling, exchanges) keep customer satisfaction high.
The goal is to minimize unnecessary returns and make necessary returns as painless as possible. Both will improve customer retention and your bottom line.
Retention dashboards bring all these metrics together
Tracking each of these metrics on its own provides value, but the real magic comes from looking at them together to get a complete picture of customer retention. That’s where retention dashboards and cohort analysis come in.
A retention dashboard is a centralized view (often a live report or a set of charts) showing key retention KPIs side by side: for example, your retention rate, repeat purchase rate, CLV, and NPS all in one place, usually tracked over time.
By seeing these metrics together, you can spot correlations and causations. For instance, you might notice that as your repeat purchase rate went up over the last 6 months, your revenue from existing customers also grew (no surprise there), or that a dip in CSAT last quarter was followed by a rise in churn rate. Having them on a dashboard helps turn raw data into actionable insight.
One powerful approach in retention analytics is using cohort analysis, which groups customers by their start period (or other traits) and tracks their behavior over time.
For example, you might create monthly cohorts based on the month a customer made their first purchase (Jan cohort, Feb cohort, etc.), and then see what percentage of each cohort made a repeat purchase in 1 month, 3 months, 6 months, etc. The results are often visualized as a retention cohort table (often color-coded as a heatmap).
Each row is a cohort (e.g., January’s new customers, February’s new customers), and each column is the percentage of that cohort still purchasing after X months. This kind of chart instantly shows if later cohorts are performing better (higher retention) than earlier ones – which tells you if your retention strategies are improving.
It also reveals typical drop-off patterns. You might notice that across cohorts you retain 50% into month 2, 30% into month 3, and then it plateaus, and that insight can direct you to intervene earlier to retain more of that 50% beyond month 2.
Cohort analysis is widely regarded as one of the most insightful methods for analyzing retention because it accounts for the fact that newer customers haven’t had as long to repeatedly purchase as older customers. Instead of lumping everyone together, you compare apples to apples (customers who have been with you for X time).
There are many tools that can help build these dashboards. If you’re on Shopify, you have some built-in analytics and now even a cohort analysis report in Shopify’s analytics dashboard (introduced in late 2022). For more advanced needs, consider third-party retention analytics apps like LoyaltyLion.
Even outside of specialized apps, you can build retention dashboards using general business insight (BI) tools. Google Data Studio (Looker Studio), for instance, can pull data from your store and visualize it in custom charts. With a bit of data work, you could create a dashboard showing month-by-month active customers, new vs. repeat revenue, churn rate, etc.
Measuring customer retention is the first step to improving it — but it’s what you do with the metrics that really counts
Tracking these customer retention metrics is immensely valuable, but the metrics alone won’t boost your business. It’s what you do with them that counts. Think of metrics as a flashlight – they illuminate where things are going well or poorly, so you know where to focus your energy.
The final (and most important) step in a retention program is to close the loop: analyze the metrics, gain insights, implement improvements, and then monitor the metrics again to see the effect.
Avoid the trap of chasing a number for its own sake, and focus on the customer experience underlying the metrics. For instance, raising NPS isn’t about pleading with customers for higher scores; it’s about genuinely delivering a better experience that naturally makes them happier to recommend you. Lowering churn isn’t about some gimmick to lock in customers; it’s about understanding why they leave and giving them reasons to stay.
When you act on metrics in good faith to improve customer outcomes, the scores will follow.
