Up to a third of purchases will result in a return. That means about 30% of your sales are likely to exit the company as refunds or exchanges. Considering ecommerce returns data when calculating value of a customer informs the profit you can expect, allows you to allocate marketing spend to acquire a new customer, and helps you design campaigns to win back customers who make a return.
In a retail setting, calculating what a customer is worth, better know as Customer Lifetime Value (CLV), isn't simple.
Shopify defines CLV as "profit associated with a particular customer relationship, which should guide how much you are willing to invest to maintain that relationship."
As this explanation suggests, investments in ecommerce retention marketing tactics are then shaped by the subsequent value a retailer can anticipate from existing consumers.
But how do you know how much money you can count on from each customer?
Unfortunately, determining a customer’s worth in an ecommerce environment is a lot easier said than done.
As statistician George E.P. Box put it, "All models are wrong, but some are useful". And similarly, any estimation of the value of a customer will be wrong. Each comes with its set of assumptions and expectations.
Churn is impractical to conclusively articulate in non-contractual setting like retail. Popular subscription companies like Netflix, Spotify, and even Dollar Shave Club will have an easier time calculating what monetary contribution they can expect from existing customers since they can reasonably forecast payments, and easily observe churn.
But for the ecommerce retailer, the issue of churn greatly complicates the process of projecting future spend of current customers. And more straight-forward historical metrics ignore any future activity, essentially assuming all current customers are done buying.
CLV shapes your retention strategies, but retention partly determines CLV.
The predicted lifetime value of a customer is not a hard and fast metric.
It is pliant because it relies on predictive elements, such as churn, which can be deceiving in a retail environment.
Another way to put it is that you don't lose weight by obsessing over the number that reads on the scale. You lose weight by focusing on the inputs of diet and outputs of exercise. And by targeting these areas, the number that reads on the scale will improve in time.
So while it may seem intuitive, CLV is not be ideal for evaluating Customer Acquisition Costs CAC).
In fact, weighing the average money retained from a customer against CAC may itself be a misleading practice, since it ignores the subsequent costs associated with brand building, service, and - as Shopify stated - maintaining that relationship.
The simplest benchmark of customer value is the average value of a purchase. This is also the most punitive approach to measuring the financial impact of customer acquisition as it assumes that people buy once – or that acquisition costs must apply to each subsequent order.
However, given high costs to acquire a customer, cost of goods sold, overhead, shipping fees, and the potential for returned products, the true profit a retailer retains from a first purchase may be slim, if any.
It would also be misleading to use the monetary contribution of long-time purchasers. Looking at the value customers contribute over time paints a better picture of what they can spend, but not necessarily what they are likely to spend.
To most accurately understand what we can afford to spend to acquire on a customer, we must understand what a new customer is likely to spend with a retailer.
Knowing this, retailers will be able to invest smarter, balancing acquisition and retention efforts to maximize profits. Acquisition and retention efforts must work in unison to grow a loyal, active customer base.
So then how can we estimate the value of a new customer? We could look at the average monetary contribution.
This effectively tells us the average dollar amount a retailer has retained per customer. It is a great assessment of the value customers have brought to a company, but does not incorporate potential future spend for current customers.
Instead, we can try to estimate the expected monetary value of a new customer by looking at how many transactions occurred at each stage in the purchase sequence and scaling this to approximate the percentage of new customers that will reach any given stage in the customer journey.
And we can multiply this by the expected monetary value of an order at each stage to approximate the value that a customer is likely to contribute along the purchase sequence.
We see that the lion’s share of the expected monetary value of a customer comes from the first purchase, confirming that we can never take additional purchases for granted.
This method will not anticipate all future activity, but estimates the likelihood of additional purchases from customers based on what customers have done in the past.
The greatest benefit of this approach is that it extends beyond simply what has happened, but does not rely on any rigid assumption of churn.
Instead, it incorporates what is reasonably likely to happen given what has already happened. It is a relatively intuitive metric that will evolve with the retailer as more transactions occur and thus more data is implemented.
We can then make a rolling sum of expected monetary contributions to estimate the total expected value for a certain stage of the customer journey.
But even more telling is when we compare what we can expect to have received by each purchase with what we have received from each purchase for those who continued to that point.
While this retailer has retained on average $1,893 per customer for those who made 10 purchases, they can only expect to receive around $560 by a hypothetical 10th purchase, since a mere 7% of even the longest-existing buyers have continued to a tenth purchase.
A 10th purchase seems almost unattainable to begin with – of course not everyone makes it to 10 purchases. But even at the 3rd purchase, the average monetary value retained per customer of $582 far exceeds the expected monetary contribution of $327 by the 3rd purchase.
Retailers retain more money from multi-purchase consumers, but not all buyers make multiple orders. In fact, the majority have not in many cases. So driving repeat purchases is critical to boosting profit.
But of course, most retailers are forced to make difficult tradeoffs in the allocation of marketing spend, with high costs of marketing and constant pressure to expand. In this environment most retailers choose to invest in top of the funnel marketing activities.
Retention marketing can be an easy victim in this process. Once a prospective customer makes a purchase, it can be tempting to count them as a customer and only rely on your ecommerce email marketing tool stack to keep them engaged.
We have heard from fashion apparel retailers that customers are not likely to buy in the time immediately following a purchase, so there is no need to spend more money to reengage them quickly.
But the data tells a different story. The graph below depicts the percentage of repurchases that have occurred on any specific number of days since the previous purchase for one of our clients.
This demonstrates that the most common time for a repurchase is actually within a day of the original purchase, there is roughly a decay in the following days.
This then allows us to see the percentage of repurchases that occurred after any number of days since the original purchase, which can be found below. We observe that the percentage declines most dramatically in the days immediately following.
As an example, only about 32% of repurchases occurred 60 or more days after the preceding purchase for this retailer. And by the way, only 22.5% of orders saw a repurchase 30 or more days after the transaction.
Once a transaction occurs, the clock to reengage is ticking.
Another powerful result of this analysis is that we can then visualize the percentage of repurchases that occurred within a certain number of days of the preliminary purchase. And with this, we can say that 45% of repurchases for this brand occurred within 3 weeks.
All the information highlights the significance of reengaging customers fast. Even if they do not buy again right away, it is important to stay top of mind.
Seemingly, repurchase behavior and CLV are tangential to a Shopify returns management company. But anywhere between 20% and 50% of customers for a typical retailer have completed a return. It is safe to say that this is a standard part of the customer journey – and one that is typically just written off from the perspective of marketing.
Fundamentally, returns will never be eliminated. Rather, they're a natural process that you should seek to optimize.
And yet sadly, most retailers have no plan to reengage these hard won customers during a natural phase of the ecommerce customer lifecycle.
Just like all other customers, retailers must continue to engage customers who return a product. By looking at first purchases that had a return associated with it, we can begin to investigate the impact of a return in a new customer’s relationship with a brand.
A returned first purchase does not seem to have a dramatic impact on the occurrence of repurchases in this case. Actually, for this particular retailer, it has increased the occurrence of a second purchase (partially due to exchanges).
But this effect dissipates by the 4th purchase, in which a greater percentage of those who did not return their first purchase continued to buy.
Additionally, a return in the first purchase tends to stunt the expected monetary contribution of those customers. For the retailer we have been examining, the anticipated monetary value of $331 for a customer whose first purchase resulted in a return falls below that of $417 for customers whose first purchase did not.
This is not to say that the value of these customers will never catch up, but it currently has not even with more than a year of time.
For another retailer, the repurchase rate after a return in the first purchase never matched that for customers who did not make a return from their first purchase.
This case illustrates that returns in a customer’s first purchase can have an appreciable impact on their lifecycle, and thus the monetary value a retailer can expect to gain. It all comes back to returns optimization.
Without the right strategies and touch points, the investment in acquiring these customers may not be recouped.
We can also look at any order, at any stage of that customer’s purchase journey - we can see if the order has an associated product return, and whether they purchased after or not. This will effectively approximate the impact that a return has on repurchasing.
We see that, even among purchases made at least 3 months ago, slightly less than 50% have seen a repurchase.
Surprisingly, purchases with returns in this time frame actually exhibit a higher rate of repurchase than do other purchases – though this is heavily inflated by exchanges, which inherently consist of a following purchase.
Exchanges have a near perfect rate of repurchase at 98.24%, while refunds have a repurchase rate of almost exactly 50%. And returns for store credit, although they account for only around 100 cases, had a rate of repurchase of 81.18%.
Even deeper than return types (exchange, refund, or credit), we can further examine repurchase rate by return reasons. This allows us to get a better perspective of the customer’s sentiment when deciding to return an item.
We see that reasons related to sizing (ie “Too Big”, “Too Small”), have far less of an adverse impact on the occurrence of a following purchase – slightly more than half of refunds with such reasons saw a repurchase.
Jake Pasini, Senior Director of Strategy Services at Listrak, explains:
“People who honestly select ‘too big’ or ‘too small’ are probably very interested in the item but just literally need to get a different size. Selecting ‘too big’ or ‘too small’ implies future purchase intent whereas ‘didn’t match description’ does not – at least not to the same degree."
Product quality, on the other hand, seems to have a much stronger impact on repurchases. Almost two-thirds of returns caused by quality issues have not seen a purchase since.
For another retailer, “Just Didn’t Like It” and “Product Didn’t Match Description” both proved to jeopardize the customer relationship, as well.
Whereas 73% of returns caused by sizing for this retailer have seen purchases since, less than 25% of returns for “Just Didn’t Like It” and 33% of returns for “Product Didn’t Match Description” have had repurchases.
Not even product damages or defects impact the rate of repurchase to this extent. Just over 80% of returns due to defects have had a subsequent purchase. Though we may not be able to speak for customers, we hypothesize that this phenomena is driven by the impact the return has on the customer’s trust.
Sizing issues are standard with clothing, and even damage or defects are likely to happen at some capacity. But they do not reflect on the brand as would the quality of the product and the transparency of the product description.
Elysse Ciccone and Shannon Atwell of Noticed, support this idea,
“Inconsistent measurements across brands help prime customers to forgive a mis-sized garment or two. But when your products don't meet a customer's expectations, you're not just winning back their business - you're winning back their trust."
And because of this, it is imperative to reengage the shopper who made the return - not just to save the purchase, but to save the relationship.
Elysse and Shannon state, “When it comes to rebuilding trust, hands-on customer service is the most effective way to communicate. Personalized outreach goes a long way to show customers that you care."
Next, you can look to address the underlying reasons for returns. Mr. Pasini articulates:
“If a statistically significant portion of reasons come back as ‘didn’t match description’ for a particular item, then that information needs to be reviewed by merchandising as there could actually be an issue.
The merchandising and UX teams need to know about ‘too small/big’ because improvements to product pages, content, or the sizing itself could be needed.”
Heavily edited photos and inconsistent descriptions across channels can be common culprits. Elysse and Shannon recommend actions shots, videos, and consistent copy to mitigate confusion and better align shopper expectations.
The biggest mistake a retailer can make is assuming that customer behavior is fixed. Repurchases are not guaranteed, and acquisition does not stop at the first purchase. As Michael Schrage stated, “The best investment you can make in measuring customer lifetime value is to make sure you’re investing in your customers’ lifetime value.”
We will be the first to acknowledge that we may never be able to definitively articulate the true value of a customer.
However, by understanding where value is coming from in the customer journey - or perhaps more importantly - where it is not coming from, retailers can make informed reinforcements to their marketing processes.
According to Jake Pasini, “The key to any of these efforts is timing: they should be as soon to the return event as possible, every day and every hour will decrease the likelihood to purchase.”
The objective is growing a customer base that is more connected to the brand, with less drop-off between purchases. At least in part, the value of a customer is up to the retailer.
Retention marketing is an enormous opportunity to efficiently grow revenue, and the path to repurchase begins at the preceding purchase.
ReturnLogic partners with retailers to help them optimize their businesses. This does not stop at improving the returns process or reducing return rate. We strive to advance retailers at any stage of business maturation.
Through this analysis, we can find where in the customer journey value is being retained, where it is lost, and identify points of natural activity through deep analysis into the customer journey in order to fuel retention marketing efforts.