Since the dawn of the internet, online fashion and apparel retailers have been locked in a fierce and seemingly never ending battle against one of the biggest problems plaguing the E-Commerce industry—product returns.
Unlike brick and mortar counterparts, E-Commerce retailers don’t have the advantage of product look and feel that a fitting room conveniently provides.
Those pants were too tight around the calf? No problem—check out a different cut.
You wear medium, and that shirt was too loose? Try sizing down.
You purchased a dress online and it didn’t fit quite right? Sorry, guess you’ll have to return it.
Unfortunately, this scenario is all too familiar to E-Commerce retailers, who— despite their best efforts struggle with the friction and resource intensive nature of product returns everyday.
To deal with the pain caused by returns many smart retailers are relying on Shopify returns management software to automate and streamline their return process—which, from a logistics and operations perspective can save countless headaches and man hours.
This time savings, while an essential part of an effective returns management strategy, fails to provide retailers with the insights they need to make effective business decisions and answer questions like:
Regardless of your vertical or industry, customer feedback is an important mechanism in the decision making loop of a business—and in E-Commerce, it can make or break your store.
Product returns, while a major time cost, can act as a valuable customer feedback engine that provides key insights into your products and target market.
It’s important to realize that shoppers return products because they’re disappointed, whether it maybe with the shopping experience, or the product itself.
Retailers can capitalize on this negative, and use it as an opportunity to collect customer feedback to learn more about their products, and what they can do to design and present them to reduce returns.
Retailers who think they are streamlining their return process by putting a return shipping label in a box or setting up their return policy to allow customers to send an item back without contacting customer support are missing out on a gold mine of actionable insights.
At the very least, you should be collecting general return reason codes for each return you authorize.
Collecting and aggregating return reason codes will provide you with a quantitative overview of why your products are being returned.
While return reason codes like “Too big” or “Defective item” are still general insights, they can serve as identifiers for potential areas of improvement—whether it’s fixing product sizing charts or changing inefficiencies and errors in your supply chain.
You can then prioritize areas of potential improvement based on the distribution of return reasons.
For instance, you may choose to investigate why shoppers are returning a product because it’s “Too Small” since it contributes to your overall return volume at a higher rate than products being returned because they were “Not As Pictured.”
Conversely, if you understand that your products generally have a tighter fit and run small—you may want focus on exploring another return reason code such as “Not as Pictured”, which may have a lower frequency, but can be fixed quicker than a redesign of your product line to improve quality and fit.
Return reason codes provide a big picture view, which is helpful for when you’re looking for trends and visualizations, but not when you need to dig deeper and understand specific problems associated with each return reason code.
When a shopper says a product was “Too Small, what did they mean?
Was the fit too tight? Was it the length? Was it not flattering in certain areas?
To identify problems with your products and process you need both standardized and customized feedback from your customers.
This combination of data allows you to intuitively look at product by return reason code, and then dig deeper to realize a common trend of shoppers that are returning it because it had a poor fit in the hips and thighs.
Armed with this insight you have several courses of action, some of which include:
The easiest way to collect both return reason codes and shopper comments is to do it sequentially.
With returns data, not only can a retailer make adjustments to products and marketing based on historical data, but also launch real time investigations and identify problems with products.
One easy way to implement this strategy is by comparing your long-term product return rate with your short term return rate to detect abnormalities.
Your long term return rate is essentially your average return rate over a given period of time. This interval must be long enough that the trend line of the return rate doesn’t fluctuate too much.
Your short term return rate is a fraction of your long term return rate.
Example: You can choose a 12 month interval to represent your long term return rate and choose a smaller three month interval from your long-term return rate to represent your short-term return rate.
With your long term and short term intervals defined we can take the difference between short-term and long-term return rates to detect any anomalies.
Graphically, these anomalies would be represented by “spikes” in the graph.
These time intervals will serve as the basis for our investigation, as it’s likely that whatever caused a spike in product returns originated in the time period we’ve identified.
With the period defined we can look at various factors that may have contributed to the influx of returns, some of which include:
Using this approach you can get to the root of your returns problem, and address the issue in an effective manner that will save you both time and money in the future.