As an operations manager, you probably worry about reducing the number of returns. According to a study conducted by three researchers in the German University of Bamberg, there are certain factors that can help predict e-commerce returns. Here’s a short list of those factors:
- Ordering multiple sizes of the same item could indicate a customer’s uncertainty about the product’s fit.
- Contrastingly, if a customer orders the same item in multiple colours, it could be because he/she likes the product and wants in different colours.
- As the average value of each product increases, so does the likelihood of its return.
- Purchases made using discount coupons are typically impulsive. This increases the chances of them being returned.
- Customers choosing post-delivery payment options are more likely to return items.
So, it’s possible to use predictive analytics to predict returns. But what are the benefits of using predictive analytics in retail returns? Well, the point of measuring potential returns is to reduce that number. Here are a few ways in which you can save your business from excessive returns:
- Create an automated email exchange to confirm with users who order the same item in multiple sizes. Offer an accurate size chart and chat assistance for such users.
- Optimise your discount vouchers to reduce returns. High discounts have been found to correlate with more returns. So, find the sweet spot wherein your sales increase and returns reduce.
- Include freebies with a customer’s parcel delivery to add the element of surprise and reduce the chances of returns.
- Check your payment-on-delivery orders and observe if they are associated with an unusual amount of return activity. You could consider banning customers who pay on delivery and return from using that delivery option.
- Track serial returners. Banning them permanently may not be the solution, but you can reduce their returns by catching their behaviour patterns.
The data you get by studying return patterns can help optimise your returns process. Here are a few examples of where predictive analytics can be used:
- To spot unusually high returns of plaid skirts because they were placed in the place of plaid shorts in your warehouse.
- To understand why a certain shirt with an experimental material is consistently returned (it could be because the material is itchy).
- To discover when the drawstrings are missing from an entire shipment of slacks.
- To find out when a batch of jeans labelled 30 inches is actually 28 inches in width.
- To spot alarming return rates of experimental products.
- To figure out why your latest product didn’t sell as per projections.
- To get product feedback that can be used to decide future product orders and stocks.
- To identify best-sellers and problematic products so that you can optimise your product inventory.
These are real errors occurring in the e-commerce industry, resulting in wasteful product returns. You can identify such efforts by applying predictive analytics to return data. You can obtain return data by collaborating with your mobile logistics solution. But gleaning insights from this data can be tricky. Here’s how you can simplify the process:
- Improve the quality of your return data. Many times, return codes are inaccurate or insufficient. You can optimise them by making them better-suited for your products and adding a comments box in the form for good measure.
- Leverage your first-line employees. It’s not enough to rely on customer feedback to understand why the return occurred. You could reinforce this information with that of what your employees observe first-hand.
- Use a logistics workflow software to accurately store information about returns, so you can efficiently deduce return patterns. The accuracy and efficiency with which this step occurs will determine how effectively you can leverage predictive analytics to reduce product returns.
Returns can weigh-in on your retail profits and fester as a long-term problem. However, you can effortlessly fix this problem using smart predictive analytics. This post describes how you can turn return data into insights, which can help reduce returns. This post explains exactly how you can get started.