Is your company maximizing the potential of its returns data? Managing returns isn’t a top five priority for 33% of retailers, according to a study from McKinsey & Co., and 25% of retailers that do track reverse logistics metrics don’t do so efficiently. These industry-wide mistakes cost retailers billions annually.
Reverse logistics metrics give retailers the data they need to quantify returns and disposition processes. Tracking five key metrics—return volume, percent of costs, condition of returned products, financial value, and errors—can help retailers identify missed opportunities and areas for improvement.
Think of product volume as the number of units returned by your customers and then:
As a metric, return volume allows you to understand where you’re losing money, how you’re salvaging some revenue (via resale), and flag these issues (product performance, durability, material quality, detail page inaccuracies, etc.) that are leading to higher return rates.
As ecommerce continues to garner more of the total retail market, return volume is also increasing. Customers returned 16.6% of the total merchandise purchased in 2021, a 56% jump from the average return rate of 10.6% in 2020. Comparing your company’s return volume against the industry average can help you determine if your reverse logistics process is working.
If you do, divide “A” by “B” to arrive at your percent of supply chain cost.
Understanding the costs associated with resale/refurbish/reuse/recycle can help you measure the cost-effectiveness of each possible action. For example, if refurbishing an item for resale yields lower margins than redirecting the item to an outlet store, you can increase your profits by skipping refurbishment.
Put plainly, it’s the condition of a returned product, as well as the process of evaluating that product’s condition. Products returned in good condition are immediately ready for resale while those that aren’t generally incur some level of refurbishment cost.
Retailers aren’t stuck with just one post-purchase path for merchandise. Intelligent dispositioning allows retailers to create different rules for different categories of product returns, even redirecting items to alternative sales channels.
Keep in mind that some categories of products—regardless of value—fare better in the shipping process. (For example, flat screen TVs are prone to breakage in shipping.) Tracking return condition data helps retailers make long-term decisions about the most profitable channel for a category of products.
In reverse logistics, every action taken or not taken has economic value. That value can be positive or it can be negative, it all depends. For example, recycling returned products might incur short-term costs that hurt economic value. But the costs of investing in sustainability might be offset by positive PR for investing in sustainability, which leads to lower CAC, increased LTV, etc., boosting economic value.
Simply stated, you can’t maximize profitability if you don’t mitigate waste, and you can’t mitigate waste if you don’t understand the fiscal impact of every reverse logistics decision you make.
Manufacturing defects, inaccurate product listings, poor packaging, and delivery problems—these are some of the items that comprise mistake cost. Using mistake cost, you can track the costly errors made by manufacturers, retailers, 3PLs, and couriers makes that lead to product returns.
Retailers can’t correct their most frequent errors if they don’t know what those errors are. Tracking delivery variances and detailed reasons for returns helps retailers adapt to customer feedback, minimize returns, and maximize customer satisfaction.
While 83% of retailers agree that returns are a concern for profitability, only 66% have a strategy to improve the economics of returns.
Narvar’s expertise in ecommerce, supply chain management, customer care, and machine learning, gives retailers the intelligent data they need to recover more value from returns. Schedule a demo today to learn how Narvar can help you make the most of your reverse logistics data.