By Larry Velman, VP of Technology

Retailers have historically treated returns as an expected yearly loss. After all, maximizing recovery on these items is difficult when pre-owned products tend to depreciate in value. Rather than tackling the complexities, organizations have understandably focused on driving profits through forward sales. But as returns continue to rise, retailers can no longer continue doing business as usual. They need to incorporate historical data into intelligent applications that seamlessly speak to each other via smart devices. Through this tech-driven strategy, retailers can make better, faster, more profitable decisions about how to manage returns.

As the VP of Technology of goTRG, I’ve had the pleasure of working directly with some of the world’s largest retailers who are ready to tackle this $550B problem. From this vantage point, I’d like to share three key issues that are costing retailers BIG and how big data is the only way forward.  

1. Making Decisions Based on Recovery Alone

In my experience, many retailers craft their returns disposition strategy based solely on historical recovery data. In this hypothetical example, let’s say a retailer expects to receive an 18% recovery from Smart TVs based on previous sales information. Often that retailer either accepts 18% as the status quo, or it strives to increase the recovery by a marginal amount. But increasing the recovery on a per item or category basis may come at the cost of a reduced sales velocity. The most profitable strategy must involve balancing velocity and recovery. 

Let’s look at this scenario: Retailer A receives a returned 63-inchSmart TV in April. Based on the sales data, this retailer determines January is the “best” time to sell because the demand for TVs before the Superbowl will warrant the highest price point. That raw data is vital and true, but it doesn’t tell the whole story. Historical sales data does not account for the cost of storing the product up to 8 months. So even though the demand for theTV may be lower in April than December, it may make sense to drop the price and get the item out the door to create more warehouse space for other items.That’s why retailers must incorporate data into intelligent software that can look at the bigger picture.

At goTRG, we developed intelligent pricing systems. These are applications that make smart decisions by looking at the market value of products, the cost of storing products, and how much they depreciate in value each day to determine if it makes sense to hold onto the item. We rarely accept margins like 18% per item. Instead, we strive for 60-80%recovery wherever possible.

 

2. Too Many Touch Points

One of the costliest inefficiencies in the reverse industry comes from the number of touches required to move a product from the return counter to its new home. Simply put, the more times a returned product is handled, the more value the retailer or manufacturer loses. These losses come in the form of overhead costs, shipping costs, packaging expenses, and repairs. On the other hand, if retailers have the data in advance, they can make better decisions about how to move products from point A to Z with the fewest stops in between.

At goTRG, we recently developed an application in partnership with one of our major retail clients that starts at point A. We programmed the application into a handheld device that the store associates can use to scan items at the returns counter. Utilizing the intelligent application, the scanner quickly informs the employee about how to handle the product. If the scanner earmarks the item to be returned to one of goTRG’s facilities for processing, our team immediately receives an alert. That means from the second the item gets returned, we know what to expect and can already create a plan to sort, process, and find a new home for the product.  

In some cases, the application indicates the value of the item doesn’t warrant the touches required to sort and process it.That’s where another one of our intelligence algorithms comes into play. This algorithm analyzes products that are shipped to us in pallets to determine if their value is worth the cost of breaking the pallets down item-by-item. The goal is to make sure we are only digging into a pile that potentially contains value.In practical terms, the system will tell us if deconstructing a pallet of alarm clocks to get to the laptops at the bottom is worth the labor required or whether it’s more worthwhile to sell the entire pallet as-is.

After that, the application incorporates information such as how long it will take to inspect and test a laptop and assign a cost. The system combines that information with the resale value of the item, which it determines by analyzing past and present market trends. This translates to significant savings not only by reducing touch points, but also increasing the speed to the secondary market.

 

3. Lack of Visibility and Accountability

Lack of visibility into which products have been returned and where they are located is a huge issue. One of the most obvious consequences is shrink, which is equivalent to inventory loss. Unfortunately when retailers don’t track the flow products through every touchpoint, they may not be able to determine the person or process responsible for the disappearance. As a result, they are more likely to experience shrink due to factors ranging from employee theft, administrative errors, vendor fraud, or cashier errors. This directly results in loss of profits–$61.7 billion to be exact.

Lack of visibility also puts retailers at risk of liability. For example, what if a returned product gets recalled? Retailers are required to remove the product from their inventory. But without visibility, they may not know how many recalled products they’re storing or where they’re located.

In addition to the physical location of the product, retailers may not have visibility into the data they need to make more informed decisions about how to sort, store, and resell products. That leads to more unnecessary touch points, and products sitting on shelves for far too long. Without visibility to data, retailers can’t answer whether or not it makes sense to store a particular product based on velocity vs. recovery.

That’s why we track each product as far upstream in the returns cycle as possible. In some cases, we help retailers track products immediately from the point of return by tagging and scanning items at the counter. In other cases, we tag returns from the moment they reach one of our warehouses. Then we track the products as they flow through our facility from the first person who receives the pallet, to the technician who tests the product, to the person who packages and ships the item to its new home. Moreover, we can monitor how long it took to move through each phase and who handled it along the way. This gives us our system built-in accountability and transparency, which makes us truly unique.

 

Bottom Line 

I’m so excited about the success we’ve achieved in helping retailers overcome their greatest inefficiencies. But we are only just beginning. As the VP of Technology at goTRG, my goal is to leverage all data points in the evolving IoT of our industry to help solve the challenge retailers face: How to maximize recovery by reducing the cost of returns, maintaining product value, and reducing the environmental impact– all while developing technology to manage returns as far upstream as possible without disrupting forward sales. That means as few touch points as possible and greater savings for retailers, consumers, and manufacturers. More importantly, it means sustainability by reducing transportation emissions, eliminating needless packaging materials, and preventing returned items from reaching landfills. At goTRG, we’re already operating under a sustainable model, but we plan to achieve zero waste by 2025. These objectives represent a slice of what the future of returns looks like. I can’t wait to continue carving the path.