Peak selling season is right around the corner, and supply chains across the globe continue to face unprecedented challenges — material shortages, insufficient labor pools, and transportation inconsistencies, just to name a few. While demand for goods may be high, limited supply could push prices up and total sales volume down. As a result, retailers need to ensure they protect margins on every order.

The pandemic has forever changed shopper behaviors, so online sales will undoubtedly set another record. While that can be very profitable, the rate of returned goods from online purchases is outpacing the growth of those sales nearly 5x. Returns are eroding margins. This can hinder the performance of forward logistics, since resources often get redistributed to manage those returns. Reverse logistics come with unique and complex processes that often lack visibility, data, resources, and scalability — features commonplace on the forward side.

For many trading partners, product returns are considered a necessary evil — another cost of doing business. Yet, without the right returns strategy in place, even the most efficient operators are likely to miss their holiday top-and-bottom-line projections. An underperforming fourth quarter may lead to cutbacks or reallocated funding as companies scramble to “right the ship” during the first quarter of the new year.

You can't improve what you can't measure

Determining your optimal returns strategy requires a “data first” mentality. You can improve returns management and processing through outsourcing, internal teams, or via a hybrid approach, but you need the data to do it. Internal teams, for example, need to understand the true total cost of a return. This includes hard costs like labor, transportation, and warehouse space. Soft costs are more difficult to isolate but equally important. They include credit resolution, IT services, and reporting, as well as support-related tasks like phone calls, emails, chatbots, and SMS.

Once the true costs have been determined, analysts can begin dissecting data points to uncover root causes and trends. This is usually where seemingly unrelated data reveals a new layer of intelligence, which feeds more granular insights. For example, instead of measuring transportation costs by truckload, we begin measuring transportation costs by item. As the levels of intelligence increase, richer insights are revealed. Intelligence begets intelligence. Applying AI and machine learning to customer, product, geographic, returns, and time-based data sets can make return processing more efficient. At the same time, by analyzing the root cause, you're reducing the number of returns.   

What's the best way to maximize value recovery?

That “data first” mentality will also boost value recovery. A dynamic rules engine can analyze cost data, processing times, warehouse locations, and shipping zones. It can also determine the most efficient disposition method — whether it be return-to-stock, return-to-vendor, liquidation and remarketing, donation, or destruction. Using AI and machine learning, the system can determine thresholds that may move a product originally slated for donation to liquidation and remarketing. For example, returning a single product to a vendor may be cost-prohibitive, but a collection of returns to the same vendor may become feasible — even optimal — despite the added costs of warehousing. Intelligent decision support systems can evaluate thousands of variables and compare thousands of potential outcomes to determine which method delivers the highest value — all while adhering to the client's return protocols.

So what's the best returns methodology?

Obviously, returns can be a daunting and highly disruptive process — one that puts significant strain on IT departments, operations, and logistics. Even large companies may face significant challenges in today's labor-constrained work environment. Some of these firms may already have mature return processes in place, but they'll still require assistance with overflow. Similarly, companies may opt for a SaaS model to glean insights while using internal resources to inspect and process returns.

Other companies, particularly small-to-midsized organizations and pure-play e-commerce retailers may prefer a turnkey outsourced solution. This approach provides the fastest time-to-value without disrupting their core functions — making, selling, and distributing their wares.

But regardless of methodology, it's important to remember data first, because you can't improve what you can't measure.