Gone are the days when retailers relied on spreadsheets, emails and phone calls to offload customer returns and excess inventory into the secondary market. Today, the process of how this type of merchandise is (re)sold is being revolutionized by data, AI and predictive analytics. This shift is not just about efficiency; it is a necessity, as customer returns and excess goods continue to place enormous stress and cost on retail operations.
The scale of the challenge is staggering: in 2024, returns were projected to cost U.S. retailers $890 billion, representing nearly 17% of annual sales. And that doesn’t include overstock which — like returned merchandise — takes up valuable warehouse space. Consider this: the average retailer allocates 11% to 25% of its warehouse space to excess, returned, or obsolete inventory.
That’s a big footprint. For inventory that can’t be re-shelved — whether due to cost, obsolescence or wear — the secondary market offers a crucial outlet. But maximizing recovery from these channels demands more than just moving goods out of warehouses; it requires a data-driven approach.
How Data is Revolutionizing B2B Resale
Retailers, brands and OEMs are increasingly relying on data to optimize every aspect of their B2B resale strategies. This approach enhances efficiency, improves recovery rates and enables informed decision-making in critical areas.
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An online B2B resale platform — especially one backed by advanced technology and robust data — can address persistent challenges in inventory management by offering multiple channels to sell and remarket goods. Many leading brands and retailers now use these platforms as centralized hubs for all their secondary market resale activity, gaining a single system of record while replacing historically fragmented manual processes with integrated solutions.
With a data-driven B2B resale strategy, retailers, brands and OEMs are able to optimize every aspect of their resale operations, including:
Confidence and consistency in pricing
Understanding the fair market value of merchandise and the variables impacting pricing — such as condition, inventory type and sales channel — is essential. A B2B platform equipped with extensive historical pricing data and variable analysis offers a clearer view of competitive market rates. The most effective platforms provide multiple options for selling into the secondary market, from open marketplaces to pre-negotiated contracts, and allow for comprehensive pricing comparisons across channels.
Access to the right buyers
Returned and unsold merchandise attracts a robust and diverse buyer base across categories and conditions. Top B2B resale platforms maintain databases of thousands of business buyers including online resellers, bin store operators, off-price retailers, exporters and refurbishers, ensuring steady demand and competitive pricing. Additionally, these platforms streamline the onboarding of existing buyers and enable targeted marketing to reach the most relevant audience.
Quick, scalable inventory movement
Platforms offering varied sales channels — such as auctions, direct sales and contract-based transactions — support greater scalability and speed. This flexibility allows companies to significantly increase the volume of merchandise moved while maintaining efficiency in sales cycles.
Brand control
The ability to manage sales channels is essential for brands and OEMs seeking to protect their image and prevent channel conflict. Online B2B resale platforms let sellers set parameters for how and to whom inventory is remarketed, with customizable restrictions to ensure alignment with brand guidelines and business priorities.
These restrictions might include:
- Restricting resale on third-party marketplaces
- Establishing geographical limitations on resale
- Selling only to exporters
- Selling only to off-price retailers
- Requiring brick-and-mortar sales exclusively
- Mandating all items be de-labeled prior to resale
Automated sales process
A well-established, technology-based B2B resale platform can manage the entire resale process from start to finish. This includes listing recommendations and setup, integrated payment processing, automatic invoicing and pre-scheduled listing launches.
Such platforms also maintain detailed records to track key performance metrics: a critical capability for accurate bookkeeping, tax compliance, regulatory adherence and verifying that buyers meet resale requirements.
Actionable data and predictive modeling
Leveraging data to meet resale objectives, whether it’s recovery, sales velocity or brand control, can significantly improve outcomes. Even small, data-informed adjustments in lot optimization, manifest accuracy, targeted marketing and selecting the best resale method can help drive better results across the resale operation.
Modern B2B resale platforms use AI and machine learning to assess more than 100 variables, including category, condition, brand, lot size, seasonality and SKU depth, producing accurate pricing estimates and reliable recovery rate forecasts.
Across B-Stock’s customer portfolio of retailers, brands and OEMs, the number one concern is how to get the highest pricing for their inventory. By leveraging 15+ plus years of B2B resale transaction data along with predictive modeling, customers are empowered with actionable insights on how to increase B2B pricing for bulk quantities of returned and excess merchandise.
Here are some examples of what these advanced analytics can uncover:
- The top five variables impacting pricing include: product category; brand; condition; manifest design; SKU depth
- Pricing differs by category and sub-category
- Pricing significantly differs between subcategories. For example, in looking at the subcategories that fall under the Apparel and Accessories category, handbags, when separated out, typically achieve higher pricing than apparel
- Different categories benefit from a deep SKU vs shallow SKU. Example: outdoor furniture fetches higher pricing in a shallow SKU listing.
- Listings that have inventory that is similar in retail price will get higher pricing
To better understand how these analytics work in practice, let’s look at an example. Imagine a housewares retailer with a truckload of furniture it needs to sell, all listed on a single manifest. To determine potential pricing for that manifest, the retailer loads the manifest into a predictive modeling tool. Leveraging AI, machine learning and historical data, the tool analyzes the manifest against 100 variables and then: 1) Predicts the total manifest price, 2) Explains the rationale behind the pricing, and 3) Offers a playbook for how to achieve even higher pricing.
If the pricing isn’t fetching what the retailer wants, it can then modify its manifest based on the tool’s recommendation. In the case of a housewares retailer, the tool would likely suggest:
- Grouping the inventory into LTL (less-than-truckload) lots
- Grouping it based on inventory that is similar in original MSRP
- Grouping it based on subcategory. Example: couches and plush armchairs would go in the same auction lot, while rugs would go in another auction lot
Once the recommendations have been applied, the retailer can plug its updated manifests back into the predictive modeling tool and it will query the new manifest and make another prediction of pricing (in an online auction environment).
Turning Returns into a Recommerce Opportunity
For years, returns and overstock were simply seen as an unavoidable cost of doing business in retail, but that perspective is changing. With smart data-backed resale strategies, returns and excess inventory can actually become valuable assets. Brands that take a data-driven approach to managing returns aren’t just boosting their recovery, they’re also uncovering insights that help them enhance the customer experience, fine-tune their product lines and advance their sustainability goals.
As the secondary market continues to expand, fueled by economic pressures and consumers’ growing demand for value, the importance of leveraging data effectively will only increase. Retailers, brands and OEMs that embrace these technologies and integrate them into their B2B resale strategies are positioning themselves to not only reduce operational costs but also unlock new revenue streams and competitive advantages.
Now is the time for retailers and brands to rethink their approach to how they resell returns into the secondary market. Adopting data-based solutions is not just optional but a strategic imperative for building a more profitable, efficient and sustainable future.
Marcus Shen serves as CEO of B-Stock, a leading recommerce platform and system of record for all B2B resale. The company’s technology drives billions of dollars of secondary market transactions each year for the world’s top retailers and brands. Prior to B-Stock, Shen was CFO and Head of Operations of Content Analytics, an ecommerce analytics solution for retailers and brands. Before that, he spent over five years at Yahoo!, where he was VP of Corporate Development, with strategic and operational responsibility for the company’s acquisitions, investments and key partnerships. Shen brings over 20 years of experience in the internet and software industries, specializing in SaaS, ecommerce, and marketplaces.
