Returns – Climbing out of the e-commerce landfill.

The intersection of big-box retailers and saving the planet – Optoro uses machine learning to optimize return logistics both for organizations' bottom line and the environment.

Where does your return go? A dusty warehouse? Your neighbor’s doorstep? Refurbishment center? More likely – to waste.

5 billion pounds of returned items end up in a landfill every year, according to Optoro, a company specializing in technology, analysis, and distribution strategies for major retailers to manage returns.2 In addition to the environmental consequences of excess returns, retailer bottom lines are also suffering due to the inefficiencies and costs associated.

With the fast rise of e-commerce, retailers rose to the occasion to meet the growing demand of customers, but secondary processes, such as returns have been handled inconsistently and inefficiently across retailers. Further compounding the complexities of e-commerce returns, return rates can be as much as four times higher than traditional retail return rates and often require additional transport for evaluation and re-distribution.3 Handling returns is a complex and nuanced process, factors such as product type, current location, weight, damage, and market demand, factor into each product’s value and optimal secondary distribution channel. Returns logistics prove costly and there is a delicate balance in optimizing for greatest recovery price of a good and the cost to re-distribute that returned good.

Retailers across the country have started using Optoro’s predictive analytics solution and distribution services to create value and savings from their excess and returned inventory and turning their inventory into assets and focusing on the recovery of costs.3

Why Machine Learning

Optoro uses machine learning to create data-based recommendations in a space where spreadsheets and email dominate internal distribution and external secondary seller channels are difficult to evaluate and compare on a per-product basis.3 The speed and accuracy of these recommendations are especially valuable for products that lose value quickly, such as new technologies.2

Optoro’s algorithm is looking to predict which distribution channel will recover the most cost of a given returned good for a given retailer. Optoro’s machine learning searches for an informed recommendation based their wide data collection of large proprietary data-sets and real-time secondary market information.4 The optimal recommendation will be created based on the reinforcement of previous patterns and trends from the data relevant to the specific returned product.

Machine learning is particularly suitable due to the availability of cross-validation in continuing to hone the algorithm to detect further optimizations in the recommendation of a returned good’s path. This is possible as there are quantifiable outcomes, such as the dollars recovered or the time to return to the market, that provide a basis to compare and improve the algorithm.4 These algorithms not only enable the organization to select the optimal re-distribution channel, but they also enable Optoro to provide retailers with robust reporting on product performance and inventory management that can be used to optimize inventory planning, warehouse design, and other functions.3

Current Plans for the Future

With over $380 billion in merchandise returned annually, Optoro raised $75 million in new funding July 2018 in order to further grow the organization to meet the growing demands of the marketplace.5 ,6 With this funding, Optoro plans accelerate the expansion of their recommendation solution, partner with the biggest retail companies in the world, and grow their team to fulfill demand.5 This funding is expected to continue to bring an innovative approach to distribution and liquidation of returned goods, while leveraging marketplaces and channels to sell goods across digital platforms ranging from Amazon, eBay, and their own proprietary secondary market sites.7

In this last round of funding, Series Funding E, Optoro has accepted additional funding from the United Parcel Service (UPS). Further solidifying a relationship founded on principles of reducing waste and creating efficiencies, such as label creation and other logistics.7

Recommendations for the Future

Optoro’s success hinges on its ability to deliver value to its retailer customers, by recovering as much as possible. Optoro’s prioritization of expanding to new markets and customers is aligned with further optimizing the recommendation engine and predictive analytics that fuel their bottom line. The more data available for the machine learning reinforcement algorithm, the more weight and confidence can be placed in the recommendations of the distribution recommendation. With more data, Optoro will be able to contextual patterns in a way that may unveil seemingly idiosyncratic data in one context to be an example of a larger trend within other data sets or retailers with similar properties.  In the near-term, Optoro should continue expanding prediction abilities to new retailers and in the long-term evaluate ways to insert themselves into the supply chain to reduce chances of substitute products.

Open Questions on Optoro’s Decisions and Future

How do you evaluate the benefits of reducing waste with the excess cost (monetary and environmental) of transportation for a given good? Is it worth reducing savings to reduce waste in certain cases?

Is the UPS relationship beneficial or harmful in the near-term and long-term?


  1. Indix Corporation, “Indix Teams Up with Optoro to Streamline Retail Inventory”, April 27, 2015,, accessed November 2018
  2. Larua Sanicola, “There’s a good chance your holiday returns will end up in a landfill,” CNN Money, December 26, 2017,, accessed November, 2018
  3. Welcome AI, “Optoro”, 2018,, accessed November 2018
  4. Mike Yeomans, “What Every Manager Should Know about Machine Learning,” Harvard Business Review, July 7, 2015
  5. Stephen Babcock, Technically DC, “Optoro raises $75M, plans hiring,” July 31, 2018,, accessed November, 2018
  6. Erica Phillips, “Retail Returns Specialist Optoro Raises $75 Million in New Funding,” July 30, 2018,, accessed November, 2018
  7. UPS Pressroom, “UPS And Optoro Form Strategic Alliance To Enhance Retail Reverse Logistics Services,” December 12, 2016,, accessed November, 2018


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Student comments on Returns – Climbing out of the e-commerce landfill.

  1. This post highlighted several aspects of the “dark side” of e-commerce that most shoppers are blind to. The growing trend of free returns and free same day shipping obscure the true cost of these services to customers and are detrimental to businesses and the environment. Frequently, the cost to ship an item may exceed the item’s value and this is only compounded if that item has to be shipped to a return center like Optoro. Moreover, the retailer that is partnered with Optoro is only getting a fraction of the product cost back. I think it would be beneficial to both consumers and businesses to increase transparency around the costs of e-commerce returns. Perhaps we would all think twice about returning an item that costs $5.

  2. Interesting read! Reverse logistics is a big pain point for many early stage companies esp. in emerging markets such as India which do not have the scale to optimise on logistics cost.
    I really like what Optoro is doing. The nature of products returned is such that they have less depth in every product type, making it all the more difficult to create any value from the returned product. Enabling the retailer to repurpose the returned inventory which eventually would have anyway become dead inventory, is truly impressive. Apart from the benefits mentioned, carbon and waste savings can be achieved with optimised operations of these returned items.

  3. I look forward to speaking with you more about your post. I found this to be incredibly insightful and I really enjoyed reading this. With the ever-growing presence of online shopping – returns and the cost of returns will continue to skyrocket – “$380 billion in merchandise returned annually”. Both the business and environmental impact is significant. One of my follow-up questions based on our beer game simulation is whether and to what extent Optoro is or will integrate with suppliers.

  4. I really liked this article! The problem that Optoro has identified (e.g. a huge increase in return rates, especially for merchandise ordered online) is a big and expensive one for retailers. Using machine learning to identify an optimal channel for reselling based on all of the variables you listed would certainly be valuable. I wonder at what point in the “returns process” this algorithm gets executed? If, for example, a customer mails their return back to the distribution center, and then the algorithm determines that the prospect of reselling is highest in Store X… the cost of shipping the product to the distribution center and handling it there was wasteful. I think a big opportunity for retailers would be to have this system run in real-time, perhaps triggered by the customer going online to print a return label in the first place. The retailer could then provide the customer with a return label to send the merchandise directly to Store X.

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