It is no surprise that returns of consumer goods is a topic most companies wish to avoid. Returns are costly, difficult to manage logistically, and prone to process errors that can adversely affect the customer experience. Unfortunately, there is no nirvana when it comes to product returns. Returns will happen, and companies will continue to focus energies on return deflection, as they should. In the meantime, for those product returns that eventually do come back to distribution centers, it is not uncommon to hear such returns referred to as the ‘cost of doing business.’
Managing the reverse ecosystem is challenging enough, but too often, companies spend little effort on defining proper strategies to recover revenue, let alone maximize it, from returns. The solution, in many cases, is to outsource. In industries like consumer electronics such as smartphones, the paradigm of returns being a hassle has been the key enabler for a very intermediated supply chain consisting of brokers, selective buyers, and specialists to appear, all of whom have little to lose and much profit to gain. As a result, the secondary market for smartphones has exploded and will maintain high growth for many reasons including the rising cost of phones, customers keeping devices longer (~28.2 months), thus investing more in repair than replace, a heightened desire for original OEM spare parts, and the strength secondary market players have developed in their CRM structure.
The smartphone industry, over the last few years, experienced several major disruptions, all of which were catalysts for accelerating secondary market growth. Key disruptors included carriers abandoning price subsidy models in favor of financing, changing consumer behaviors evidenced by the correlation between longer time for consumers to upgrade their devices and the rapid growth of MSRP with new smartphone models. Lastly, it is consumers becoming very savvy with e-commerce tools for selling their devices at fair market value (vs. utilizing carriers’ trade-in programs which historically offer much lower value for used smartphones). The reality in the marketplace caused by these disruptors vastly differed from industry expectations. More specifically, smartphone OEM’s and wireless carriers arguably underestimated the impact the pricing model change would cause.
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Over the past few years, sales of new model smartphones have slowed, as has the volume of returns of the older models when customers finally upgrade their devices.
Predictions that pent-up demand when launching new smartphone models would mimic that of earlier years simply did not meet expectations. Regarding returns, one might think that fewer returns are a good thing, and in absolute terms, that seems logical. However, from a sales lifecycle point of view, the reality is disappointing in several aspects. Carriers, especially, have had to launch more aggressive and expensive sales promotions to accelerate sales. To some degree, this has been successful, but still, returns continue to lag simply due to the many options consumers have for maximizing their revenue when upgrading. This means that deflection of returns cannot be the sole focus for minimizing loss on the product. Rather, the dynamic also increases the importance of maximizing revenue from returns that, albeit at a lower volume, still come back to carriers for a variety of reasons.
As a U.S. based cell phone carrier, we certainly embraced processes for deflecting expensive returns. However, we did not stop there. Our Reverse Logistics team reframed returns as an opportunity. First, a new strategic vision was crafted to respond to the significant changes in the reverse ecosystem. To meet our revenue recovery goals, we knew first and foremost, that disintermediation of the reselling process was paramount. We evaluated several strategic approaches including the building of intelligence in-house of secondary market pricing now that many online solutions present time-series modeling of fair market and trade-in values. We also evaluated buying the intelligence and modeling engines from third parties to directly sell to secondary markets at the times predicted for maximum revenue recovery. Lastly, we considered taking the easy route and outsource to a third party knowing recovery would be diluted by revenue sharing. We scrutinized the marketplace in light of these options, searched for reliable sources of data intelligence, and studied our competitor’s practices and choices of vendor partners. We also revised our strategy for reselling. We executed trials of e-auctions directly and beta-tested several third-party resale models to gauge the level of effort against revenue recovery. We discovered that when attempting to maximize recovery revenue for returned devices, no one-size-fits-all resale model exists. Our data proved that internalizing all processes produced the best revenue recovery results.
This journey to transform the paradigm of returns being a cost of doing business to a paradigm that returns can be a revenue driver was long and arduous. The transformation affected people, process, and technology and therefore, needed to occur in phases. Learning the key drivers for maximizing results was time-consuming. Key performance measurements required development. Further testing of various resale models was necessary, and lastly, internal processes within our operation needed a significant redesign in order to speed to results. Throughout our journey, we learned that key success factors for maximizing recovery revenue included a sustainable, disciplined knowledge of where returned devices were in our ecosystem (e.g., in-store, in transit, in the warehouse), the building of processes across our enterprise to track and accelerate in-bound returns, the acquisition of knowledge of where certain models of smartphones sold best, and the ability to improve cycle times for triage, test, and disposition to best predict and capture optimum resale values.
Central to this journey was our continuous improvement process. We utilized a plan/test/ do/review approach throughout. The model and its accompanying toolkit (e.g., RASCI, FMEA, five whys, etc.) enabled us to identify and resolve challenges quickly. Inherent to a solid continuous improvement model are the feedback loops. The information gained during our journey from feedback alone enabled us to exceed expectations in the strength of our processes, in the amounts of our revenue recoveries, and in shortening our journey’s timeline.
The biggest challenge of the journey, without question, was data. Finding correct, trustable sources of data internally was difficult, not because it didn’t exist, but rather, because data lied in pieces with different internal owners across many functional areas. Once we were able to gather the right data, we crafted the story, built the business case, and strengthened the necessary relationships to gain buy-in and stand up a change coalition. The entire journey took nearly 3.5 years and was a huge success in several aspects. Our investment in new processes and technologies was calculated to have an ROI of 7.5 months but was met in two months. Revenue recovery rates easily doubled in some cases, but in most cases saw no less than 10 percent improvement. Revenue recovery percentages continue to grow today as cycle times continue to lessen. Feedback loops stood up with our buying community resulted in quicker process improvements and a clear understanding of which reselling models work best based on the available product, its functional and cosmetic condition, and the best locations for demand. Our revised strategy and vision certainly enabled us to plan for the future, but as importantly, executing with excellence resulted in changing product returns from an expense driver to a lucrative, profit center.
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