Generative AI has dominated boardroom discussions in the business world over the past year. While some skeptics may question the full extent of its capabilities, others are keenly focused on extracting value from their investments. For those early adopters who wish to hop on the Generative AI bandwagon, particularly in the reverse logistics field, the rewards could be massive. The confluence of Generative AI, predictive analytics, computer vision solutions, and other novel technologies, can help organizations realize a multiplier effect on efficiencies, cost savings, and productivity throughout the returns lifecycle. Its ability to generate new data and analyze thousands of scenarios can enable retailers to stay responsive and agile in the rapidly evolving field. With Generative AI, retailers can gain rapid, real-time insights into product health, resale prices, shipping expenses, storage requirements, and other touchpoints. In future, organizations that will intelligently wield Generative AI, coupled with a fool-proof implementation strategy, will be able to derive maximum value out of their returns operations.

The Case of Missing Data: Overcoming Data Drought to Enable Precision in Predictions

Insufficient data presents retailers with the challenge of inadequate or limited insights into crucial aspects such as return reasons and patterns, effectiveness of policies, returns processing efficiency, and more. Without comprehensive data in these areas, retailers may struggle to accurately forecast return volumes, optimize inventory levels to accommodate returned items, personalize post-return marketing efforts, or enhance the post-purchase customer experience. Generative AI can help retailers bridge these data gaps, enabling them to gain deeper insights into return trends, customer behavior, operational inefficiencies, and market dynamics.

Generative AI has the remarkable ability to engineer synthetic data that closely mirrors the characteristics, properties, and traits of real datasets. Using the existing datasets as foundation, retailers can recreate returns data, addressing any missing information or inconsistencies to ensure a more robust and reliable dataset. With this synthesized data, they can conduct more accurate predictive analyses, run dynamic what-if scenarios and adjust operations on the fly to imbue resilience across the returns process. This degree of flexibility and adaptability can enable retailers to anticipate return patterns in real-time, minimize return costs, and ensure seamless returns experience for customers. Moreover, it can empower them to make informed decisions on whether to issue refunds, process exchanges, or provide store credits to customers.

The Breakthrough: Conquering Asset Recovery Challenges

Asset recovery is a crucial aspect of the reverse logistics process, and with the implementation of Generative AI, retailers can effectively address key challenges within this domain. Generative AI has the capability to aggregate data from diverse sources such as databases, APIs, IoT devices, and other repositories, irrespective of their formats or locations. Following data collection, advanced algorithms spring into action to cleanse, standardize, and enrich the data. This process can help logistics personnel and operations managers yield immediate insights into return trend analysis, cost breakdown of returns, inventory management metrics, shipments, customer feedback integration, and compliance with return process regulations.

A grave challenge in the asset recovery process is fraud detection and entitlement management. Effective fraud detection mechanisms are crucial in asset recovery, and Generative AI can substantially enhance these mechanisms. By leveraging advanced machine learning algorithms like deep learning, Generative AI can analyze various factors including past transactional data such as invoices and shipping information. This analysis facilitates the timely identification of anomalies or fraudulent activities, thereby ensuring the integrity of the asset recovery process.

Continuous agent support is also essential at various stages in reverse logistics, starting from issue raising to ticket closure. Throughout the entire process, which includes slot booking, entitlement detection, recovery, and closure, multiple interactions with the helpdesk team are required to facilitate a smooth recovery process. Generative AI, in tandem with auto agents, has the capability to oversee the entire process from a control tower viewpoint, guaranteeing smooth coordination and delivering a seamless experience for both customers and drivers.

The route to easy returns: Paving way for smoother returns and delighted customers

According to a report by the National Retail Federation, for every $1 billion in sales, the average retailer incurs $145 million in merchandise returns. This statistic clearly indicates that each return sets off a domino effect of challenges for retailers. Generative AI emerges as the go-to solution in this scenario.

Generative AI's ability to understand natural language and be trained on a vast corpus of data enables it to decipher customers' intents more accurately, and at a faster pace. With its capabilities, retailers can effectively manage return requests submitted through their portals and accurately categorize them considering factors like product type, pickup location proximity, pickup availability times, price range, and return window. Once the product is marked as eligible for return, they can also generate automated return labels. These automated labels can accurately specify details such as item codes, pickup addresses, alternative addresses, and contact information essential for pickup partners to facilitate smooth returns.

Retailers can also employ a blend of Optical Character Recognition (OCR), image classification and Generative AI to minimize return occurrences. Generative AI models can be trained on thousands of image datasets to understand both textual and visual characteristics of products. Retailers can leverage this capability to extract crucial information from product images, such as color, size, tracking numbers and barcode data. This enables precise matching of each product with its corresponding delivery code, ensuring accurate and prompt deliveries to customers and lowering the frequency of return incidents.

The gain game: Maximizing profit recovery, minimizing losses

Beyond its extensive list of benefits, Generative AI can also drive profit recovery from returned goods. Traditionally, returned items were simply relisted on a single online platform at fixed prices upon retrieval. But with Generative AI, the approach has changed. Retailers now have the power to deploy dynamic pricing strategies across a multitude of platforms. Now, they can intelligently assess and list returns on secondary markets, ensuring highest possible recovery rates and minimal losses.

An apparel retailer can leverage Generative AI to conduct a comparative analysis to determine which secondary market offers the most lucrative opportunities for maximizing returns on the returned items. By examining factors such as demand fluctuations, competitor pricing, brand popularity, seasonal trends, and return policies across various online platforms like Amazon, eBay, Walmart Marketplace, and Shopify, they can gain a comprehensive understanding of the current market landscape. With these up-to-the-minute insights at their disposal, they can make key decisions like running targeted promotions, tweaking product descriptions for better visibility, or surging prices based on seasonal demand peaks. In essence, they can devise strategies to turn returned items into saleable inventory, striking the optimal balance between cost-effectiveness and operational efficiency.

Bottom Line

In essence, Generative AI can provide end-to-end support to retailers across the entire returns lifecycle, starting from the moment a consumer initiates a return. Whether it pertains to registering the return, shipping the item, assessing its condition, prepping it for resale, recycling, or managing the refund, this technology stands to modernize every step, culminating in cost reductions and improved efficiencies throughout the returns journey.

---

Amit Gautam
In his role as Chief Executive Officer for Innover, Amit Gautam has responsibility for all aspects of the company’s product & services strategy & execution, as well as its financial performance and growth. Amit has a relentless focus on Growth and Innovation and holds a strong personal commitment towards “Outcome-Driven” Digital Transformation for businesses. Amit collaborates with the C-suite executives of Fortune 1000 companies and guides them to adopt a digital-first mindset, delivering bold transformations and exceptional experiences. Prior to Innover, Amit worked with firms like GE and Cognizant in various leadership roles. Amit studied Data Science at Harvard and holds a Bachelors Degree in Engineering from India.