Returns: Predicting and Preventing

According to the NRF’s 2015 Return Fraud Survey, returns cost retailers approximately $260 billion in 2015. The number of returns has been growing and the problem is complex for retailers. In-store returns have increased from an average of 8% in 2015 according to the National Retail Federation’s Annual Return Fraud Survey to over 20% in 2016, according to Supply.AI’s analysis. The increase in returns is attributed to omni-channel operations. During the holiday season, return rates can be as high as 30%–40%, and one NRF consumer survey found that more than one out of every three gift recipients polled had returned at least one item during the last holiday season. Returns wreak havoc on retail operations. They impact warehousing, supply chain and merchandising, and they are highly unpredictable. Thus, returns management and reverse supply chain management are becoming increasingly important. Supply.AI is tackling the returns problem on the front end via its AI platform, and trying to prevent returns from happening at all. Based in Silicon Valley, Supply.AI was founded in September 2015 by Karthik Sridhar and Gurudatt Bhobe, who heads the company’s Technology and Data Science division. Supply.AI is currently a member of a six-month Alchemist Accelerator program run by Ravi Belani for enterprise startups. The program admits only companies at a time, and these startups must have technical teams and be able to monetize from within the enterprise. Supply.AI will graduate from the Alchemist Accelerator program in September 2016, and the company is already working with three omni-channel retailers.

Supply.AI: AI Returns Solution

Supply.AI is changing the industry by helping retailers predict and, to some degree, prevent returns. The company’s AI deep-learning solution correlates patterns of consumer behavior at the online point of sale and then executes intervention strategies to try to prevent a return from occurring. Supply.AI is currently working with three retailers, and has helped one of them reduce its returns by 1.8%. Projecting for the year, this would increase the retailer’s top-line revenue by $61.2 million. There are multiple contributors to returns, not just one single cause. The Supply.AI platform takes these varying factors into account in order to predict and prevent returns, analyzing two major categories of information: customer behavior and systems behavior. The platform analyzes a customer’s buying behaviors and online purchase patterns to estimate how, why and when that customer buys and returns products. This customer behavior category includes fraud and product attributes. The platform analyzes the systems behavior information in order to determine how the retailer services the customer. This analysis includes data on shipping timeliness, order dispatch information and whether the shipment was lost or damaged—all of which is linked to shipping carrier performance. Supply.AI algorithms then build data models from historic shipping performance to accurately identify which shippers provide a higher quality of service in customer locations, Supply.AI found that more than 20% of returns occur as a result of these systems failing.) Supply.AI then combines all of the customer’s shopping and shipping information and uses its deep-learning predictive analytics to predict whether the customer will return an item. There is a small window of opportunity between the time a customer makes a purchase and the time Supply.AI can apply one of its four intervention strategies to help prevent a return, which means that Supply.AI systems run on a real-time basis.

Source: Angel.co

According to Supply.AI’s analysis, in 55% of all online apparel purchases, there is an issue of validation, such as of size or fit. For example, a customer may order a size M shirt online, but have a history of ordering size XXL shirts. In this case, Supply.AI’s auto-confirmation email system would send the customer a message, asking him to validate that he wanted a size M, based on his order history. Supply.AI found that another 20% or so of all returns are made by customers who order many items online. Retailers have the opportunity to reach out and get to know these customers, and to offer them promotions that encourage them to keep the items they have purchased. Carrier and logistics issues account for another 22% of all order returns. These types of returns may occur when there is a change in the shipping address, carrier or shipping time. Lastly, 3% of returns are due to “friendly fraud” like chargebacks. Supply.AI’s solution can not only detect the probability of a return occurring, but also execute intervention strategies at the point of sale for each of the four types of returns to try to prevent a return from happening.

An Outsized Impact on the Bottom Line

Seemingly small changes can have a big impact on the bottom line. Most retailers have a mandate to reduce their returns, and Supply.AI is currently working with one large retailer charged with reducing its returns by 0.3%, a typical target. Supply.AI has helped the retailer improve its return rate to approximately 1.8%, adding $61.2 million to the retailer’s top line. This is improvement was six times greater than what any process change the retailer instituted might have returned. Retailers that look at returns as an element of risk avoid becoming prisoner to their own processes. The three retail categories the Supply.AI app is most useful for are apparel and accessories, electronics and devices, and food and beverage.

Managing Returns: Costs and Benefits

Omni-channel retailers are confronting how to manage growing numbers of returns. Returns are unpredictable, and each one costs a retailer in terms of processing time and storage space and in other ways. According to industry experts, on average, one dollar of returned goods translates to only 20 cents of value after factoring in credit card fees, labor costs to prepare the goods for resale and shipping costs. According to Jonathan Byrnes, Senior Lecturer at MIT’s Center for Transportation & Logistics, retailers generally lose 10%–20% of their profits to returns.

Source: Angel.co

Reducing returns on the front end allows retailers to save time and money on the back end. Supply.AI’s automated, intelligent solution helps retailers predict and prevent returns, thereby saving on costs. Additionally, the platform provides transparency into customer behavior throughout the ordering process, a valuable benefit for retailers that can lead to further, actionable insights.