How retailers can get ahead of Easter Sale returns

Online shopping returns are a pain. Here's how to handle them
Matt Hopkins
A consumer will return a parcel

In 2020, online shopping returns were free, and in-person shopping shut down. Four years later, the world is shifting back, but shoppers have stuck to some new habits. Now dependent on returns, buyers are changing the retail landscape.

According to The National Retail Federation, for every $1 billion made in sales, retailers incur $145 million in returns. This is a double-barrelled issue. Not only is the customer paying more, but the retailer also shoulders the cost of returning goods to its merchandising supply chain. This leaves business profits vulnerable as online stores stand to lose the majority of their pre-tax profit. With the rate of returns steadily creeping up, businesses may struggle to keep the lights on. 

Results from the National Retail Federation show the sharp increase in online shopping has sparked an 11% increase in return rates. Retailers must absorb the costs of any changes in demand during the return cycle - products can decrease in value by the time they have successfully returned to the supply chain. This may be due to various price deductions during the time between the purchase of the item and the return of the item to inventory. Other items simply don’t make it back. During the journey back to the supply chain, products can become damaged and consequently unsellable. According to statistics from the returns tech platform Optoro, 9.5 billion pounds of returns ended up in landfills in 2022. 

But the cost of returns doesn’t have to put a retailer out of business – preparation is key. Predictive analytics can forecast return trends to prevent overstocking and turn around returned goods quickly. As well, the future of automation through generative AI can help businesses endure these heavy losses. Generative AI can help reduce return rates by identifying new hero products and in some cases, open up new product development to the customer.  

Mitigate the expense of costly returns

The truth is, that businesses do not have to shift to paid returns. This can do more harm than good to their customer bases. In efforts to deter customers from relying on returns, many retailers have adapted their return policies by shortening product return windows. Others require goods to be physically returned. 

While this can be effective at reducing the number of returns, it can deter customers reliant on returning their items from making a purchase in the first place. Ideally, retailers keep their return policy without getting too weighed down by the costs.  

Retailers should introduce enhanced capabilities to better manage inventory through more adaptive demand planning which can identify key purchase patterns and returns. If businesses have a holistic view of their operations, they will be able to plan for customer returns. With the rise of social commerce, retailers have a fantastic opportunity to improve their understanding of customer journeys and fine tune merchandising, pricing, offers and advertising. 

This will allow retailers to take an always-on approach, frequently testing which products are performing successfully and creating a more dynamic approach to ranging and assortment that ultimately requires less guess work and inventory. Retailers that can better align customer journeys to merchandising, sourcing and supply chain will by default become more adaptive and profitable.

Automation & efficiency 

Automation in retail operations is crucial for reducing costs and improving sales margins, especially considering the complexity introduced by returns. Returns management can pose significant challenges to operational and planning roles, impacting profitability if not effectively addressed. 

AI offers a solution by enabling automation in various aspects of retail operations and planning. Particularly in cases where large volumes of data and decision points are involved, such as daily replenishment or markdown optimization, AI proves invaluable. By leveraging AI and machine learning, retailers can enhance demand forecasting accuracy and streamline the handling of returns. 

Integrating returns data into AI-driven systems augments decision-making processes and facilitates more precise inventory replenishment and fulfilment decisions. This increased automation minimises disruptions and ensures smoother operations. 

Furthermore, automating decisions at various levels—SKU, store, channel, or day—significantly reduces the burden of exceptions for merchandisers and demand planners. This allows them to focus on tasks that add more value to the business and customer. 

Looking ahead, retailers will increasingly prioritise strategic initiatives aimed at optimising merchandising offers, developing new product ranges, and enhancing customer experiences.

Leveraging new data, insights, and AI technologies will be integral to this process. Ensuring efficient returns management will be a fundamental step in achieving these strategic objectives and delivering an exceptional overall customer journey. 

Written by
Matt Hopkins
Written by
March 19, 2024