Returns Data as a Strategic Asset in Omnichannel Retail
In an era where omnichannel retail is reshaping the customer journey, one often-overlooked area offers rich insights: returns.
According to Pete Barker, Director of Product at Appriss Retail, the strategic use of AI-driven analytics in return data can help retailers uncover product issues, identify operational inefficiencies and make smarter merchandising decisions.
Rather than treating returns as isolated transactions, leading retailers are turning them into feedback loops that inform inventory planning, product development and marketing strategies.
Reverse Logistics Meets Artificial Intelligence
AI and machine learning technologies can sift through vast amounts of returns and reverse logistics data to identify patterns that human teams might miss. For example, repeated returns of a particular product across multiple store locations could signal a design flaw, misleading product description or an unmet customer expectation – data that can be fed back into the supply chain to mitigate future losses.
This approach aligns with the broader push for supply chain visibility and real-time decision-making across ecommerce and physical retail operations.
A Key Advantage in Reducing Costs and Enhancing CX
The benefits of this AI-driven approach extend beyond operations. By proactively addressing the root causes of returns, retailers can lower return rates, improve customer satisfaction and enhance brand loyalty. In omnichannel environments, where customer acquisition costs are high and brand consistency is critical, these insights offer a measurable competitive edge.
