Now that many more consumers are exhibiting true omnichannel behavior—starting the shopper’s journey in one channel and completing it in another—the risk control teams are faced with unfamiliar situations. The zigging and zagging between different transaction systems can make it hard to trace the origin of a loss and respond to it.
Your chain probably has skilled loss prevention teams that are well-versed in frauds within their focus area of store or ecommerce channel. However, they may have little visibility to risk and loss in other channels. Not only may visibility be limited, but completely different data points are analyzed. Even the picture of what constitutes normal behavior can be different.
If an order is returned, more risk enters the equation. Unless each channel’s systems synchronize in real-time, some consumers will probably take advantage of the disassociation between systems to behave dishonestly even to the point of committing return fraud.
Store loss prevention teams may not be up to speed on the types of fraud ecommerce teams encounter in high volume, like bots that create fake accounts, item not delivered scams, or the types of loss that occur in the D2C supply chain. Conversely, ecommerce teams may not grasp the variety of in-store controls needed to lower the risk for day-to-day BOPIS losses.
If an order is returned, more risk enters the equation. Unless each channel’s systems synchronize in real-time, some consumers will probably take advantage of the disassociation between systems to behave dishonestly even to the point of committing return fraud.
If you are truly committed to creating an omnichannel ecosystem, consider approaching the transaction process holistically. Omnichannel shoppers enjoy the convenience, and improving their experience can lead to higher profits as happened for this retailer. Meanwhile, internal teams from marketing to loss prevention can benefit from more robust information.
This 20-minute video offers insights into the differences between siloed transaction data points as well as ways you can connect omnichannel consumer behaviors in ways that can lower risk. Appriss Retail Chief Data Scientist David Speights, PhD, offers an overview of return fraud types, relevant data points per channel, and case studies.
