.png)
Signifyd announced the launch of its AI-powered Return Abuse Prevention solution, a customizable innovation that attacks a $43 billion problem by detecting return abuse and empowering merchants to provide friction-free refunds for valued customers while thwarting those seeking to game the system.
Signifyd’s Return Abuse Prevention solution gives merchants the flexibility to customize their return-abuse response according to the unique needs of their businesses.
It gives online retailers the flexibility to set automated guardrails to alert customer support employees to the degree of risk involved with a return request — identifying them as low risk, moderate risk or high risk — along with recommendations on whether and how to process the return.
It allows for custom policies — for instance a guideline to never refuse a return from a loyalty program member.
Return Abuse Prevention also lets merchants simulate how their return rules would work in real life before they deploy them in the real world.
With the solution in place retailers can apply their proposed return policies to past purchase data and review the what-would-have-happened results.
“Unfortunately, fraudsters and a subset of consumers are becoming more aggressive and ingenious when it comes to taking advantage of return policies meant to make life easier for shoppers,” said Signifyd Vice President of Product Gayathri Somanath.
“Return Abuse Prevention relies on Signifyd’s network data, machine learning models and our new Decision Center module to give retailers the tools they need to stay ahead of this increasing, revenue-crushing trend.”
The new solution extends Signifyd’s identity-centric commerce protection to another crucial post-purchase checkpoint — the point of return and refund initiation.
It squarely tackles one of the most nettlesome customer-experience conundrums of the current commerce era.
Overcoming the challenge of returns is now more important than ever as ecommerce sales continue to rapidly become a greater portion of retail sales.
Online purchases are much more likely to be returned and the percentage of fraudulent returns resulting from online orders is significantly higher than purchases made in a store.
The world’s most successful retailers have largely solved the challenge of providing online shoppers with precise site search, rich images and product descriptions, rapid fulfillment and around-the-clock customer support.
But returns remain a serious drain on revenue and a hindrance to building customer lifetime value.
About 18% of online retail sales were returned in 2020 and 7.5% of those returns were fraudulent, according to the National Retail Federation and Appriss Retail. That represents the loss of $11.6 billion in the cost of goods alone, according to a Signifyd analysis.
But fraud costs much more than simply the cost of the purloined products. A return sets off a chain of shipping costs, inspection costs, restocking costs or liquidation costs.
In all, returns cost retailers nearly four times the cost of the goods involved, meaning returns cost merchants $584 billion a year, including $43 billion due to fraud, Signifyd calculated.
While it would be tempting to simply clamp down on returns across the board, retailers realize that wouldn’t be wise.
In fact, 75% of consumers surveyed by Upwave on behalf of Signifyd said they would be likely to stop buying from a brand based on a poor return experience.
And about 58% said they always carefully check a retailer’s return policy before making a purchase.
Free and easy returns are all-but-required for merchants who want to be competitive. But the easier it is to return items, the easier it is for professional fraudsters and dishonest customers to take advantage of return policies.
“When you have no safeguard or security check, it becomes clear to fraudsters and abusers that you are an easy target.
“It’s as if the word had gotten out and they were telling all their friends and associates to go hit up Cuts,” says Steven Borrelli, CEO of direct-to-consumer apparel brand and Signifyd customer Cuts.
from Help Net Security https://ift.tt/32N8AJf
0 comments:
Post a Comment