Online retailers are likely seeing an increase in overall transactions ahead of the holidays, but they need to remain vigilant in monitoring for activity that may signify fraud.
A spike in the sales of a particular product or a transaction with an extremely high dollar amount, for example, should be reason for concern and immediate action. If ecommerce retailers are able to automatically reject orders and narrow down the pool for potential fraud, the results can be quite positive.
But how do you know if an order is good or bad? Many ecommerce companies and brands are now exploring machine learning and other advanced technologies to help mitigate fraud and increase sales. Using these solutions not only helps improve the customer experience, it will result in improvements in other areas critical to the enterprise including the order decline rate (an indicator of how well sellers are doing when it comes accepting more good orders in general).
Companies like Sift Science, Forter, Signifyd, Riskified and others currently offer powerful machine-learning solutions that are able to flag suspicious orders automatically in real-time, so that retailers do not have to do this this work manually (which is often fraught with errors).
Digital marketing executive with proven experience in all aspects of search engine optimization (SEO), performance-based advertising, consumer-generated/social media, email marketing, lead generation, Web design, usability, and analytics. - 20-year Internet marketing veteran, currently serving as the Digital Marketing Campaign Manager at Antenna Group (formerly Chicago Digital). - Former Editor-In-Chief of Website Magazine, and a regular speaker on Web technology digital marketing strategy - Author of several books on digital marketing Including Web 360: The Fundamentals of Web Success; Affiliate 360: The Fundamentals of Performance Marketing; Domains 360: The Fundamentals of Buying & Selling Domain Names, and SEO 360: The Fundamentals of Search Engine Optimization.