Automated scalping systems generate enormous amounts of headache on the consumer side, but sellers are also just as fed up. High-profile examples like the Taylor Swift / Ticketmaster debacle of 2022 dominate headlines and news cycles, but the day-to-day scalpers hurt consumers and sellers the most.
One large retailer (Retailer A) approached Spur for assistance on periodic releases of new inventory. Persistent supply chain challenges throughout 2021 and 2022 led shipments for a high-demand gaming product to be delayed and periodically received. This was the perfect storm for scalpers to unleash bots to jump the product release and corner the market on limited inventory.
It cuts both ways
A common misconception is that scalped inventory really only hurts the consumer. One might cynically say “well the retailer made their sale, why do they care?” This ignores the reality of being a retail store: margins are tight. In the retail world, many name-brand and popular products are sold at extremely low margins. Factor in customer support, marketing, sales, returns, theft, chargebacks, and pretty quickly any margin that was there is eaten away (or possibly negative). In this competitive market, one of the most effective ways to build profitability is to cross-sell customers with bundles or other higher-margin accessories along the way. So, when a scalper corners the market on a single item, and only that item, retailers lose out on this critical sales opportunity (and extra margin). Additionionally, bots behaving in this nature create frustrating user experiences, drive up infrastructure costs, increase the likelihood of chargebacks or other fraud, and also introduce regulatory risks to the seller.
Retailer A was ahead of the game and relatively sophisticated. They knew that they had issues with scalping bots and that these bots affected the company’s bottom-line on high profile sales. Additionally, they had rudimentary tools and tracking in place to help facilitate the detection and scoring of various carts in their digital storefronts. By themselves, however, they were only able to see a small part of the picture. Retailer A had correctly flagged one specific residential proxy network as a primary culprit for their scalped sales. However, in doing so they over-tuned detection for one proxy network and one actor. What the retailer did not realize was that multiple actors were using multiple residential proxy networks to pull off similar attacks.
From 1 to 100+
Spur’s Anonymous + Residential data set was brought into the retailer’s sales flows and visibility skyrocketed. Where Retailer A’s internal efforts provided visibility on a single proxy network, Spur’s data enabled visibility into over 100 different networks. Immediately the boards lit up and Retailer A was able to achieve their strongest product release yet. This bot detection analysis has moved beyond individual product releases and has become an integral part of the entire customer session tracking process.
Applicable to any retail
The best part about the experience with Retailer A is that Spur has helped apply it now to Retailer B, Retailer C and beyond. This outcome isn’t unique. It turns out that a large portion of the internet is automated. When reaching a real person for a real sale is important, understanding the footprint of residential proxy networks is non-negotiable.
