Behind the curtain of the world’s largest, ever-growing online retail marketplace called Amazon.com, there’s a secret sauce to success: state-of-the-art algorithmic technologies working at lightning speed to optimize product prices, and the logic behind it is riveting.
Originally founded in 2011 by Victor Rosenman, CTO and Co-Founder Eyal Lanxner joined Feedvisor early on to transfer his coveted algorithm insights into a company that was built to help online retailers and sellers optimize their prices in real time, and ultimately result in better business performance. Lanxner has a rich background in data mining and algorithms development across multiple realms, and brings a vast array of expertise to Feedvisor’s unparalleled repricing technology. In this article, Lanxner sheds some light on the company, which has become a leading vendor in the e-commerce intelligence and optimization space, and to date, manages millions of products responsible for driving 2% of Amazon’s global sales.
To attain a precise and quantifiable representation of the online retail market, Feedvisor employs a mix of Machine Learning models as well as Game Theory notions, to operate their repricing algorithms. “You don’t want to cannibalize your products because price wars will take effect” explains Lanxner on the logic behind the Game Theory techniques. “You want a win-win, not a lose-lose situation”. Their technology works to help sellers determine the highest price they can charge for their product, while still maintaining the highest ranking within Amazon’s product page.
A layman’s term for anybody at Feedvisor, the ‘buy-box’, is the page location that’s seminal to buyers and sellers alike. It’s the part of the product page that shows Amazon’s recommended seller to purchase from, for a specific product at a specific time. This recommendation can change in matter of minutes or even seconds according to the competing sellers and their attributes at the time. Setting your prices in relation to your competition for residing in the buy-box, is the key to more visibility and more sales. The challenge is finding a balance between the desired sales volume and maximum profit margin potential. Feedvisor’s price optimization algorithm does just that, making sure that its customer strategies are well aligned with their business goals and constraints.
But the competitiveness isn’t just about the same products being sold; it’s transitioning to similar products as well – namely private label and exclusively sold products. “Amazon is gradually changing from a buy-box competition driven environment to a search-based competition”, explains Lanxner. “Retailers are producing, branding and selling products that serve a similar function to consumers (e.g. different types of yoga mattresses), and placement and ranking in Amazon’s search results complicates the dynamics even more. In these cases, in addition to the pricing optimization step, there’s a preliminary step of identifying the subset of similar products which pose as true competition. This will be a key challenge in the future era of Amazon’s marketplace, from both business and technology perspectives.”
With $43 million in total funding (from investors including General Catalyst, SquarePeg Capital, and JAL Ventures), 100 employees (hiring!), and multiple pending AI patents, Feedvisor is moving forward with major momentum, and on course to reel in the largest sellers and brands on Amazon.com.