Our Investment in Luca: The AI-Driven Pricing Co-Pilot for Large E-commerce Companies
Retail e-commerce is as old as the advent of the web. In the decades that followed, a wave of innovation across supply chain, logistics, advertising, payments, checkout, and storefront tech has vastly improved the consumer e-commerce experience. Unfortunately, that evolution has been mostly one-sided. Brand, category, and pricing teams still largely live inside of spreadsheets to figure out how to optimize sales and profits. Data science in the context of large retail is still nascent, and the humans that are responsible for growing the business still ultimately rely on intuition and a limited set of tools to get the job done.
Pricing Like It’s 1999: Large E-Commerce Sellers Are Stuck in Spreadsheets
Pricing—arguably the most critical function—suffers the most. Retail pricing teams have the unenviable task of synthesizing large amounts of data from multiple channels to build pricing strategy—sales history, market trends, competitor price changes, and inventory availability. And the problem is compounded when there are tens of thousands of SKUs to process—in other words, it’s a mess. The output from this manual workflow never captures all of the revenue or margin upside. Invariably, given the complexity of the data processing challenge, costly decisions are often made.
A Better Way: A Modern Tool for Pricing Teams
That’s where Luca (YC Winter ‘23) comes in. Luca is a pricing co-pilot that uses AI to recommend and manage SKU-level pricing and discounts using data models based on a retailer’s historical prices and competitor data.
Like a time machine, Luca frees pricing teams from their spreadsheets and transports them to the era of modern, purpose-built tools and data-driven decisions. Undeterred by large data sets and complex signals, Luca is “always on,” continuously surfacing revenue and profit headroom while making recommendations for price adjustments. But that’s not to say that robots will now make strategic decisions—Luca doesn’t remove humans from the process. Instead, Luca enables people to make better pricing decisions through clear insights and real data—or as Luca aptly describes it, they are a pricing co-pilot.
The Perfect Team to Tackle This Pricing Problem
Today, I’m excited to welcome Luca to the Menlo Ventures portfolio after leading a $2.5 million seed round alongside our friends at Y Combinator, Soma Capital, Uber’s angel syndicate, and strategic angels from TikTok, Microsoft, and DoorDash. In backing Luca, we’re backing an incredible team led by Tanvi Surti and Yonah Mann, who worked on Uber’s dynamic pricing team. Tanvi led the pricing team for UberPool, while Yonah focused on pricing Uber Eats, and together, they created massive margin uplift for both businesses using a combination of data science and building internal tools for Uber’s teams to use. In other words, they lived and solved the hair-on-fire-problem themselves, and now they’re bringing that experience to the retail e-commerce ecosystem. Menlo was an early investor in Uber, and we couldn’t be more thrilled to back two star PMs from the Uber diaspora.
It helps that we speak the same language. Before moving into venture, I spent many years as an early-stage operator and executive, helping to build Braintree and AdMob and subsequently managing GTM for new products at PayPal and Google. I’ve witnessed the maturation of e-commerce and monetization firsthand. I’ve also experienced the complexity e-commerce teams face when optimizing their business for revenue and margins.
With their purpose-built tool/platform to address the broken workflows of pricing teams, Luca is a perfect fit for the Menlo portfolio. Pricing science is a core theme we’re excited about—just last month, we announced our investment in Orb, which helps infrastructure, fintech, and SaaS companies configure billing and optimize pricing. Both companies sit at the intersection of two primary focus areas for Menlo: vertical SaaS and AI. We’re certain Luca will be a huge asset to the retail ecosystem.
