Elli Peterson's TechCrunch

Three years ago, I stood in front of our executive team explaining why our conversion rates had plateaued despite significant investment in traditional personalization tools. Our customer lifetime value wasn't growing, cart abandonment remained stubbornly high, and our multichannel selling strategy felt increasingly fragmented. That presentation marked the beginning of our journey into generative AI—a journey that would fundamentally transform not just our technology stack, but how we approached customer experience optimization, inventory management, and checkout process engineering. The lessons we learned weren't found in vendor whitepapers or conference presentations. They emerged from real failures, unexpected successes, and countless iterations that taught us what actually works when deploying AI in a competitive e-commerce environment. Our first exploration into Generative AI in E-commerce began modestly—almost cautiously. We had a catalog of 47,000 products, many with ou...

Marketing leaders today face a converging set of challenges that traditional solutions no longer adequately address: customer expectations for personalized experiences have reached unprecedented levels even as acquisition costs continue climbing, campaign execution remains frustratingly manual and slow despite waves of martech investment, and attribution remains murky across increasingly complex customer journeys spanning a dozen or more digital touchpoints. These aren't isolated problems but interconnected symptoms of marketing operations that haven't evolved to match market dynamics. The proliferation of channels, acceleration of buying cycles, and fragmentation of audience attention have created an execution gap that overwhelms human capacity for analysis, content creation, and optimization. Yet these same forces have generated the data volume and interaction patterns that make AI-driven solutions not just feasible but essential for competitive marketing performance. The eme...