Jetlore

Jetlore operated as a business-to-business artificial intelligence platform specializing in predictive technology for the retail market.

The company was established in 2011 by Eldar Sadikov, Montse Medina, and Sergey Andreev, who were all Ph.D. students in Stanford University's computer science program. Sadikov, who served as CEO, was born in a secret nuclear city in Russia and came to the U.S. at 17. He pursued a Ph.D. in Computer Science at Stanford, with research stints at Google and Microsoft, before dropping out to launch Jetlore, inspired by the entrepreneurial culture of Silicon Valley. Medina, a Spanish national and fellow Stanford Ph.D. student in computational mathematics, co-founded the company and later became a partner at Deloitte before leaving the business world to enter a contemplative convent. The founding team's academic background in machine learning and probabilistic models at Stanford heavily influenced Jetlore's technological foundation.

Jetlore's business model was centered on a Software as a Service (SaaS) offering for large e-commerce retailers. It provided a prediction platform that analyzed consumer behavior across merchant websites to understand individual preferences for attributes like style, size, color, and brand. This technology allowed clients to personalize marketing content in real-time, such as promotional emails and product listing page layouts, moving beyond manual, rule-based marketing. The primary objective was to increase conversion rates, boost sales, and improve customer loyalty and lifetime value for its clients, which included major retailers like Uniqlo, Nordstrom Rack, and eBay. Revenue was generated by enabling these large retailers to optimize marketing decisions for each consumer.

The core product was an AI-powered prediction platform that mapped unstructured consumer behavior data into structured, predictive attributes. Its proprietary 'learning-to-rank' technology would analyze a retailer's entire catalog, break down products into specific attributes, and then learn each customer's real-time preferences for those attributes based on their behavior across all channels. This generated a relevance score, allowing for the dynamic ranking and display of products for each unique user. For instance, eBay utilized Jetlore’s Predictive Layouts to customize marketing emails based on individual style and size preferences, leading to higher revenue per email and reduced customer churn. In May 2018, PayPal acquired Jetlore for an undisclosed amount to enhance and accelerate its PayPal Marketing Solutions, integrating Jetlore's team and technology to expand its value proposition for merchants beyond the checkout process.

Keywords: predictive personalization, AI retail technology, e-commerce personalization, customer behavior analysis, machine learning retail, learning-to-rank technology, retail marketing AI, SaaS, consumer preference engine, dynamic content optimization, PayPal Marketing Solutions, Eldar Sadikov, Montse Medina, Sergey Andreev, retail data science, customer churn reduction, online retail conversion, structured predictive attributes, personalized marketing content, AI prediction platform