Peter Eigenschink is a Revenue Data Scientist and solo founder with 12+ years of technical and analytical experience, focused on turning post-sales behavioral data into predictive signals that make churn and expansion decisions actionable for $5–50M ARR B2B SaaS companies. With a PhD in Quantitative Marketing and a track record building revenue simulations, price optimization, and lead-management systems, he bridges academic rigor and hands-on product delivery—having grown a micro-SaaS to six-figure ARR as a one-person team. He consults on churn prediction, customer segmentation, expansion analytics and using CS intelligence to refine ICP and GTM strategy, emphasizing lightweight predictive layers rather than more dashboards. Past projects include large-scale lead scoring across thousands of products and price-revenue simulation for €200M+ procurement portfolios, showing he scales methods from enterprise retail to niche SaaS. He currently researches data-driven decision-making in RevOps at WU Vienna, offering short, evidence-based assessments to reveal whether existing health scores actually predict churn and LTV.
12 years of coding experience
8 years of employment as a software developer
Master of Science - MS, Physics, Master of Science - MS, Physics at University of Vienna
Ing., System- und Informationstechnik, Ing., System- und Informationstechnik at Technologisches Gewerbemuseum
PhD in Quantitative Marketing, PhD in Quantitative Marketing at WU (Vienna University of Economics and Business)
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