Hindsight. Insights. Foresight.
HIF reflects a governing principle: economic growth compounds only when measured accurately, modeled systemically, and governed with discipline. This philosophy is not an abstraction. It is the operational foundation of every advisory engagement and every line of research the firm produces.
HIF Analytics
HIF is an advisory and research firm focused on the structural economics of promotion within CPG enterprises. The firm operates at the intersection of causal measurement, stack interaction modeling, and promotional system governance.
Rather than treating promotion as a marketing tactic, HIF approaches it as a capital allocation system — one that influences revenue quality, margin integrity, and enterprise value. The Promo Physics™ framework was developed to formalize this discipline.
Advisory. Structured economic assessment and promotional system redesign for mid-sized CPG enterprises.
Research. Independent analysis of promotional economics — incrementality, stack interaction, margin erosion, and capital exposure
Sanchin Raj
Founder of HIF
Promo Physics™ emerged from observations across engagements: promotional performance is measured tactically but governed insufficiently. The gap between campaign reporting and economic reality was not a matter of better dashboards — it was a failure of systematic evaluation.
Over time, that observation evolved from advisory insight into a formal economic framework — designed not only to inform decisions, but to operationalize them.
Sanchin brings more than two decades of experience spanning engineering, enterprise analytics, and executive advisory. With a foundation in applied technology — including fifteen years in engineering and early-stage ventures — he went on to earn his MBA from the McCombs School of Business at the University of Texas at Austin and subsequently led advanced analytical work within Fortune 500 environments.
There, he served as an internal advisor to senior executives across consumer and business segments, developing and deploying over one hundred predictive, optimization, and prescriptive models into production — generating more than $1B in total economic value. He holds a BS in Computer Science and Engineering.
He has presented on the economics of causal AI — including the limitations of correlation-based prediction and the imperative of causal inference in business decision-making — at industry conferences including the Causal AI Conference (San Francisco), PMG’s AIvolution series (Austin and Dallas), and Tech Titans (Dallas).
His commitment to clients is not revenue expansion alone, but the creation of governed promotional infrastructure that strengthens enterprise value over time.