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Slaven Bogdanovic, PhD

Sr. Data Scientist

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About Slaven

Slaven started his career in market research, where he worked on product and price optimization for clients in the Fast-Moving Consumer Goods (FMCG) industry. He played a pivotal role in managing projects for some of the agency’s fast-growing clients. Over time, his interests led him to customer satisfaction research, where he specialized in segmentation studies.

Slaven then honed his business intelligence and data science skills, with a focus on improving workforce effectiveness. His approach combines statistical and econometric methods with advanced machine learning techniques. He has expertise in using tools such as Tableau, R, and Python to analyze and interpret data effectively.

Slaven has a Ph.D. in research psychology, where he specialized in quantitative methods. This has provided him with a solid foundation in analytical techniques. He completed his graduate studies in psychology at the Faculty of Philosophy at the University of Belgrade.

Recent Articles

Pricing Intelligence Engine

Rebuilding Pricing and Promotion Analytics for a Global Data-Storage OEM

April 21, 2026

A Fortune 500 global data-storage OEM was bleeding margin in its $200M U.S. B2C hard-drive business. One flagship family had taken a substantial net-pricing hit year-over-year, and roughly 45% of historical promos were returning only 0 to 20% ROI. Revology rebuilt the pricing and promotion analytics from the ground up using causal Double Machine Learning, a retailer-math ROI model, and a three-archetype segmentation framework. The target: $3M to $6M of incremental EBITDA (a 10x to 20x return on the engagement) within 12 months.

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Project APEX Pricing Power

Unlocking Pricing Power for a Global Pharmaceutical Manufacturer in Emerging Markets

April 21, 2026

A Fortune 500 global pharmaceutical manufacturer was making emerging-market pricing decisions by feel. We built a repeatable Pricing Quick Wins engine across four pilot markets, grounded in causal elasticity modeling, automated competitive equivalence mapping, and price-pack architecture and inflation-aware simulators. The pilots identified around $8M of median revenue opportunity, with a best-case of ~$12M. Local teams now own the engine and can repeat the analysis annually as inflation and the competitive set shift.

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Automated RGM Engine

Operationalizing Revenue Growth Management Analytics for a Leading Plant-Based Creamer Brand

April 21, 2026

A leading plant-based creamer brand wanted real visibility into more than $13 million of annual trade spend and a credible way to forecast promo ROI before writing checks. We built the Revenue Growth Management analytics engine for them in Python and Power BI, running on their existing stack. The team now refreshes pricing, promo, and revenue/gross profit performance deep dive models in 10 to 20 minutes and catches variance the old process missed by weeks.

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