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Professional woman team member representing Revology Analytics team.

Teodora Matic, PHD

Sr. Data Scientist

About Teodora

Teodora is a Ph.D. researcher who focuses on applied statistics and machine learning. She began her career in market research, where she worked primarily on customer satisfaction, brand communication, product and brand optimization. In her role as a market researcher, Teodora used both traditional methods and newer technologies like eye tracking and facial recognition to collect precise data on consumer behavior and product interactions. This helped her to create more targeted and effective brand strategies.

Later, she transitioned to business intelligence and specialized in data visualization and reporting. Teodora uses tools such as Tableau, Python, and R to transform complex data sets into clear visual reports that aid in business decision-making. This helps provide clear insights into business metrics and strategies, supporting informed decisions.

Teodora has a Ph.D. in Applied Statistics from the University of Ljubljana, Slovenia, and a Master’s degree in Research Psychology from the University of Belgrade, Serbia. She earned her Bachelor’s degree in Psychology from the same university.

Recent Articles

Pricing Intelligence Engine

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

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

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

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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