The evaluation of the data-led decision making and in-store performance outcomes is developed with the help of quantitative methods including descriptive analysis, correlation, and regression modelling. This dissertation explores and analyses a data-driven trade marketing model that can enhance the in-store ROI with insights to be acted upon, powerful analytics, and localized execution strategies. The paper is founded on a case study of one company, Empati Reklam, a multi-country trade marketing and retail solutions company working in various retail settings. The study combines statistics of sales, shopper behaviour, store level performance variables and promotional performance variables to form a holistic analytical model.  The analysis is supplemented by qualitative inputs of marketing managers and retail partners, which involve the contextual elements that can affect the execution efficiency in different countries. Results indicate that trade marketing programs that are backed by live data, predictive analytics, and performance dashboards that are designed in a standardized manner are always better than intuitively developed campaigns. The model has shown quantifiable gains in sales uplift, conversion rate, shelf compliance and promotional efficiency and a decrease in wastage of resources and inconsistencies in executions. When comparing nations, it is important to note that it is necessary to balance between centralized data governance and local market customization in order to produce the maximum impact. The research has both theoretical and practical implications to the academic community and the managerial community since it provides a practical and scalable trade marketing model on the basis of academic evidence. To decision-makers, the offered framework outlined the methodical way to connect data analytics to the in-store strategy, allowing to more clearly measure the ROI, as well as to continuously improve the performance..