Investment Decisions in Strategic Marketing Using Causal Inference Models

Correlation-based measures of marketing successes are frequently employed to aid managers in identifying where they should invest their marketing dollars. However, there is reason to believe that these measures themselves could be problematic as meaningful indicators of whether or not a campaign has truly been successful. In contrast, causal models show significant promise for constructing robust estimates of true causal relationships between marketing activities and firm outcomes such as sales, customer retention, and brand equity. This paper discusses the role of causal inference methodologies in strategic marketing. Key techniques include propensity score matching, difference-in-differences, instrumental variables, and causal machine learning. By systematically reviewing both real-world research and simulated experiments, we demonstrate the potential of causal models to help firms unlock improved value from their marketing budgets, simplify channel strategies, and more accurately measure return on investment. We further discuss challenges including model interpretability, sensitivity to bias, and limitations of available data. The paper concludes with a discussion of how causal analytics can be embedded within modern marketing decision-making frameworks to support evidence-based strategic investment decisions.

Causal Inference Strategic Marketing Marketing ROI Marketing Analytics Causal Machine Learning Propensity Score Matching Difference-In-Differences Instrumental Variables Uplift Modeling Decision Science

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