Marketers traditionally relied on linear forecasting models, assuming the future mimics the past. However, modern challenges such as economic shocks and AI-driven changes render these models inadequate. Instead, causal AI represents a shift from static predictions to dynamic responses, akin to GPS navigation that adapts to real-time information. Traditional models break down due to their assumptions of stable variables and minimal lag effects, which fail to account for the complexity of modern buyer behavior and external influences. Causal AI enhances strategic responses to these new realities.
Causal AI reorients your approach from static prediction to rolling recalibration. Its strength isn't just in making predictions; it emphasizes real-time adjustments based on current data, improving responsiveness to market changes.
Traditional forecasting methods are based on outdated assumptions that the future will mimic the past and that relationships among variables remain stable, which is no longer a valid premise.
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