Limitations of Predictive Analytics(5)

Bilal Hussain Malik

                                              Limitations of Predictive Analytics

                                              Limited Understanding of Causality

One of the greatest flaws of predictive analytics is that it cannot establish causality. While predictive models can easily identify patterns and correlations between variables, they do not necessarily indicate whether a variable is the cause of another. Correlation only means that two behaviors or events occur more frequently together than coincidence would ever lead us to believe, but it is not definitive proof that one is the result of the other.

This disability is especially risky when organizations act on forecasts without full understanding of the underlying causes. For instance, a model might indicate that customers purchasing extensively in luxuries tend to churn. But assuming at face value that the luxury purchases cause churn and attempting to discourage them can backfire, driving away good customers. In practice, both behaviors could be the result of a third, unobserved variabl e.g., a change in lifestyle or dissatisfaction with the quality of service beyond the model.

Mentioning correlation rather than causation has the potential to lead to bad strategy, effort for naught, and even customer discontent or reputation loss. Predictive analytics should thus be employed as an idea generator, not a conclusive decision.

To fill this gap, organizations would have to combine predictive foresight with causal analysis techniques such as randomized controlled trials (RCTs), A/B testing, or structural equation modeling. Additionally, the incorporation of domain knowledge can establish the foundation for interpreting results in the appropriate context and identifying probable causal connections.

In summary, even with the powerful predictive foresight, understanding the "why" of patterns entails more inquiry than predictive models can offer.