Etienne explains that a smart algorithm provides good solutions to a specific part of a problem, but that smart analysis chooses, combines and adjusts smart algorithms to solve the full problem. “Simply applying smart algorithms without thinking about the data can lead to misleading results”, Etienne says. This point is demonstrated by comparing the ice cream sales predictions of two algorithms called ‘boosting’, a popular machine-learning algorithm, and ‘SPECS’, their own algorithm. While boosting misleadingly seems to explain ice cream sales better within the dataset on which the models are estimated, it turns out to predict much worse ice cream sales on new data. Etienne explains that “while boosting is by all means a smart algorithm, it is just not smart to apply it to this kind of data that is trending over time”, highlighting the importance of smart analysis. “Smart analysis can go beyond the prediction of sales and allows us to understand what actually drives sales. For example, using statistical learning, we can also determine causal relationships and perform scenario analysis”. As an example, Etienne estimates a model that shows how a one-time 10% increase in advertising budget positively affects sales up to three months later.
Some real-life examples combining smart algorithms and analysis include using ‘spatial statistics’ to understand the best location for a new sales point, ‘financial econometrics’ to decide on when to take out a new loan based on interest rate predictions, and ‘clustering’ to create customer profiles based on loyalty cards for marketing campaigns.
However, we must be realistic about what data can tell us. Etienne concludes, “you may not have all the data you need for the best analysis; you might have made incorrect choices in the model or you may be wrong about the relationship between the variables. The good news is that smart analysis can quantify these uncertainties, such that they can be considered in your predictions and decision-making process”.