Optimizing media effectiveness using machine learning: a guide for modern marketers
Today’s marketers suffer from an ever-increasing competitive landscape where vast amounts of money are being poured into marketing and media. The market is also dynamic and evolving, both in terms of products and consumer behaviors. As a result, navigating a business on quarterly metrics and traditional marketing success indicators is hard. This situation is hardly acceptable for the modern CMO as marketing and media today need to be accountable for invested money.
As marketing effectiveness cannot be measured directly for most media channels, this poses a challenge. Basically, we need to be able to answer the question of how many new customers the latest TV ad generated to be able to calculate a return on investment (ROI). One might be tempted to look at the lift in sales during the campaign period compared to some benchmark, but this is a fool’s errand as many factors contribute to any metric at any given time. As such, you may end up with a negative lift from the TV ad, which is hardly an acceptable (or the correct) outcome. Therefore, insights from correlation analyses should be reviewed with care. In order to alleviate this situation, many have turned to machine learning which enables a system to learn from data and identify which factor contributes to what amount of a certain metric each day. This way, an estimate of your TV ad’s “true” effect can be calculated from such a model.
However, it can be daunting, to say the least, to get started, and in this paper, we will outline the necessary steps to take to avoid pitfalls and common mistakes.