Marketing Analysts: 3 Reasons to Embrace Machine Learning

As a marketing analyst, you are aware of the increasing convergence of marketing and technology. In the past year or so, “machine learning” has come to the forefront of technology trends, and its capabilities suggest that it will have a long-term impact on the marketing industry. As a graduate student at Northwestern University who is studying marketing analytics methodology as part of my coursework, I have become curious to find out more about machine learning and the advantages it offers to marketers, especially for those who analyze customer data and develop insights on a regular basis.

While machine learning capabilities are still relatively new, there are already many ways in which marketing analysts can benefit from knowing more about the technology. In a recent Huffington Post article, Steven Wong and Derek Slater of Ready State, a marketing agency in San Francisco, state that “machine learning’s near-term effect on marketing is greatly underappreciated” and discuss several examples of how machine learning has already contributed to the marketing industry. For programmatic advertising, machine learning technology has helped to optimize real-time bidding results based on its analysis of past consumer behavior, ensuring that it delivers ads that are contextually relevant to the user. While some marketers may be intimidated to learn more about machine learning due to its complex technology, Wong and Slater agree that at least being aware of its strategic marketing implications is important for working effectively with other data-driven professionals.

Image Source: Kevin Moturi

In addition to delivering relevant content to consumers through programmatic advertising, machine learning has also helped marketing analysts to determine which actions are most important in the consumer decision journey. In an interview with eMarketer, Google Analytics thought leader Justin Cutroni explains that “[metrics] are not all of equal value, and a company shouldn’t be paying the same amount for all leads.” For example, the Smart Goals section within Google Analytics uses machine learning to analyze past website visits and confirm which actions are most likely to lead to a conversion. This capability can help marketers spend their budget more efficiently by prioritizing channels that lead to the highest number of conversions.

After reviewing these two articles along with my relevant coursework at Northwestern, I have developed three reasons why marketing analytics professionals should become familiar with machine learning:

·      Speak the language - Given that machine learning is an advanced, data-driven technology built by data scientists and engineers, it is imperative that marketing analysts can at least understand its key principles so that they can explain its capabilities to clients who may not have a clear understanding.

·      Identify valuable conversions - Machine learning can predict user behavior at all points of the consumer decision journey, making it easier than ever to pinpoint the most important conversions.

·      Customize consumer interaction - With machine learning, marketers can humanize automated consumer interactions that respond with customized answers learned over time, creating a more authentic connection between brand and consumer.

By becoming familiar with machine learning and its advantages for the marketing industry, analysts can expand their skill set and make more informed business decisions than ever before.

Katie More is a graduate student at Northwestern University pursuing her master’s degree in integrated marketing communications with a specialization in marketing analytics. She has three years of advertising agency experience at Havas and hopes to work in marketing analytics/research upon graduating in December 2017. To contact Katie, you can find her on Twitter or LinkedIn.