Growing Role of Machine Learning in Marketing

Machine learning is a buzzword in the digital world right now, and for a good reason: Representing a major step forward in how machines can learn to find hidden insights without being explicitly programmed where to look.

As you read, I am sure all of you have come across one common question “What are some interesting use cases of machine learning?” To answer to this, there are some very interesting use cases for machine learning in nearly every facet of our world. My field, marketing, is all about problem-solving or decision-making relying on various analytical frameworks from economic, psychology, sociology, and statistics. This helps us as marketers, understand our target audience better, so that we can predict and react faster than anyone else. And as marketers, we strive to engage our audience in more meaningful conversations, understanding which words, phrases, sentences or even content formats resonate with our defined audience.

A research study on Lexical Analysis in Marketing by Marie-Laure Gavard-Perret, Jean Moscarola highlights that 2016 has been a year of progress in lexical analysis with the goal of finding the content or text that drove marketing success. For example, a hypothetical test at a butchery section of a hypermarket shows after a testing period of a campaign, that the audiences interact best when “Big” is used instead of “Slice”. Slice was associated more with “not enough” while “Big” was associated with “too much”. A perfect example of how machine learning can be applied for textual analysis by considering the context of the utilization of the words.

As a progressive step, I believe 2017 will see marketing campaigns get personalized by combining content analysis at the campaign level with content analysis at the individual level, where automation would be used to generate a new predictive model from the currently available data and then deploy that new model.

Content Analysis and Management

When it comes to content marketing, machine learning will help marketers:

  • Identifying ideal topics
  • Helping understand your audience’s reading level
  • Telling you what sentiment you need to express
  • Finding the ideal structure
  • Identify emotional language
  • Tell you when to publish
  • Identify which screen your audiences are on and how that could affect the designed content flow

Incremental Machine Learning

These techniques will become more prevalent, leading to real-time changes in marketing execution, not just ongoing. What we are really talking about is the ability to modify a campaign that is already in place by introducing new data rather than having to stop the existing campaign or even building a new campaign from scratch.

For example, a manufacturer of kid’s apparels wanted to offer special discounts on newborn products. Their challenge was to identify the right target audience i.e.  new parents and hopefully turn them into loyal customers. Here machine learning helps them identify the right target audience and communicate the right message at the right time. You would wonder how ML experts helped them identify the buying habits of to-be-parents. Once they knew this, it was easier for them to design algorithms to target the right audience.

To conclude, as machine learning techniques become more widely used and understood, more non-technical literature will become available. This growing repository will lead to an acceleration of subject matter expertise around machine learning in marketing that will lead to revolutionizing the industry.