REPOST FROM: Harte Hanks


WRITER: Korey Thurber


Marketing activities create a deluge of data. Today’s marketers face, at times, an almost overwhelming number of challenges when it comes to deploying truly personalized, choreographed and impactful communication streams across the customer journey. For every $1,000 spent, marketing activity can generate literally millions of data points in the form of impressions, clicks, responses, conversions, revenue, and sentiment data points. So how does one make sense of this plethora of data? This is where Artificial Intelligence and “machine learning” swoop in to save the day.

In this post I’ll be explaining what machine learning is and what it does. My subsequent posts in the series will cover:

Companies that are using machine learning well and how they’re using it
Applying machine learning to marketing through personalization and sentiment analysis
How to get started with machine learning
What Is Machine Learning?

Very simply, Machine Learning is a form of Artificial Intelligence (AI) that is used to solve problems by finding patterns, deriving insights, and making predictions from tremendous amounts of data. It enables computers to churn through this data and to learn and improve without explicitly being told to do so.

Let’s say an analyst wishes to build a predictive model to score and rank individuals on their likelihood to buy product x. Roughly speaking, the analyst will follow these steps:

Collect and compile the data
Perform an initial exploratory data analysis to identify potential predictors
Build the model
Implement the model in a centralized customer database
Structure a test to validate the model in the “real world” via a marketing campaign
Collect the results and evaluate the performance of the model
Utilize the results and corresponding new data points to rebuild the model to hopefully improve upon the results generated by the initial model
Repeat steps 4) through 7)
As you can see, it is a manually intensive process that mainly consists of data manipulation and manpower from analysts and data scientists to find the insights and improve the predictions going forward. Machine learning, on the other hand, leverages the massive processing power of computers. Their objectivity allows them to see patterns in tremendous amounts of data that the biased human mind cannot, and then apply those insights to determine how new data can be used to accurately predict results.