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Machine learning is a branch of Artificial Intelligence. It is based on the principle that programs can learn from data and act on behalf of humans.

The algorithms behind machine learning aren’t new. Thanks to the advances in cloud computing and big data, companies can implement machine learning at a relatively low cost.

What does it mean for the Ad Tech / Martech industries? Digitally native brands, especially in the D2C space, are collecting gigabytes of data every day. Cloud computing and big date made this collection possible. Machine learning enables them to learn from the data, make predictions, and automate tasks.

Two popular methods are being used in machine learning: supervised learning and unsupervised learning.

Unsupervised learning is often used to find relevant attributes (dimensions) in a dataset. Let’s say you have a customer transactions history, along with the customers’ attributes (age, location, favorite color, etc.). You can use unsupervised learning to group customer that are similar together and identify common attributes.

Supervised learning is used when you have pre-classified data. For example, if you’ve identified a group of high spenders in your customer base (thanks to an unsupervised learning technique), given a specific attribute (let’s say location), you can predict which customers are more likely to upgrade their plan.

If you want to take a step forward in learning more about machine learning, I recommend you to read SAS’ article “Machine Learning: What it is and why it matters.”

leocelis

Hi! My name is Leo Celis. I’m an entrepreneur and Python developer specialized in Ad Tech and MarTech.

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