What are Machine Learning Algorithms?
Machine learning algorithms are algorithms which are trained to perform a task without being explicitly programmed how to do it. These algorithms acquire this ability by learning from provided data, often learning by example.
At their core, machine learning algorithms are intended to assist in making generalizations from observed data. In marketing, it can be applied to personalize the visitor experience.
To illustrate, in a classic A/B testing scenario, if given additional data about each user in the experiment (e.g., the user’s gender), a machine learning algorithm can learn to use this data to recommend the products that each specific user will have the highest probability of positively interacting with.
The more relevant the user data that’s provided to the machine learning algorithm, the better job it can do in the long run in finding the best products or offers to serve each specific user. For example, in addition to the user’s gender, the user’s past interactions with products (e.g., page-views and purchases) are often relevant for choosing which items to present to him/her.
Not all data about the user should be considered ‘relevant’ in a given situation. For example, if the algorithm is choosing between presenting the user with a promotion for a blue shirt or a promotion for a red shirt, data about the color of shirts the user has purchased in the past is clearly relevant, while data about the user’s shoe size could actually harm the performance of the algorithm.