Definition:
Machine learning is a branch of artificial intelligence that uses data to train models capable of identifying patterns, making predictions or generating outputs. Its algorithms adjust the model using examples, rather than requiring a rule to be manually programmed for every situation.
During training, relationships present in the data are learned. The model can then apply them to new information. This does not mean that it dispenses with mathematical models or learns automatically every time it is used.
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Types of Machine Learning
The main approaches to machine learning include the following:
- Supervised learning: uses examples associated with a known answer. It can learn to classify information or predict values, such as identifying unwanted emails or estimating demand for a product.
- Unsupervised learning: looks for patterns in data without previously labelled answers. It can group similar items, such as customers with similar purchasing behaviour.
- Reinforcement learning: an agent learns to select actions by interacting with an environment and receiving rewards. Its goal is to improve the cumulative outcome, not simply to observe and draw conclusions.
These approaches can be combined within a system. The choice depends on the problem, the available data and how results are evaluated.
How to apply Machine Learning in digital marketing
In digital marketing, machine learning can analyse behavioural patterns and estimate outcomes to help guide certain decisions. Applying it requires defining what should be predicted or improved, obtaining suitable data and checking how the model performs on information other than the data used to train it.
Chatbots can use trained models to interpret queries or generate customer service responses. However, there are also chatbots based on rules and predefined flows: a conversational interface alone does not demonstrate the use of machine learning.
Data quality and the way results are measured affect the usefulness of the system. Detecting a pattern does not guarantee that it will persist in the future or that an automated decision will improve business results.
Machine Learning Examples
Its applications in digital marketing include the following:
- Advertising bids: Google Ads Smart Bidding strategies use models to adjust bids in each auction according to conversion or conversion value goals. They include strategies such as Target CPA and Target ROAS.
- Product recommendations: a store can use browsing and purchasing patterns to select products that may be relevant to each user.
- Customer segmentation: models can group customers according to similar characteristics or behaviours. These groups make it possible to study differences and adapt commercial activities without their labels having to be defined in advance.