Machine learning is an analytical method that lets a system learn by itself — without human guidance and in an automated way — to uncover patterns, trends and relationships in data. Each interaction with new information then triggers actions that tend towards optimising the response.
That ability to learn from data and produce new results is what defines machine learning as a remarkable expression of artificial intelligence, one that contributes to many aspects of our daily lives.
It is no surprise to hear that parts of society and of the business world already take advantage of this analytical innovation: today we find companies across many sectors embracing its benefits — financial services, healthcare, sales and marketing, government and transport, among others.
Machine learning is a sub-field of computer science and a branch of artificial intelligence whose goal is to develop techniques that let machines learn. In reality, the machine that actually learns is an algorithm that reviews the data and is able to predict future behaviour. Automatically, in this context, also means that these systems improve autonomously over time and without human intervention.
Here is how it works:
1. Companies — whatever their sector or line of work — store large volumes of historical data: sales, customers, products, events and more. Historical data — for example on the whole customer base — properly organised and treated as a block, produces a database you can exploit in order to:
Predict future behaviour
Encourage the behaviour that advances business objectives
Avoid the behaviour that damages them. In short, machine learning lets you move from being reactive to being proactive.
2. That enormous quantity of data is impossible for one person to analyse in order to draw conclusions, still less to make predictions. Algorithms, on the other hand, can detect behaviour patterns from the variables we give them, and discover which variables led to, affected or influenced the later action.
3. All of that is an analysis of historical data — but where is the prediction? Here it is:
An example: customers who left a company or stopped consuming a service, with widely differing characteristics, at some point adopted a “certain” behaviour that led them to a common specific action — leaving the service. It is reasonable to expect that those who are still customers, and who show that same behaviour, are at risk of leaving.
According to that predictive model it is fairly likely to happen. If the marketing department had that information, it could proactively offer them a change and try to keep them.
The challenge of getting value out of data has been enormously simplified. With quality data, the right technologies and suitable analysis, it is now possible to build behaviour models that analyse data of great volume and complexity.
The systems deliver fast, accurate results without human intervention, even at scale. The outcome: high-value predictions that support better decisions and better business actions.
