media update's Aisling McCarthy delves into what predictive analytics is, and how it is being utilised in the media and marketing spheres.

Predictive analytics is a branch of AI that uses algorithms to make predictions about the future. Using techniques such as data mining, statistics, machine learning, and AI, the predictive analytics technology is able to analyse current and historical data in order to make future predictions.

The application of this technology includes fraud detection in networks, predictions regarding products or services customers may be interested in, or basket analysis to understand consumers better.

The concept of predictive analytics has been around for years and, historically, many businesses have tried to leverage past performance to steer the direction of new projects and to predict their results.

With the development of AI technologies, predictive analytics has become more and more accurate. Using historical data, AI-powered predictive analytics is able to find patterns in the available data about a specific topic, phenomenon, or organisation through algorithms, text analytics, and machine learning techniques.

The importance of having good data

Having useful historical data to work with is key to making predictions about the future. The sheer amount of data required to make informed predictions means that manually analysing the data is not viable.

The larger the data pool is, the more accurate the predictions will be. This means that by utilising AI systems and being able to analyse Big Data, the predictive analytics will be more accurate. For example, if you wanted to predict what products consumers will want to buy in the future, you would need to provide good data on which products they have bought in the past, the attributes of those products, as well as demographic characteristics of the customer.

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Artificial Intelligence is helping to decode some of the big data in the media. Read more in our article, How AI makes sense of the media’s Big Data.