Marketing experts, of course, try to show users the products they need at the right moment: when they are at their most ready to make a purchase. We simplify this task. The growth of artificial intelligence technology in advertising has greatly simplified this task.
Personalisation has ceased to be something out of the ordinary: it is now a necessity for both marketers and buyers.
With the help of artificial intelligence, product recommendations in advertising have undergone a significant transformation and now play a major role in the purchasing process. Machine learning algorithms can process enormous and ever-changing sets of history data and forecast the products that the user will want to see next.
In 2014, a survey of more than two hundred participants in the European retail market showed that about 80% of companies were actively using analytics tools. But the main problem is mistakes when it comes to interpreting results from analytics tools, identifying hidden patterns and building models of the future.
Predictive analytics is a class of data analysis methods that focuses on forecasting future behaviour in order to make optimal decisions.
Predictive analytics uses statistical methods, data mining, and game theory, and analyses current and historical facts to make forecasts about future events. In business, predictive models use patterns found in historical and current data to identify risks and opportunities. Models capture relationships among many factors to make it possible to assess the risks or potential associated with a specific set of conditions, guiding decision-making about possible transactions. Modelling is used in actuarial calculations, financial services, insurance, telecommunications, retail, tourism, health care, pharmaceuticals, credit scoring, and other areas.