Historical context of predictive analytics

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The first steps towards predictive analytics were made in the mid-20th century, when statistics and mathematical methods began to be actively used for forecasting. However, at that time, approaches were limited by the volume of available data and computing power.
With the development of technology and the Internet, the amount of data available iceland phone number list for analysis has increased significantly. This gave impetus to the growth of interest in predictive analytics in the late 90s and early 2000s. In the 2000s, with the advent of more powerful computing resources and access to big data, the era of machine learning began, which allowed the creation of more complex models and significantly improved the accuracy of forecasts.
Today, predictive analytics is used in a wide range of fields, from healthcare to sports, and continues to evolve.
Practical Application of Predictive Analytics in Marketing
For marketers, predictive analytics is a powerful tool for creating personalized strategies. Thanks to forecasts, you can:
Personalize offers . By understanding customer preferences and behavior, companies can offer the most appropriate products or services. For example, online stores use analytics to create personalized recommendations based on purchase history.