Subject The article discusses theoretical considerations of predictive and prescriptive analyses. Objectives The research summarizes algorithms and aspects of predictive, prescriptive analysis and identifies points of corporate growth triggered by the use of digital analytics. Methods The research employs general principles and methods of research, such as analysis and synthesis, grouping and comparison, abstraction, generalization. Results The article characterizes predictive and prescriptive analyses, modeling algorithms and identifies six focal points for analysis. I focus on key algorithms for modern analysis, i.e. setting trends and regression models, clusterization and classification of data, detection of data deviations and association analysis. The article reviews the algorithm used to build models, which involves training and test datasets. As part of each analysis, I find key aspects and points of corporate performance growth. Conclusions and Relevance The article provides solutions for better business performance resulting from the use of digital analytics, i.e. adapting a product and marketing to customers’ needs, reduction in the cost of business processes, articulation of the effective HR policy, making preventative decisions on fraudulent transactions, optimization of business model. The findings may be useful to analysts.
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