Cohort Analysis:

Using this technique, users are put into groups according to a shared characteristic, and their behaviour is examined over time in order to learn how that behaviour evolves (Wikipedia, 2023). Businesses can improve user engagement, retention, and revenue by identifying trends and patterns in user behaviour. Cohort analysis is an effective technique for companies trying to learn more about the behaviour of their customers and base strategic choices on data.

Descriptive Analysis:

Descriptive analytics is the technique of using data analysis to discover information about the past (Hillier et al., 2023). To make data simpler to understand and useful, it must be summarised and organised. Dashboards, reports, and visualisations are a few examples of descriptive analytics.

Prescriptive Analysis:

Predictive analytics is the process of analysing historical data and making predictions about upcoming events or behaviours using statistical models and machine learning algorithms (Fields, 2023). In order to predict what is likely to happen in the future, it entails recognising patterns and trends in data. Predictive analytics applications include revenue forecasting, customer attrition prediction, and fraud detection.

Bibliography:

Fields, A. (2023) What is prescriptive analytics and how can it benefit your business?, WebFX. Available at: https://www.webfx.com/blog/marketing/prescriptive-analytics/ (Accessed: 09 May 2023). 

Hillier, W. et al. (2023) What is descriptive analytics? A complete guide, CareerFoundry. Available at: https://careerfoundry.com/en/blog/data-analytics/descriptive-analytics/ (Accessed: 09 May 2023). 

Wikipedia (2023) Cohort analysis, Wikipedia. Available at: https://en.wikipedia.org/wiki/Cohort_analysis (Accessed: 09 May 2023).