Descriptive Analytics

What is the primary purpose of descriptive analytics?

What type of analytics is used to identify the customer segment responsible for generating the highest revenue?

Which analytics type uses statistical modeling and machine learning to forecast what will happen in the future?

What is the primary goal of prescriptive analytics?

What type of analytics involves correlating data with other data sets to uncover underlying reasons for trends?

What is the order of analytics types, from most basic to most advanced?

Which analytics type helps identify specific features of the customer segment that contribute to their product purchases?

What is the primary objective of Prescriptive Analytics?

Which type of analytics uses historical data to predict future trends?

What is the main goal of Descriptive Analytics?

Which type of analytics is used to identify the reasons for trends?

What is the primary goal of Diagnostic Analytics?

What technique is used in Predictive Analytics to forecast future trends?

What is the primary objective of Descriptive Analytics?

What is the main difference between Prescriptive Analytics and Predictive Analytics?

Summarizes raw data using data mining and aggregation techniques to reveal trends.
Involves measures of distribution (frequency or count), measures of central tendency (mean, median, mode), and measures of variability (variance and standard deviation).
Helps identify customer segments responsible for generating the highest revenue.

Analyzes data to uncover the underlying reasons for trends identified through descriptive analytics.
Correlates data with other data sets using techniques like probability theory, time-series analysis, filtering, and regression analysis.
Identifies specific features of the customer segment that contribute to their product purchases.

Uses statistical modeling, data mining, and machine learning to analyze large volumes of data to forecast what will happen in the future.
Relies on historical data to make predictions.
Projects expected demand for the next quarter.

Goes beyond descriptive, diagnostic, and predictive analytics to recommend the next course of action.
Harnesses data optimization, simulation, and decision analysis methods using artificial intelligence or machine learning techniques.
Recommends the optimal price and marketing strategies based on the forecasted demand and other contributing factors.