Identifies the relationship between a group of explanatory variables (eg, billing, call center operations, on-site service or employee performance) and the variable of interest (eg, profitability by customer segment)
Divides the market into groups that are like-minded enough that all customers in a given segment can be marketed to as if they were a single entity. The methodology of choice for segmentation is cluster analysis or discriminate analysis.
Conjoint and discrete-choice models
Models customer choice behavior or the trade-offs customers will make between different product features, including price
A powerful, visually represented, multivariate analysis technique that combines factor analysis and regression to study direct and indirect relationships between variables of interest (eg, customer characteristics and experiences and perceptions of the value of their relationship with an energy supplier)
Identifies trends or patterns in data over time and predicts or forecasts future values (e.g., forecasting the need for waste water treatment plant capacity based on predicted water use)
A sophisticated way to analyze cross-classification tables and test interactions between variables for statistical significance (eg, the test for the existence of a disparate impact of airline baggage screening procedures on selected minority groups in the passenger population)
Focuses on time as the variable of interest, such as the study of the ‘survival’ i.e. retention rate of particular customers.