Sophisticated statistical analysis from PA's market analytics experts
PA's statistical analysis team provides the full range of data analysis services, from analyzing qualitative focus group data to using sophisticated multivariate statistical analysis techniques on quantitative data. We have designed and conducted workshops for clients who wish to develop an in-house statistical analysis capability, and we have served as expert witnesses on statistical analysis and interpretation for litigation cases. We are skilled at programming and using primary statistical analysis software packages, such as SPSS and SAS, as well as different types of specialized software.
Listed below are some of PA's typical analyses and modeling techniques, with examples of their uses:
Multivariate models - 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, customer satisfaction)
Market segmentation - analytically divides the market into meaningful groups. Markets can be segmented at a high level for strategic analysis or at a product-specific level to guide product design and marketing
Cost-effectiveness models - determines the impact of a program in economic terms (eg, the change in customer satisfaction from an increase in customer service performance, to help utilities better allocate customer service resources)
Conjoint and discrete choice models - models customer choice behavior or the trade-offs customers will make between different product features, including price
Structural equation models - 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)
Time series forecasting - identifies trends or patterns in data over time and predicts or forecasts future values (eg, forecasting the need for waste water treatment plant capacity based on predicted water use)
Distribution fitting - tests the similarity (or differences) between two distributions (eg, settlement amounts with and without date of first exposure to determine insurance carriers' liabilities in a litigation case)
Population estimation - estimates the value of a parameter (eg, aircraft fleet size and flight hours, based on sample survey data)
Log linear models - 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)
Survival analysis - focuses on time as the variable of interest, such as the study of the 'survival' of new businesses or the 'survival' times of products (eg, in quality control research for automobiles, the 'survival' of the parts of a product under stress - reliability and failure-time analysis)
