Frost & Sullivan utilizes state-of-art quantitative techniques to derive deep and valuable insights from market research data. Similar to other research firms, Frost & Sullivan surveys customers using typical survey methodologies (e.g., telephone interviews, Web-based, etc.). Yet, unlike other research organizations, Frost & Sullivan provides strategically developed analysis plans that create solutions for your company’s challenges. This includes the derivation of generally unobservable or latent customer motives and market dynamics that nonetheless have a significant impact on customer attitudes, preferences, and behaviors. Generally accepted methodologies provide the necessary robustness, validity, and reliability to research projects, but we take the research one step further by providing various quantitative analytical techniques.
Listed below is a sampling of the quantitative analytical techniques we use:
Multivariate analysis is generally the workhorse for most customer research projects at Frost & Sullivan. The following are the most commonly used tools:
- Descriptive and Comparative Tools - At the initial stages of analysis, various data exploration tools like the comparison of means and the analysis of variance are used to determine significant differences across various factors
- Data Reduction - Multi-item scales are also fine-tuned using factor analysis to both the convergent and discriminant validity of the scales. Factor analysis is also used to discover latent or unobservable factors underlying a set of observable factors.
- Cluster Analysis and Perceptual Mapping Tools - For market segmentation, cluster analysis, perceptual mapping tools (like correspondence analysis), and multi-dimensional scaling are generally used.
- Regression Analysis Tools - Linear regression, logistic regression, and similar quantitative tools are used to evaluate predictive relationships between factors for relatively non-complex models. This is also used to identify underlying drivers of satisfaction and purchase, which may be significantly different from what consumers explicitly state.
Structural Equation Modeling
For more complex models that capture the multi-directional and dynamic interaction among several variables, Frost & Sullivan uses structural equation modeling (SEM). Such models, which can potentially provide both descriptive and predictive value, can be the basis for the formulation of indexes that measure the relative performance of companies and business units.
Conjoint Analysis is an experimental quantitative technique that simulates decision situations customers face. Choice-Based Conjoint (CBC), Adaptive Conjoint Analysis (ACA), and MaxDiff are the most commonly used tools. These types of analysis provide insights that help companies decide on the optimal set of features that will deliver higher levels of satisfaction to the customer.
Frost & Sullivan is virtually unlimited in our ability to utilize statistical and mathematical tools to create solutions through quantitative research.