Marketing Science CMI offers a full range of analytical services, from simple cross-tabulations to structural equation modeling. Having this expertise in-house ensures that the research objectives remain the focus throughout the entire process, from design through analysis and interpretation. Total Unduplicated Reach and Frequency (TURF) Originating in media campaign analysis, TURF is a useful tool in determining the optimal mix of products in a line extension. It takes into account “reach,” which represents the number of consumers interested in at least one product in the line, and “frequency,” which represents the average number of purchases per household (or business) for a product line, based on specific combinations of products in the line. Case Study: Brand and Category Extensions
Back to Top Conjoint Analysis/Discrete Choice Modeling “Trade off” approaches are extremely useful in understanding consumer decision-making. CMI conducts conjoint analysis and discrete choice modeling to assist in new product/service design, price sensitivity estimation, and market segmentation. Depending on client needs and the business objectives, analysis can be conducted at the aggregate level or the individual level (individual level analysis leads to more accurate prediction of market share). Case Study: New Packaging Development
Back to Top Structural Equation Modeling (SEM) Structural Equation Modeling (SEM) is a powerful analytical tool that depicts the causal relationships among a set of performance measures and latent traits underlying these measures. SEM allows for modeling both direct and indirect relationships simultaneously to determine the true dynamics of business processes. It deals with multicollinearity (high correlation among performance indicators) and estimates measurement error more accurately than traditional regression analysis. Within SEM, a wide variety of approaches are available for handling different types of data and data distribution, such as missing data, categorical data, and non-normal data. Case Study: Structural Equation Modeling
Back to Top Latent Class Analysis/Modeling (LCA) Latent Class Analysis/Modeling (LCA) is a statistical method used to categorize individuals into distinctive groups or classes based on their responses to a set of indicators. It does not rely on such traditional assumptions as linearity, normal distribution, and homogeneity. In addition, the categorical indicators do not have to be standardized on the same scale (i.e. the indicators can be a mix of dichotomous, trichotomous, and polytomous variables). Therefore, LCA is a more powerful and flexible analytic technique than factor analysis, cluster analysis, and regression analysis. The results from LCA add great insight into targeted business or marketing strategies. Case Study: Latent Class Analysis/Modeling
Back to Top QTabsTM QTabsTM, CMI’s online crosstab solution, was developed by CMI to meet the needs of corporate decision makers who want their questions answered now, without waiting for someone else to fit additional analysis into their schedule. With QTabsTM, users can access survey data via the Internet at their convenience, creating customized crosstabs “on the fly”. QTabsTM is flexible in design, and allows for significance testing, recoding of variables, and filtering. View QTabsTM demonstration
Back to Top
|