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The Way We Pull Your Data Together Is What Sets Us Apart

Artemis Crane utilizes some of the most advanced tools for quantifying and analyzing market data.  Years of training in advanced statistical methodologies allow for our consultants to provide you data that is accurate and relevant.

Our commitment to data quality is your satisfaction in knowing that you can make important decisions for your business based on true facts. We look forward to sharing this commitment with you as we create innovative ways to generate the market knowledge you require.

For each and every one of our clients, we use a wide range of analytical techniques. Combining several techniques into your study will allow us to be absolutely sure that your data is significant and accurate.  Please click on the different types of analysis below to get more information on what we can offer you.

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Correlation Analysis: Correlation analysis, expressed by correlation coefficients, measures the degree of linear relationship between two variables.

Regression Analysis: Regression analysis measures the strength of a relationship between a variable you want to explain (e.g., buying behavior) and one or more explanatory variables (e.g., product quality perceptions).

Factor Analysis: Factor analysis aims to describe a large number of variables or questions by only using a reduced set of underlying variables, called factors. It explains a pattern of similarity between observed variables. Questions which belong to one factor are highly correlated with each other.

Cluster Analysis: Cluster analysis is an exploratory tool designed to reveal natural groupings within a large group of observations. Cluster analysis segments the survey sample (i.e. respondents or companies) into a small number of groups based on similar responses.

Correspondence Analysis (Perceptual Mapping): Correspondence analysis is a technique which allows rows and columns of a data matrix (e.g. average satisfaction scores for several products) to be displayed as points in a two-dimensional space or map. It reduces a complicated set of data to a graphical display which is immediately and easily interpretable. Perceptual maps used in brand positioning are often based on correspondence analysis.

Conjoint Analysis: Market research is frequently aimed at discovering which aspects of a product or service are most important to customers.  The ideal product or service, of course, would have all the best characteristics, but realistically, trade-offs have to be made. Conjoint analysis is often used in product development studies to help managers make product and pricing decisions.

Discriminant/Logistic Regression Analysis: Discriminant and logistic regression analysis are statistical techniques that highlight differences between two or more groups (e.g., buyers and non-buyers) based on respondent characteristics and perceptions.

Multidimensional Scaling: Multidimensional scaling (MDS) can be considered to be an alternative to factor analysis. In general, the goal of the analysis is to detect meaningful underlying dimensions that allow the researcher to explain observed similarities or dissimilarities in the data that are driving customer responses.

Structural Equation Modeling: Structural Equation Modeling (SEM) is a very general, very powerful multivariate analysis technique that includes a number of other traditional analysis methods as special cases. It effectively includes a whole range of standard multivariate analysis methods, such as regression, factor analysis and analysis of variance.  This method is typically used for advanced regression-type analysis, but is preferred to basic regression because it accounts for error in deriving path estimates.

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Questions regarding our data analysis approach or methods? Please contact us by clicking below.