Data Analytics
STATISTICAL ANALYSIS
The number of statistical analysis and visualization tools has exploded, yet there are a core set of capabilities that can be used in many situations to help explain consumer behavior and trends. InnoProbe utilizes many tools to assess consumer and B2B/industrial marketing research data, or sales data. We advise clients to design wisely, as the usefulness of any statistical method hinges on data quality and underlying assumptions. Our expertise includes:
- Analysis of Covariance: to adjust for the effects of other variables and permit analysis of variance to be used effectively.
- Analysis of Variance: to decompose variance into variance attributable to the effects of specific variables.
- Cluster Analysis: to form homogeneous groups (e.g., people, brands, markets).
- Canonical Correlations: to measure in precise quantitative terms the relationship between two sets of variables.
- Conjoint Analysis: to measure the importance of benefits or attributes, overall and for individual respondents.
- Discriminant Analysis: to classify people or objects into pre-defined groups.
- Factor Analysis: to reduce a set of inter-correlated items to a smaller set of independent items and underlying structure.
- Perceptual Mapping: to obtain a simplified picture of perceptions of brands and images.
- Regression Analysis: to measure the relationship between a dependent variable and one or more independent variables.
Consider discussing the details of your project with us before zeroing in on a specific technique (which may or may not be appropriate). This will maximize your overall research investment.
These techniques can be used in combination with many of the other designs described elsewhere on our website. InnoProbe has the expertise to utilize all of these tools. Call us to discuss your specific data analysis needs, and we can recommend the approach that is best suited for your marketing decision.
