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MSA's experience and analytical capabilities allow for an in-depth
analysis of survey response data. From basic crosstabs and stub
& banner tables to extensive multivariate analysis and modeling,
MSA can provide the knowledge and expertise to help clients to
truly understand the insights offered by their data. We are able
to apply all of our techniques and methodologies to data from surveys
that we designed or surveys from a third-party source.
Software and databases used by MSA for the analysis of survey data include:
- Asteroid (Asteroid software and custom Asteroid databases
can be created for clients to run their own analyses)
- Insight
- SPSS
- SAS
- Quanvert
- Data files can be sent and received in virtually any format
(ASCII, .txt, .dat, .sav, .sas7bdat, .xls, etc.)
Typical survey research analyses conducted for clients include:
- Extensive crosstabs and stub & banner tables
- Correlation analysis
- Factor analysis and principal components analysis (PCA)
- Cluster analysis (hierarchical and k-means)
- Conjoint analysis
- Discriminant analysis
- Perceptual mapping
- Regression modeling
- Correspondence analysis
- Canonical correlation analysis
- Multidimensional scaling (MDS)
- CHAID and MAID (Multivariate Automatic Interaction Detection)
- Structural equation modeling (SEM)
- LoMA (Longman Moran Analytics): LoMA is a series of marketing models at MSA's disposal. Originally developed by Bill Moran, LoMA consists of the following modules:
- Market Driver Analysis (MDA): What attribute dimensions drive brand share for the category or segment as a whole? To what extent do attribute differences explain differences in share?
- Brand Driver Analysis (BDA): How strong is my brand franchise and in which segments do I compete (or fail to compete) most effectively? And what are the long term prospects of the brand?
- Competitive Distance Analysis (CDA): To what degree does my brand compete directly with other brands in the category and what brand attributes or other influences are driving the competition?
- Market Structure Analysis (MSA): What is the hierarchy of choice dimensions used by consumers when purchasing products in a category, i.e., what is the "consumer decision tree" structure?
- Please click here for more information regarding MSA's Consumer Choice Analytics.
MSA's ability to uncover the insights in survey responses means a
greater return for our clients' research investments.
An example of Structural Equation Modeling to uncover
latent dimensions underlying and impacting brand performance.
A client can use these insights to better leverage brand equity
to achieve greater market share or volume:
Perceptual map of brands in a category from a brand-attribute
matrix using correspondence analysis (bubble sizes for the attributes
represent overall importance). Three potential market segments with
unique points of differentiation are shown:
MAID Tree Diagram of category purchasers used to identify
segments of opportunity. In addition to demonstrating purchase intent
for heavy brand purchasers, the diagram also uncovers a hidden
segment of competitive purchasers who have expressed strong interest
in the brand:
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