Cross-Tab's statistical data analysts review data to detect trends or patterns, thereby deriving insights and shape business strategy. Analytics services involve sophisticated analytics procedures and a wide variety of statistical methods and models. Some of the more commonly used multivariate analysis techniques include:
Multiple and Logistic Regression
Statistical Tests
Factor Analysis
Cluster Analysis and Market Segmentation
Conjoint Analysis
Discriminant Function Analysis
Multidimensional Scaling
Econometric and Statistical Modeling
We have undertaken market research data analytics for segmentation studies, concept testing, advertising-related studies, pricing studies, continuous tracks, customer satisfaction studies, churn analysis, and for several other study types.
Study type: In-depth analysis of IT spending trends
Client: A Fortune 100 company
Nature of the study:
Merging, aggregation and analysis of data and generation of reports
The data was disaggregate across several vendors - with variations in the questionnaires and inconsistency in variable names and value labels
What we did:
Data Integration and Merging
Models / Integrative Analysis
Trend Analysis and Forecasting
Forecasting Analysis
Regression Analysis
Segmentation and Clustering
Secondary research
To map the findings of primary research results and to provide more information on IT spending trends
How did this help the client:
Key segments and their influencers and behavior patterns were identified
Regression analysis revealed a weak relationship between the predictor variables and the dependent variable (next year IT spending outlook)
Cross-Tab suggested and executed CHAID algorithms that resulted in several key insights on spending patterns
Cross-Tab carried out additional secondary research on spending trends over the last three years and mapped it against the primary research results to give deeper insights
