The CLEMSTAT solution is a statistical toolkit, complemented with industry-specific syntax and macro sets, which allow you to perform industry-specific analyses in market research and science, as well as for the financial, telecom, and government sectors. With built-in case studies and a 'Statistics Coach', CLEMSTAT provides effective support at every step of data analysis, from data preparation to data manipulation and modelling.

CLEMSTAT includes a combination of the following IBM SPSS Statistics modules on demand:

• SPSS Statistics Base
• Regression: allows you to build much more accurate predictive models that go beyond traditional linear regression approximations
• Advanced Statistics: allows complex relationships to be explored using powerful multivariate techniques
• Decision Trees: visual classification, decision trees to identify individual subgroups and explore relationships in the data
• Custom Tables: display results in a presentation-ready tabular format with a user-friendly interface
• Exact Tests: more accurate analyses on small samples and rare occurrences in large databases
• Categories: detect unknown relationships between categorical data using efficient scaling and dimensionality reduction techniques
• Forcasting: forecasting by analysing historical data, modelling, and using powerful time series analysis techniques
• Conjoint: analysing consumer preferences for more effective product development
• Missing Values: solving missing data problems by quickly diagnosing the situation and managing the data accordingly for more robust results
• Data Preparation: lightweight, automated data validation instead of labor-intensive manual verification for more accurate results
• Complex Samples: designing and analysing non-random surveys and studying the population efficiently and accurately
• Direct Marketing: : includes the tools needed for a sophisticated analysis of customer data, such as RFM analysis, segmentation, profiling.
• Bootstrapping: helps to reduce the effects of outliers and anomalies that would reduce the accuracy and applicability of analyses.
• Neural Networks: enables non-linear modelling processes to explore more complex data relationships than ever before.