SPSS MODELER

Data Science with

IBM SPSS Modeler

Leverage SPSS Modeler, a proven data science tool for tackling analytical tasks. Its intuitive drag-and-drop interface empowers user-oriented professionals to apply their expertise in data science projects while still utilizing powerful machine learning algorithms. Thanks to its client-server architecture, these algorithms can be effectively utilized even with large datasets.

Data Science via Drag-and-drop. Leverage Our Expertise to Your Advantage

As an IBM Gold Business Partner, we support you in establishing a modern Data Science platform within your organization and stand by your side during the implementation of advanced analytics projects. You benefit from both our long-standing collaboration with IBM and our experience derived from trusted and collaborative partnerships in analytics with our clients.

Best Practice Examples,

where we have successfully implemented SPSS Modeler for our clients:

  • Supporting the marketing department in optimizing their cross-selling campaigns
  • Establishing a feature store for the regular provision of variables for machine learning models
  • Developing a customer churn model for various product categories and continuously providing up-to-date predictions
  • Detecting anomalies in log files to support the R&D department in the development and quality control of new products

Data Stream in IBM SPSS Modeler

Leveraging Complex Algorithms with Ease

SPSS Modeler supports a wide array of modern machine learning algorithms that cover a broad spectrum of business cases. Beyond methods for classical regression and cluster analysis, it also enables the training of association models, commonly applied in market basket analysis.

Our clients have long valued the following advantages of SPSS Modeler, including

  • Intuitive capabilities for exploratory data analysis of your enterprise data
  • Analysis of unstructured text data, such as emails or customer feedback, to extract keywords and sentiments.
  • Extension
    of pre-built modeling capabilities with custom Python and
    R functions.
  • Deployment
    in SPSS-native and standard environments, such as Apache SPARK.
  • Execution of time- and event-based job schedules for regular batch processing.
Portrait Photo Silvio

Silvio Bergmann
Senior Manager
Data & Analytics
silvio.bergmann@isr.de
+49(0)151 422 05 418

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