SPSS MODELER

Data Science with the

IBM SPSS Modeler

With SPSS Modeler, you can rely on a proven data science tool for mastering analytical tasks. The intuitive drag-and-drop interface enables particularly user-oriented users to apply their expertise in data science projects and still use powerful machine learning algorithms. use powerful machine learning algorithms. These can also be used effectively with large volumes of data thanks to the client/server architecture.

Data Science by drag-and-drop. Turn our expertise to your advantage

As an IBM Gold Business Partner, we support you in setting up a modern data science platform in your company and are at your side during the implementation of advanced analytics projects. In doing so benefit from our many years of cooperation with IBM as well as our experience from past, trusting and partnership-based cooperation in analytics topics with our customers. customers.

Best practice examples,

where where we have successfully used the SPSS Modeler with our customers with our customers:

  • Support for the marketing department in optimizing their cross-selling campaigns
  • Development a feature store for the regular provision of variables for machine learning models
  • Development of a customer churn model for various product categories and the continuous provision of up-to-date forecasts
  • Detection of anomalies in log files to support the R&D department in the development and quality control of new products

data stream in the IBM SPSS Modeler

Complex Algorithms Simple Benefits

SPSS Modeler supports a variety of modern machine learning algorithms that cover a wide range of business cases. In addition to methods for classic regression and cluster analysis, association models such as those used in market basket analysis can also be trained.

The benefits that our customers have long appreciated about SPSS Modeler include

  • Intuitive options for explorative data analysis of your company data
  • Analysis of unstructured text data, such as emails or customer feedback for keyword and sentiment extraction
  • Extension of
    ready-made modeling options with your own Python and
    R functions
  • Deployment
    in SPSS's own 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
Analytics & Insights
silvio.bergmann@isr.de
+49(0)151 422 05 418

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