Key features

Support for many data sources

SPSS Modeler can read data from flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop. You can extend the capabilities of SPSS Modeler to push back data processing with the SQL Optimization add-on (subscription) or the Analytic Server (perpetual license).

Visual analysis streams

SPSS modeler provides an intuitive graphical interface to help visualize each step in the data mining process as part of a stream. Now analysts and business users can easily add expertise and business knowledge to the process.

Automatic data preparation

SPSS Modeler automatically transforms data into the best format for the most accurate predictive modeling. It now only takes a few clicks for you to analyze data, identify fixes, screen out fields and derive new attributes.

Automated modeling

SPSS Modeler can test multiple modeling methods, compare results and select which model to deploy in a single run. This enables you to quickly choose the best performing algorithm based on model performance.

A range of algorithmic methods

SPSS Modeler offers multiple machine learning techniques — including classification, segmentation and association algorithms including out-of-the-box algorithms that leverage Python and Spark. Users can now employ languages such as R and Python to extend modeling capabilities.

Text analytics

SPSS Modeler captures key concepts, themes, sentiments and trends by analyzing unstructured text data. Now you can uncover valuable insights in blog content, customer feedback, emails and social media comments.

Geospatial analytics

Explore geographic data such as latitude and longitude, postal codes and addresses using SPSS Modeler. By combining that information with current and historical data you can generate better insights and improve predictive accuracy.

Support for open source technologies

SPSS Modeler enables the use of R, Python, Spark and Hadoop to amplify the power of analytics. You can also extend and complement these technologies for more advanced analytics while maintaining control.

Multiple deployment methods

IBM SPSS Modeler is available as part of IBM Data Science Experience, as well as a standalone subscription or perpetual offering. Using Modeler Gold, data scientists can schedule jobs to run at desired times. IT administrators can integrate deployment into existing systems for batch, real-time or streaming.

Machine learning methods and algorithms

SPSS Modeler supports decision trees, neural networks and regression models. Now you can take advantage of ARMA, ARIMA and exponential smoothing; transfer functions with predictors and outlier detection; benefit from ensemble and hierarchical models; support vector machine and temporal causal modeling; and employ time series and spatial AR for spatiotemporal prediction. Generative adversarial networks (GANs) and reinforcement also enable deep learning.

Create and train a machine learning model without coding

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Customer case study

  • Redcats Group extends predictive analytics to 17 brands with SPSS Modeler.

    Redcats Group

How customers use it

  • Use case: Acquire and Retain Customers

    Problem

    Predicting customer churn is difficult. Creating the right offers is challenging. Correlating staffing, products and other factors with customer acquisition is inefficient.

    Solution

    With SPSS Modeler, enterprises are delighting customers, building the right offers and aligning business needs while shrinking the time it takes to go from idea to experimentation, and then to production.

  • Use case: Build new offers and innovating business models

    Problem

    Understanding how customers are reacting to and acting on information is difficult. Creating the right offers for the right channels is challenging. Spending too much time with data and scripting the information inhibits efficiency and innovation.

    Solution

    From data preparation to applying machine learning algorithms, SPSS Modeler enables new ways of exploiting information. Now you can confidently create new offers, drive channel performance and optimize business processes for better team productivity.

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