IBM SPSS Modeler

IBM SPSS Modeler

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Score 9.5 out of 100
IBM SPSS Modeler

Overview

Recent Reviews

Good for all

9 out of 10
March 21, 2018
It is presently being used to develop a predictive model and to also carry out some statistical analyses. It makes it easier for employees …
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Pricing

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IBM SPSS Modeler Personal

4,670

On Premise
per year

IBM SPSS Modeler Professional

7,000

On Premise
per year

IBM SPSS Modeler Premium

11,600

On Premise
per year

Entry-level set up fee?

  • Setup fee optional
For the latest information on pricing, visithttps://www.ibm.com/us…

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services

Features Scorecard

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Product Details

What is IBM SPSS Modeler?

IBM SPSS Modeler is a predictive analytics platform that helps users build accurate predictive models quickly and deliver predictive intelligence to individuals, groups, systems, and enterprises. With an intuitive interface and drag-and-drop features, the software is designed to be easy to use with the goal of enabling everyone from business users to data scientists to uncover valuable insights quickly.

Organizations use IBM SPSS Modeler to find answers to their critical business questions, such as:

  • Which customers are likely to churn?
  • What products and services are prospects likely to purchase together?
  • Which insurance claims have a high probability of fraud?
  • Which machines on the assembly line are likely to fail?
  • Where do we need to deploy additional law enforcement resources?
  • How can we select the right applicants for the right roles?

IBM SPSS Modeler Features

  • Supported: Intuitive visual analysis streams
  • Supported: Broad range of advanced analytical techniques
  • Supported: Automatic data preparation

IBM SPSS Modeler Screenshots

Use a single run to test multiple modeling methods, compare results and select which model to deploy. Quickly choose the best performing algorithm based on model performance.Explore geographic data, such as latitude and longitude, postal codes and addresses. Combine it with current and historical data for better insights and predictive accuracy.Capture key concepts, themes, sentiments and trends by analyzing unstructured text data. Uncover insights in web activity, blog content, customer feedback, emails and social media comments.Use R, Python, Spark, Hadoop and other open source technologies to amplify the power of your analytics. Extend and complement these technologies for more advanced analytics while you keep control.

IBM SPSS Modeler Video

IBM SPSS Modeler Downloadables

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IBM SPSS Modeler Technical Details

Deployment TypesOn-premise
Operating SystemsWindows, Linux, Mac, AIX
Mobile ApplicationNo
Supported LanguagesEnglish, French, German, Italian, Spanish, Japanese, Simplified Chinese, Traditional Chinese, Brazilian Portuguese, Korean, Polish, Russian

Comparisons

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Frequently Asked Questions

What is IBM SPSS Modeler's best feature?

Reviewers rate Support Rating highest, with a score of 10.

Who uses IBM SPSS Modeler?

The most common users of IBM SPSS Modeler are from Enterprises (1,001+ employees) and the Unknown industry.

Reviews and Ratings

 (37)

Ratings

Reviews

(1-7 of 7)
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Tim Daciuk | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Modeler is used to analyze large amounts of data and to develop and deploy predictive models. The software will look for an appropriate model, assess the quality of models, and create predictions for new data. Modeler can be used in the analysis of numeric data and also with text data. Additionally, text and numeric data can be combined in models if appropriate.
  • Combine text and data
  • Provide facilities for all phases of the data mining process.
  • Use a node and stream paradigm to easily and quickly create models.
  • The graphics are weak.
Modeler is well suited for understanding consumer data. The ability to create a prediction and then to understand what is driving that prediction is strong in Modeler. Modeler is closely aligned with the CRISP-DM data mining approach meaning it is not just the 'doing' but also the theoretical background behind the development of data mining models.
Ben Holmes | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
IBM SPSS Modeler is the on-premise version of Modeler Flow, which we use in the Cloud Pak for Data as a Service application in IBM's cloud. Mainly, it is an interface based application with nodes that replaces the need to write code. In several instances, it can be a quicker method to designing a data science project. We use it for the Text Analytics node to assist with ad hoc NLP requests to turn over NLP in a quicker fashion than typical.
  • Fast code builder.
  • No need to maintain software versioning.
  • Visual code, easy to see what's happening.
  • Details of models and nodes requires some "digging", "clicking".
  • Customization of versions is hard to implement.
  • Latest open source code packages take time to integrate into the application.
Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.
Char Paul | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
As an online psychology and statistics tutor, research consultant, and uni student, I use SPSS daily to support undergraduate, postgraduate and lecturer learning of how to use the SPSS program and interpret findings; as well as using the program for in-house research, my assessments, and as a consultant for NGOs, small business and as a research assistant for academic staff. Problems addressed include: descriptive analyses, assumption checking, NHST, effect sizes, and building models such as with path analysis, SEM, moderation and mediation (using PROCESS), and visual representation of data; for lecture and tutorial reviews, research proposals and reports including theses, and to manage research projects from outside of academia.
  • Inputting data
  • Test selection
  • Data manipulation
  • Providing sandbox data sets
  • Two week trial for students considering purchase
  • Reasonable rent prices
  • Including MCAR in basic packages
  • Standardise language across dialogue windows
  • Enable 'undo' after sorting cases
  • Include Bayesian options
For those first learning to set up a dataset, analyse data and interpret output. Quantitative analysis. Not well suited to correlations which don't meet the assumptions of tests on SPSS, creating CIs for some regression analyses, qualitative analysis (I use NVivo).
The online support board is helpful and the free add ons are incredibly appreciated.
Akos Krommer, CISA, ACDA | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
IBM SPSS Modeler is used by our analytics department in order to perform management analysis and reporting, and also to perform financial control testing within the company. With the various statistical models and analytical tools, we can use this product for data discovery and exploration, then analyze the raw data and find patterns in it that help decision makers in their business decisions.
  • A very nice and easy to use interface.
  • A great variety of analytics, from statistical calculation to data validation and predictive statistics.
  • Has a steep learning curve.
  • Has very old style graphs, with lots of limitations.
  • Some advanced statistical functions cannot be done through the menu.
  • The data connectivity is not that extensive.
  • It's an expensive tool.
SPSS Modeler is very well suited for researchers (especially in the social area) or for those who want to find patterns in a big dataset. It has very colorful statistical and analytical features, so SPSS is perfect to analyze data and find patterns in it. Unfortunately, it has very limited and outdated visualization capabilities, so IBM SPSS can be used to produce data, but not to visualize it. It is recommended to use other tools for visualization.
Himanshu Singh | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
IBM SPSS Modeler is extensively used by our data analytics team. It is used for customer analytics to formulate and understand retention and engagement metrics, and used to analyze our CRM. It gives us a good amount of insights that help us to with easy and quick decision making. The business people use it for business problems and they don't have to have technical understanding.
  • The model formulation from large amount of structured and unstructured data is commendable.
  • It has a beautiful and intuitive new UI.
  • The manual is very easy to understand. Hence, reduces the on boarding.
  • The natural language processing (NLP) needs improvement.
  • Already present integrations to other IBM products is poor.
  • There are many things like Text analytics which are only available in gold edition.
  • It is hard for a non-tech person to implement advanced modelling which requires Python, R.
If your analytics is research oriented, coming with cases and hypothesis, and you have the sound technical knowledge of Programming which is used in data mining (Python, R), then this tool will give you a huge advantage irrespective of whether you have structured or unstructured data. However, if your organization doesn't have the data to work upon, this product won't be able to help you further.
Jesús Quintana | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Our company has 20+ years of developing solutions based on SPSS tools, one of them is IBM SPSS Modeler. In our experience, we have seen companies using Modeler across the whole organization to support critical business process. The versatility of the tool and easy deployment make it a first choice in organizations from Government to Retail, Finance, and Academics. Being able to reduce churn, to retain customers, forecast sales and inventory stock all within a friendly and powerful user interface is something that customers are really looking forward when it comes to accelerating ROI.
  • GUI is really well accomplished and friendly, almost everyone with little investment in training can take advantage of the tool.
  • Escalability, you can grow your investment in licensing according to your actual needs, from an annual authorized user, to perpetual concurrent and Big Data and Machine Learning capabilities.
  • Open Sorce Ready: take leverage of all your developments made in R or Python and deployment all over the organization even with the user who isn´t used to code.
  • Some Analyses aren't there out of the box but can be added through open languages like R and Python.
  • Graphs could be better.
  • Unable to read data stored in OLAP databases
Modeler is well suited for Retail, Credit Scoring, Telcos, Government. And less suited when it comes to transactional environments.
March 21, 2018

Good for all

Olayinka Awoyemi | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
It is presently being used to develop a predictive model and to also carry out some statistical analyses. It makes it easier for employees who are statistically or mathematically oriented to learn and use the software for various modeling exercises and assignments. It makes visualization of the modeling process to observe and follow. It is also compatible with available architectural facilities.
  • Predictive modeling
  • Scheduling of tasks
  • Statistical analyses.
  • Too much foreign programming software
Predictive modeling and analytics.
Data preparation and data presentation.
Preparing staging tables and platforms for other applications.
Statistical data analysis, including data manipulation and migration.
It's been a great deal