IBM SPSS Statistics vs SAS Analytics comparison

IBM and SAS are both solutions in the Data Mining category. IBM is ranked #2 with an average rating of 9.0, while SAS is ranked #5 with an average rating of 8.5. IBM holds a 16.8% mindshare in DM, compared to SAS’s 8.1% mindshare. Additionally, 89% of IBM users are willing to recommend the solution, compared to 95% of SAS users who would recommend it.

IBM SPSS Statistics

Read 40 IBM SPSS Statistics reviews

4,646 Views
1,858 Comparison Views

89% willing to recommend

SAS Analytics

Read 13 SAS Analytics reviews

1,254 Views
1,241 Comparison Views

95% willing to recommend

IBM SPSS Statistics

SAS Analytics

Comparison Buyer's Guide

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Executive SummaryUpdated on May 21, 2025

SAS Analytics and IBM SPSS Statistics compete in the data analysis category. IBM SPSS Statistics tends to have the upper hand due to superior features despite SAS Analytics offering better pricing and support.

Features: SAS Analytics is recognized for advanced analytics capabilities, predictive modeling, and handling large data sets. Meanwhile, IBM SPSS focuses on statistical analysis, complex data manipulation, and is user-friendly for non-technical users.

Room for Improvement: SAS could enhance its user interface to be more intuitive, improve integration with emerging data platforms, and offer a more affordable subscription model. IBM SPSS might benefit from expanding customization options, increasing processing speed for larger data sets, and refining their customer support response times.

Ease of Deployment and Customer Service: SAS offers a customizable deployment with smooth setup due to flexible options and integrated support. IBM SPSS provides a standard deployment with easily accessible support, focusing on quick implementation.

Pricing and ROI: SAS Analytics has higher initial costs but offers beneficial ROI with its robust capabilities. IBM SPSS operates on a subscription model, giving short-term affordability and value with a comprehensive feature set.

To learn more, read our detailed IBM SPSS Statistics vs. SAS Analytics Report (Updated: April 2026).

Buyer's Guide

IBM SPSS Statistics vs. SAS Analytics

April 2026

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Helped 892,868 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

IBM SPSS Statistics

Ranking in Data Mining

2nd

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Science Platforms (11th), AI Data Analysis (16th), AI Research (5th)

SAS Analytics

Ranking in Data Mining

5th

Average Rating

9.2

Reviews Sentiment

5.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2026, in the Data Mining category, the mindshare of IBM SPSS Statistics is 16.8%, up from 16.5% compared to the previous year. The mindshare of SAS Analytics is 8.1%, up from 6.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining Mindshare Distribution
Product	Mindshare (%)
IBM SPSS Statistics	16.8%
SAS Analytics	8.1%
Other	75.1%

Data Mining

Featured Reviews

EzzAbdelfattah

Associate Professor Of Statistics at a university with 10,001+ employees

Advanced predictive analytics have supported my research and student projects across many methods

The only function I may need to be added or hope to be added to IBM SPSS Statistics is how to treat unstructured data. This mainly exists with IBM SPSS Modeler, but I do not think it is able to treat something like videos and similar content unless you are using languages like Python inside IBM SPSS Modeler or inside IBM SPSS Statistics. For the menu itself, for the selection, it does not exist. Thinking of the future, I believe that the owners of IBM SPSS Statistics should think about improving the package itself to be able to treat unstructured data.

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Sanjay Charitesh Makam

Student at Rochester Institute of Technology

Data management has become faster and clearer but integration and large data handling still need work

SAS Analytics needs to improve for entry-level people in my college because buying licenses is somewhat costly for small businesses. SAS Analytics is also somewhat complex for students who are entering the data field. Additionally, SAS Analytics is not good at integration with other environments, so it should improve on that, and some syntaxes are not good. SAS Analytics has an older architecture in some areas, so it should enhance the UX side and performance in some aspects. SAS Analytics documentation should improve to be clearer for students who are currently using or trying to use SAS Analytics, so the documentation should be simplified. Customer service is pretty good as of now, as they respond in two to four hours on business days, but faster response times may solve many problems and attract more users. I rate SAS Analytics seven or eight out of ten. SAS Analytics should improve in massive data handling, as I mentioned earlier. SAS Analytics struggles when it handles massive data, and it should also get top-tier support. The support should improve from two to four hours down to one hour or even thirty minutes. SAS Analytics should also offer more low-code options for those who do not know how to code or to save more time, and they should consider reducing their pricing. Currently, SAS Analytics has limited flexibility and very limited integration, so improvements in those areas are necessary. SAS Analytics student version does not handle long or lengthy data well right now, so it should improve on that point. As I have mentioned, it struggles with larger data sets, but for current small or medium data sets, it is very good. Customer support should improve to fifteen to thirty minutes or immediate live agents during business hours. The two to four hours wait can sometimes take more than one day, and that does not make sense. We have to stop all our work until they reply if we encounter critical problems, so they should improve for certain.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"If you want to do a quick analysis, SPSS is quite robust and quicker in terms of providing the output."

"I told you I wrote five books about using SPSS, so of course, I'm totally impressed with this package more than any other one."

"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."

"It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation."

"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."

"Capability analysis is one of the main and valuable functions, and we also do some hypothesis testing in Minitab and summary stats; these are the functions that we find very useful."

"I absolutely like the product, and that's the reason I recommend all of my students buy SPSS."

"It is perfectly adequate if all you need are the results and not the trail of evidence."

More IBM SPSS Statistics pros

"The most valuable aspect of SAS Analytics is the ability to automate work using macros without heavily relying on technology teams, allowing me to create functions that operate independently and streamline workflows efficiently."

"I think that the cost-benefit ratio is okay, it is the right balance."

"It has improved the level of efficacy and validity of our reports."

"The team immediately resolves the issues."

"We have noticed at least a thirty to forty percent saving in time and a thirty to forty percent increase in the efficiency of our work."

"It has facilitated timely analysis results with quality work and meaningful output."

"I use it to replicate our entire financial system to verify/duplicate calculations."

"All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team."

More SAS Analytics pros

Cons

"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."

"I know that SPSS is a statistical tool but it should also include a little bit of analytical behavior; you can call it augmented analysis or predictive analysis, but the bottom line is it should have more graphical and analytical capabilities."

"I'm unsure if SPSS has a commercial offering for big servers, unlike KNIME, which does."

"The technical support should be improved."

"It could allow adding color to data models to make them easier to interpret."

"Perhaps in terms of visualization, it's not really easy to do some data visualization, just simple, descriptive analysis in SPSS."

"It would be helpful if there was better documentation on how to properly use the solution. A beginner's guide on how to use the various programming functions within the product would be so useful to a lot of people."

"I do think that the design of the experience can be improved."

More IBM SPSS Statistics cons

"This solution should be made more user-friendly. It needs a better visual output to explain logic to senior executives."

"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."

"In the graphical area much more improvement is still needed."

"The installation could also be easier, and the price could be better."

"I would like to see their interface to R added to either Base SAS or SAS Analytics."

"SAS Analytics could improve in areas such as handling stress tests and workloads, as well as predicting financial movements, specifically profit and loss movements."

"It is relatively expensive."

"SAS is very expensive. The challenge with them is that you have to pay for a license with every module you buy."

More SAS Analytics cons

Pricing and Cost Advice

"We think that IBM SPSS is expensive for this function."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."

"The price of this solution is a little bit high, which was a problem for my company."

"The price of IBM SPSS Statistics could improve."

"It's quite expensive, but they do a special deal for universities."

"I rate the tool's pricing a five out of ten."

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

More IBM SPSS Statistics pricing and cost advice

"I rate the product’s pricing a one out of ten."

"The cost for SAS Business Intelligence can prove to be a little prohibitive."

"Setup costs were quite reasonable."

"I think that the cost-benefit ratio is okay."

"Licensing was rather straightforward."

"Prices were comparable with alternative solutions."

"Our licensing covers the usage for around 50 data analysts."

"It is relatively expensive. It is not an easy software to afford."

More SAS Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Manufacturing Company

Computer Software Company

University

Financial Services Firm

25%

University

15%

Construction Company

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	6
Large Enterprise	20

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	11

Questions from the Community

What is your experience regarding pricing and costs for IBM SPSS Statistics?

I think the price of the solution is very reasonable. The cost depends; you have the option for subscription or you can purchase the license. Most of our customers are paying every year for a type ...

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What needs improvement with IBM SPSS Statistics?

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What is your primary use case for IBM SPSS Statistics?

I do use IBM SPSS Statistics, and even my students are using it for their projects and reports while working on PhD or Master's degrees. They are analyzing data using it. In comparison with other s...

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What do you like most about SAS Analytics?

I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app.

See all answers

What needs improvement with SAS Analytics?

With SAS Analytics, I find that the graphs are very poor, and since I use Power BI, I think SAS Analytics needs to enhance the visualization options to do better visualizations. Many other software...

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What is your primary use case for SAS Analytics?

I work with SAS Analytics solutions, specifically with SAS Data Mining, because I extract information from the core system of the bank via ODBC connections and use SAS Base on the servers. I am the...

See all answers

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 31% of the time

Databricks vs IBM SPSS Statistics

Compared 6% of the time

KNIME Business Hub vs IBM SPSS Statistics

Compared 4% of the time

Altair RapidMiner vs IBM SPSS Statistics

Compared 4% of the time

Alteryx vs IBM SPSS Statistics

Compared 3% of the time

More IBM SPSS Statistics Competitors

KNIME Business Hub vs SAS Analytics

Compared 14% of the time

OpenText Intelligent Classification vs SAS Analytics

Compared 13% of the time

IBM SPSS Modeler vs SAS Analytics

Compared 11% of the time

SAS Enterprise Miner vs SAS Analytics

Compared 11% of the time

Lytics Conductor vs SAS Analytics

Compared 11% of the time

More SAS Analytics Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

April 2026

Download IBM SPSS Statistics product report

Buyer's Guide

SAS Analytics

April 2026

Download SAS Analytics product report

Also Known As

SPSS Statistics

No data available

Overview

IBM SPSS Statistics is renowned for its intuitive interface and robust statistical capabilities. It efficiently handles large datasets, making it essential for data analysis, quantitative research, and business decision-making.

IBM SPSS Statistics offers extensive functionality supporting both beginners and experts. It is used for data analysis across industries, accommodating advanced statistical modeling such as regression, clustering, ANOVA, and decision trees. Users benefit from its quick model building and ease of use, which are indispensable in data exploration and decision-making. Room for improvement includes charting, visualization, data preparation, AI integration, automation, multivariate analysis, and unstructured data handling. Enhancements in importing/exporting features, cost efficiency, interface improvements, and user-friendly documentation are sought after by users looking for alignment with modern data science practices.

What are IBM SPSS Statistics' most notable features?

Intuitive Interface: Simplifies navigation for both beginners and experts.
Predictive Analytics: Advanced modeling capabilities for future trends.
Robust Statistical Functions: Includes regression, clustering, ANOVA, and decision trees.
Large Dataset Handling: Efficiently manages complex and sizable data.
Customizable Tables: Users can create tailored reports and exports.

What benefits and ROI do users report in reviews?

Ease of Use: Streamlined processes for rapid analysis.
Advanced Functionality: Comprehensive tools for diverse research needs.
Quick Model Building: Speeds up predictive and statistical analysis.
Versatile Application: Suitable for academic, business, and social science research.

IBM SPSS Statistics is implemented broadly, including academic research for in-depth studies, business analytics for informed decision making, and in the social sciences for comprehensive data exploration. Organizations utilize its advanced features like AI integration and automated modeling across sectors to gain actionable insights, streamline data processes, and support research initiatives.

IBM

SAS Analytics offers a powerful suite of tools for statistical analysis, predictive analytics, and data handling, making it ideal for industries requiring robust data-driven decisions. Its extensive capabilities cater to professionals familiar with SQL and demand forecasting needs across sectors.

With a strong presence in analytics, SAS Analytics provides a seamless experience for data preparation, exploration, and reporting. Users benefit from its ability to handle large data sets, generate interactive reports, and integrate with multiple platforms. Despite its high costs and need for improved visualization and natural language querying, SAS Analytics remains a favored choice for those requiring comprehensive statistical modeling and risk analytics. Enhancing self-service analytics and accelerating support response times are areas of needed improvement. Companies use it extensively for business intelligence and demand forecasting, particularly in sectors like banking and financial services.

What are the key features of SAS Analytics?

Interactive Reports: Embeds intuitive and engaging reports.
Extensive Analytics Functions: Offers a wide range of analytics capabilities.
SQL Compatibility: Streamlines use for SQL-proficient users.
Predictive Analytics: Enhances analysis with SAS E-Miner.
Automation: Utilizes macros for automated tasks.
Large Data Handling: Manages extensive data effectively.

What benefits can users look for in reviews?

Increased Productivity: Saves time with automation and efficient data processing.
Enhanced Efficiency: Direct data source connections streamline workflows.
Versatility: Adapts to diverse analytical and reporting requirements.
Comprehensive Resources: Provides extensive help and support materials.

SAS Analytics is widely implemented in industries for tasks like national auto insurance pricing, financial replication, and marketing analytics. Teams in banking and financial services apply it for quantitative analyses, risk assessments, and generating detailed operational reports, demonstrating its adaptability and strength in handling complex data scenarios.

SAS

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

Aegon, Alberta Parks, Amway China, Axel Springer, Bank of America, Belgium Special Tax, CAP Index, CareSource, CBE Group, Cemig, Center for Responsible Lending, CESCE, Ceska sporitelna, Chantecler, Chico's, Chubb Group of Insurance Companies, CIGNA Thailand, City of Wiesbaden, Germany, Confused.com, Creditreform, Des Moines Area Community College, Deutsche Lufthansa, Directorate of Economics and Statistics, DIRECTV, Dow Chemical Company, Dow Chemical Company, Dun & Bradstreet, EDF Energy, Electrabel GDF SUEZ, ERGO Insurance Group, Erste Bank Croatia, Farmers Mutual Group, Finnair, Florida Department of Corrections, Geneia, Generali Hellas, Genting Malaysia Berhad, Grameenphone, Grandi Salumifici Italiani, HealthPartners, Highmark, Hong Kong Efficiency Unit, HP, Hyundai Securities, Illinois Department of Healthcare and Family ServicesInc Research, ING-DiBa, Institut Pertanian Bogor, InterContinental Hotels Group (IHG), IOM, Kelley Blue Book, Lenovo, Lillebaelt Hospital, Los Angeles County, Maspex Wadowice Group, National Bank of Greece, New Zealand Ministry of Health, New Zealand Ministry of Social Development, Nippon Paper, NMIMS, North Carolina Department of Transportation, North Carolina Office of Information Technology Services, Northern Virginia Electric Cooperative (NOVEC), Oberweis Dairy, ODEC, Ohio Mutual Insurance Group, Oklahoma State University, OneBeacon, Orange Business Services, Orange County Child Support Services, Organic, Orlando Magic, OTP Bank, Plano Independent School District, Project Odyssey, Royal Society for the Protection of Birds, RSA Canada, SCAD, Scotiabank, Singapore National Library Board, Sobeys Inc., SRA International, Staples, Statistics Estonia, Swisscom, SymphonyIRI Group, Telecom Italia, Telef‹nica O2, Town of Cary, Transitions Optical, TrueCar, Turkcell Superonline, UniCredit Bank Serbia, University of Alabama, University of Missouri, USDA National Agricultural Statistics Service

Buyer's Guide

IBM SPSS Statistics vs. SAS Analytics

April 2026

Free Report: IBM SPSS Statistics vs. SAS Analytics

Find out what your peers are saying about IBM SPSS Statistics vs. SAS Analytics and other solutions. Updated: April 2026.

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