I find IBM SPSS Statistics very useful for statistical analysis and working with datasets without having to write lots of code. It makes it easy to perform statistical analysis and manage complex datasets. I'm impressed with its ability to give me accurate statistical analysis in minutes, something that would take a lot of time to do manually. Review collected by and hosted on G2.com.

Nothing Review collected by and hosted on G2.com.

I like its user-friendly interface, powerful statistical analysis features, and ability to handle complex datasets with minimal coding. Review collected by and hosted on G2.com.

It can feel expensive, resource-heavy, and less flexible than coding-based tools for advanced customization. Review collected by and hosted on G2.com.

The simplicity of the interface and the ability to edit the data within the interface. Additionally, the integration of coding understanding enhances the data analysis. Altogether, the analysis every time is adequate without any lag or error from the software. Review collected by and hosted on G2.com.

Perhaps the graph system and the accessibility related to creating the visualization from the data analysis. Review collected by and hosted on G2.com.

What I like most is the professional rigor, paired with how easy SPSS is to use. The biggest standout for me is the “point-and-click” interface for complex math. As a marketer, you may need to run a Cluster Analysis to identify customer segments or a Conjoint Analysis to understand which product features people actually value. In tools like R or Python, you typically have to write code to do this; in SPSS, you can simply select the variables from a menu. That makes high-level data science much more accessible for marketers who aren’t necessarily programmers.

The data management and cleaning capabilities are also far better than what you can do in standard spreadsheets. SPSS is built to handle “messy” survey data, like when respondents skip questions or provide inconsistent answers. It includes built-in options to flag outliers, handle missing values, and recode variables (for example, turning “Age” into “Age Brackets”) across thousands of rows in seconds, which helps ensure the final report is actually accurate.

I also really like the Direct Marketing Module. It’s a dedicated set of tools within SPSS designed specifically for marketing use cases. It lets you run RFM Analysis (Recency, Frequency, Monetary) to identify your most loyal customers, along with “Propensity to Purchase” modeling. Instead of guessing who to email, you can use statistics to predict which customers are most likely to buy. Review collected by and hosted on G2.com.

What I Dislike: Dated Aesthetics and High Cost

My biggest immediate dislike is the outdated user interface (UI). SPSS looks and feels like software from the early 2000s. Even though it’s functional, it doesn’t have the modern, sleek design you get with tools like Canva or Monday.com. That “gray box” vibe can make the software feel more intimidating and a lot less “fun” to use, especially during long data-crunching sessions.

Another recurring frustration is the limitation around visualization. SPSS can generate charts and graphs, but they often come out looking overly “academic” and dry. If you’re a marketer who needs to put a polished deck in front of a CMO, you’ll almost always end up exporting the data to something like Tableau, Power BI, or even just Excel to get visuals that look brand-compliant and more modern.

Finally, price and performance on big data can be a real barrier. SPSS is expensive and often comes with a significant annual license fee that can be tough for smaller marketing teams to justify. On top of that, if you’re trying to crunch “Big Data” (millions of rows from web traffic or live social feeds), it can get sluggish or even crash. It feels like it was originally built for structured, survey-style datasets, not massive, real-time data streams. Review collected by and hosted on G2.com.

I used IBM SPSS Statistics to help with the analysis of survey data during my PhD research. I found it easy to navigate and the analysis led to strengthening the results section of my PhD thesis. I consider the customer support to have been of a particularly high standard. Review collected by and hosted on G2.com.

I had no dislikes of this IBM package. Like every software package, a certain amount of orientation is required to fully utilise what the product can offer. Review collected by and hosted on G2.com.

I like that IBM SPSS Statistics helps make my work in UX more credible because it allows me to present data-backed decisions to higher-ups. Instead of just saying I feel an option is better, I can use SPSS to show that the data indicates one option performs better, and provide numbers, making our decisions more credible. Review collected by and hosted on G2.com.

I think that the UI is the biggest weakness. Being a designer, I feel that SPSS's UI looks old and cluttered. Some features are hard to discover, and it's really hard to begin as a beginner. The tables are dense. It's hard for stakeholders to interpret quickly, so maybe a UX summary output could help. Review collected by and hosted on G2.com.

The menu driven interface helps me run descriptive stats tests, crosstab views and chi square tests without a single line of code to be written Review collected by and hosted on G2.com.

The scale currently, with so much data rows the load time often increases and heavier procedures strain the memory Review collected by and hosted on G2.com.

I like IBM SPSS Statistics for its easy-to-use, menu-driven interface, which allows me to perform complex statistical analysis without needing to write code. The software's capability to present results in clear tables and charts makes interpretation simple and accurate. This is particularly beneficial for students and researchers working with large data sets. I appreciate how IBM SPSS Statistics efficiently handles complex statistical tests like descriptive statistics, regression, and ANOVA simply by selecting options from the menus. Additionally, the output viewer feature is very useful as it automatically organizes results into well-structured tables and charts, making data interpretation easier. I also find its strong data management tools, such as variable labeling and handling missing values, very helpful as they aid in cleaning and preparing data efficiently before analysis. Overall, these features make IBM SPSS Statistics a reliable and accurate tool for academic and research work. Review collected by and hosted on G2.com.

The cost and licensing are quite high, making it difficult for students and small organizations to access. The user interface feels outdated compared to modern analytics tools and could be improved to be more interactive and visually appealing. IBM SPSS Statistics also has limited flexibility and automation compared to programming-based tools like Python, especially for advanced raw custom analysis. Improving integration with other tools and adding more modern data evaluation options would enhance the software. The licensing and activation steps can be a bit confusing initially, particularly for students. The setup process could be improved by making the licensing and additional processes simpler and more user-friendly. Review collected by and hosted on G2.com.

I love that IBM SPSS Statistics allows you to run complex analyses like MANOVA or factor analysis without needing programming knowledge, thanks to its point-and-click interface. The 'Data View' vs. 'Variable View' setup is super helpful for keeping datasets clean, especially with a lot of survey responses. It's excellent for handling large datasets, much better than Excel. The 'Syntax' feature is fantastic for automating repetitive tasks without having to delve into full Python or R, making it much easier. SPSS is also very reliable, and I trust the math and the outputs, which means something in peer-review scenarios. The appeal of SPSS is its incredibly low barrier to entry; if you can use a menu, you can perform high-level statistics, which is impressive. It's an absolute workhorse for getting reliable statistical proof for business cases. If my friend is a researcher who understands statistics but hates programming, SPSS is a 10/10 because it allows running complex models with just a few clicks. Review collected by and hosted on G2.com.

The UI feels like it’s stuck in 2005, and it's clunky. The licensing process is a headache every time I have to renew, and the price is astronomical if my university or company isn't covering it. The visualizations look dated right out of the box, so I usually have to export the data to other tools like Tableau or PowerBI to make it look presentable. It can also be slow when working with truly 'Big Data' (millions of rows). The initial setup can be frustrating, especially the licensing part - even a small error in entering an authorization code can prevent activation. Review collected by and hosted on G2.com.

I like the ease of use and ease of administration Review collected by and hosted on G2.com.

I can't think of anything that I dislike Review collected by and hosted on G2.com.