

Find out in this report how the two AI Data Analysis solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
I have seen a return on investment with Amplitude, saving about 120 man hours per month for a specific report that needs to be created.
It has saved us a lot of time since I can see the analysis as quickly as possible in the dashboard, resulting in significant time and money saved.
You can pursue answers whichever way you would prefer through the normal support routes or you can source it from the community that they offer on Slack.
There could be live chat support for different types of charges or solutions that would be more helpful.
Amplitude customer support is responsive.
Amplitude's scalability is fine; I have millions of active users, tens of millions, with high throughput, and it performs great.
Amplitude is very scalable, considering that we do not have to do any manual work ourselves.
Amplitude is quite scalable.
I did not notice any delays or issues with Amplitude's performance and speed when handling large datasets.
Support could be improved. Sometimes I need to create a ticket and communicate with one of their advisors via email.
Longer form time series analysis seems nearly impossible to do on this platform.
Reconciling clickstream data with Databricks or other AWS systems could help analysts spend less time verifying the accuracy of both sources, which would be really helpful.
I believe that the owners of IBM SPSS Statistics should think about improving the package itself to be able to treat unstructured data.
It does not handle very large data sets well. When there are 100,000 respondents, it does not manage effectively and crashes more often when the data set becomes very large or while merging yearly waves such as 2018, 2019, 2020 to 2026.
I'm unsure if SPSS has a commercial offering for big servers, unlike KNIME, which does.
Pricing is often egregiously high, and the company has changed billing models on us once already.
We are using a free version and would upgrade to a paid version if it were cheaper.
Amplitude's pricing is good and not overpriced; it is fair for the amount of data we are extracting and the analysis we perform.
Based on Amplitude charts and outcomes, our product team takes decisions, so it has improved decision-making.
Amplitude has positively impacted my organization as it allows us to make decisions based on data and iterate faster.
Collaboration was a significant part. What improves collaboration is the self-serve functionality, which was a big deal for PMs to have access to just that data and also the base layer of how that data is structured, which connects to clicks that every report refers to.
Predictive analytics is the most important part of analytics.
IBM SPSS Statistics provides excellent data visualization features that other tools do not have.
I mainly used it for cross tabs, correlation, regression, chi-squared tests, and similar analyses often seen in published papers.
| Product | Mindshare (%) |
|---|---|
| Amplitude | 0.4% |
| IBM SPSS Statistics | 0.4% |
| Other | 99.2% |


| Company Size | Count |
|---|---|
| Small Business | 2 |
| Large Enterprise | 9 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 7 |
| Large Enterprise | 20 |
Amplitude is a digital analytics platform that empowers businesses to understand and optimize customer experiences. It offers real-time insights into user behavior, helping companies identify patterns, measure engagement, and build data-driven strategies to improve their products and increase customer satisfaction.
This platform provides comprehensive analytics, combining data science and machine learning to help teams visualize trends and predict user needs. It integrates seamlessly with various data sources, making it easy to analyze customer journeys, track user interactions, and understand how features contribute to business goals. It also supports cohort analysis to group users based on behaviors, aiding personalized product improvements.
Key features include:
Benefits of using Amplitude include the ability to improve customer retention by understanding key engagement drivers, increase conversion rates through optimized funnels, and refine user experiences with more accurate segmentation. This leads to increased ROI as teams can focus on the most impactful improvements.
Amplitude is valuable across various sectors like e-commerce, fintech, and SaaS. It helps e-commerce teams refine product recommendations, fintech companies assess user acquisition strategies, and SaaS firms personalize onboarding experiences.
Pricing is tailored based on usage and features, offering free, growth, and enterprise plans. Customer support includes comprehensive documentation, a knowledge base, and expert guidance for setup, data management, and strategic analysis.
In summary, Amplitude helps businesses analyze and optimize digital user experiences to enhance engagement, conversion, and retention through a robust suite of analytical tools.
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?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.
We monitor all AI Data Analysis reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.