Coremetrics / IBM Digital Analytics (discontinued) vs. IBM SPSS Statistics

IBM analytics has continued to improve upon the days of being the original core metrics. After using the updated version for quite some time, it has been great at providing the needed analytics to measure ROI and goal performance for our quarterly KPI's. It has resulted in a great increase in web engagements although we are a midsize company, smaller outfits may not need such an expensive option.

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IBM SPSS Statistics is well suited for pretty much any data analytic scenario. It can handle extremely complex and large-scale datasets with ease. It especially shines if you have to do any kind of analyses that involve significance testing. Being able to do any number of significance tests (i.e., t-tests, chi-square, ANOVAs, etc.) right inside the tool is very valuable. The only scenario I would say it is less appropriate is if you need to work on a very small dataset and answer very simple questions, like frequencies or averages. In those cases, something like Excel could probably do the job just as easily.

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• IBM CXA comprises an acquisition called Tealeaf. This tool has deep heritage and this is evident in its present-day capabilities.
• The Universal Behaviour Exchange or UBX puts the concept of personalisation at the forefront. The ability to combine physical (analog) and digital transactions to create the complete picture of a customer journey, is a stand out benefit.
• The solution does not have to involve the purchase of software. IBM CXA can be sold as a service bundled with analytics as a service. This not only lowers the cost of ownership, it gets around one of the principal issues. Strong staff with design and analytical capability to drive the solution and deliver tangible benefits.
• The seamless integration of Watson AI services to help with the heavy lifiting. Watson reinforces the analytical focus this solution has and can learn to recognise situations specific to a company.

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• SPSS has been around for quite a while and has amassed a large suite of functionality. One of its longest-running features is the ability to automate SPSS via scripting, AKA "syntax." There is a very large community of practice on the internet who can help newbies to quickly scale up their automation abilities with SPSS. And SPSS allows users to save syntax scripting directly from GUI wizards and configuration windows, which can be a real life-saver if one is not an experienced coder.
• Many statistics package users are doing scientific research with an eye to publish reproducible results. SPSS allows you to save datasets and syntax scripting in a common format, facilitating attempts by peer reviewers and other researchers to quickly and easily attempt to reproduce your results. It's very portable!
• SPSS has both legacy and modern visualization suites baked into the base software, giving users an easily mountable learning curve when it comes to outputting charts and graphs. It's very easy to start with a canned look and feel of an exported chart, and then you can tweak a saved copy to change just about everything, from colors, legends, and axis scaling, to orientation, labels, and grid lines. And when you've got a chart or graph set up the way you like, you can export it as an image file, or create a template syntax to apply to new visualizations going forward.
• SPSS makes it easy for even beginner-level users to create statistical coding fields to support multidimensional analysis, ensuring that you never need to destructively modify your dataset.
• In closing, SPSS's long and successful tenure ensures that just about any question a new user may have about it can be answered with a modicum of Google-fu. There are even several fully-fledged tutorial websites out there for newbie perusal.

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• The user interface is in Flash, which can be very frustrating and slow at times. Apparently, this is to be transitioned in a future release.
• Can only segment the last 93 days of data. Any historical segmentation beyond the 93 days must be run in Explore (which is credit based, and has its own limitations with the number of credits per month, based on the initial contract with IBM).
• Reports can only display 93 days of data at a given time for custom date ranges. There are pre-programmed date ranges setup with IBM during implementation (last week, last month, last quarter etc.), but are not flexible enough to answer more specific questions.
• Certain reports cannot have segments applied, making answering some simple questions a bit more tricky. For example, I can create a segment around mobile devices and apply it to the marketing channels report, but I can't create a marketing channel segment and apply it to the mobile reports.
• Built in API calls allows for nice report design and automation.

• Cost is becoming prohibitive.
• Availability of procedures in the base package seems to be dwindling.
• The copy-and-paste function from output to Excel is not as easy as it once was (now I have to do a "paste special").
• Text and date handling are terrible.
• Need to include AI-based NLP for survey verbatims and other text-based fields. This is becoming increasingly important in the CX world, yet SPSS seems to be ignoring it.

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IBM Digital Analytics is a great solution for our clients and I believe they offer the best solution for the retail space. We have access to IBM support via email or live chat and they can answer many of the reporting questions that come up. IBM is receptive to our feedback of the product so I am confident they will continue making improvements

Both
money and time are essential for success in terms of return on investment for any kind of research based project work. Using a Likert-scale questionnaire is very easy for data entry and analysis
using IBM SPSS. With the help of IBM SPSS, I found very fast and reliable data
entry and data analysis for my research. Output from SPSS is very easy to
interpret for data analysis and findings

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Very easy to implement and use.

Probably because I have been using it for so long that I have used all of the modules, or at least almost all of the modules, and the way SPSS works is second nature to me, like fish to swimming.

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Never had any issues

SPSS can tend to crash when I am trying to do a lot of data. This can slow me down when I need to do a lot of data

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As reports are templated, the system is pretty quick. Sometimes you have to wait a bit for a report to render. Or you might have to re-load the page. But there is no real issue here and the system is on par with other similar systems.

SPSS does the job, but it can be slow. I do have to plan a lot of time to get through a huge amount of data.

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Overall, the level of support is very good and I would say it is a strong asset of the solution. However, you can sometimes feel that there is a difference of level among the support team.

I have not contacted IBM SPSS for support myself. However, our IT staff has for trying to get SPSS Text Analytics Module to work. The issue was never resolved, but I'm not sure if it was on the IT's end or on SPSS's end

Online training is really great. One of the best assets that they have. Lots of great videos, pop quizzes at the end of each module. Fantastic. Other tools have similar features, but not as good.

See previous comment: reading and understanding the encyclopedic implementation guide is a must.

Have a plan for managing the yearly upgrade cycle. Most users work in the desktop version, so there needs to be a mechanism for either pushing out new versions of the software or a key manager to deal with updated licensing keys. If you have a lot of users this needs to be planned for in advance.

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Much of the work we did in IBM Digital Analytics could have been answered through Google Analytics, a much simpler, agile and FREE solution set. Not mention, given the vast number of Google Analytics USERS, free and actionable support is simply a click away ... this compared to IBM Digital Analytics fractured and often absent support service.

If you have made it this far, you should have a very good idea of how SPSS stacks up the competition (data processing and analytics tools). Even the free ones, such as r Studio or Stata, are leaps and bounds ahead of SPSS. IBM is resting on a reputation developed nearly 30 years ago and has shown no desire to improve.

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This solution can support large amount of data and transaction. The way that user management features are built, it shows it is meant for large organizations.

I am neutral because I have not had to look into scalability since I am using as a student.

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• We spend too much time trying to work around bugs on the new UI.
• We spend too much time trying to figure out how to make certain segments work because support and the knowledge center are lackluster.
• Our sales rep is very unresponsive and leaves us searching for a lot of answers on our own, including what other products we may benefit from that IBM offers.

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• I found SPSS easier to use than SAS as it's more intuitive to me.
• The learning curve to use SPSS is less compared to SAS.
• I used SAS, to a much lesser extent than SPSS. However, it seems that SAS may be more suitable for users who understand programming. With SPSS, users can perform many statistical tests without the need to know programming.

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