Causal vs. JMP

Definitely suited best for B2B / B2B2C product modelling. We used this for B2B / B2B2C / C2C and the C2C side of things was always more complex to model out due to this being dependent on marketing spend (CAC) and factors around virality which really cannot be forecasted (not a shortcoming of Causal, just implied by the mechanics of modelling)

Incentivized

Many organizations have seen their analytical capabilities, and the results from them, plateau. Of these, we've observed, that most of them didn't appreciate that they could do (even) better. These companies should definitely consider JMP. Any company that is research-based can benefit from accelerating their research, learning more in less time, effort and cost, with JMP's tools. Basically, any organization that is hungry enough for improvement to seek out better ways is suitable for JMP. Those who are happy with their current performance are not likely to consider the changes, though they were not major impediments by our clients, required.

• Graphs are more detail-oriented and contain statistical inferences.
• Everything is drag and drop. Pretty much easy to use and handle and also to learn.
• Importing and exporting the results are easy and they can be attached with any other tool for processing.

Incentivized

• Loading a large amount of data is very tedious as it takes a lot of time and it crashes very frequently.
• I dislike the limited options they have in terms of statistical models or analysis tools.
• Variable value designation is a big problem in JMP, the software fails to recognize the type of data when it comes to the numeric value.

Incentivized

I've mentioned this earlier, but the licensing agreements are very prohibitive. I work with a company where my role has become less and less doing my own analytics and more and more trying to help other people in that role. As we are bringing more people "up to speed" it's hard to justify licenses for 2-3 people when they aren't full time, Six Sigma black belts just looking at stats all day. A floating license option would make this a no-brainer, since these people could continue their other work and add JMP usage as they grow their skills, but this is not something JMP/SAS has offered.

The GUI interface makes it easier to generate plots and find statistics without having to write code. The JSL scripting is a bit of a steep learning curve but does give you more ability to customize your analysis. Overall, I would recommend JMP as a good product for overall usability.

Incentivized

The helpful tips are great for new users. I am always able to find solutions to a tool I am working with through the hep section. And my area has a users group that meets each quarter to share ideas and view upcoming JMP revisions.

Incentivized

I have not used your online training. I use JMP manuals and SAS direct help.

We actually use both JMP and IBM SPSS, but I think JMP's complexity lends itself to more in-depth statistical analyses. SPSS is designed for that as well, but we tend to use it more for quicker analyses, and we have found that JMP is far more powerful.

• JMP has resulted in literally millions of dollars in ROI due to identification of correctable errors.
• Use of JMP control charts JMP has greatly simplified and improved interpretation of Lean, FMEA, and PDSA type analyses.
• Use of JMP has enable the testing and subsequent selection of 'best practices' saving uncounted hours in false starts based on 'collective experience'.
• The down side is that JMP is not a 'magic box', one still has to take care in applying the tools properly. Moreover, time-consuming approaches using JMP may still be the 'order of the day', because the service (even power user) is unaware of significant shortcuts available for free on the JMP community website.

Incentivized