It helps our customers who want to find some insights from their data.

The solution has not improved the functioning of my company. So, we switched off the solution.

We are creating models and putting them into production much faster than we would if we had gone with a strictly, code-based solution, like R or Python. In the time it takes to write the code to build one model, I am building three models inside SPSS.

It is just a lot faster. So you do not have to write a bunch of code, you can throw that stuff on there pretty quickly and do prototyping quickly.

• Pretty much the automated modeling process helps us to get going so quickly.
• A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly.

It minimizes coding.

Our go live process has been slightly enhanced compared to the previous programmatic process. There is now a faster time to production from the business end. We have C&DS, so we are able to drop the model streams in C&DS, then deploy it through there. 

Our go-live process has changed compared to the previously programmatic code based process. It’s not just the time to go-live but it’s also the process itself; the improvement in terms of performance, and maintenance is also important. I would say it has saved us a lot of time, about 20 or 30% of our time. I don’t have the numbers in front of me but I think something along those lines.

The main benefit is it makes things a little easier to do. If you want to solve a problem with R, for example, that's a lot more of a struggle. Essentially, R is a programming language. This package makes it more user-friendly, particularly for people who do not have a background in programming.

The benefits are that this product makes us a more efficient sales staff. We're reducing the inefficiencies in the buying patterns of our customers, by calling them when we know they're ready to order, instead of waiting for them to call us. It makes us more effective in our calling practices as well. We're not just cold-calling anymore, we're actually calling customers we know are ready to buy.

In terms of our go-live process changing, I believe we're following some pretty standard practices there. I don't think we've changed too much, other than which servers we were using as production servers.

Quickness and ease of use with the guarantee of robust modeling techniques and trustworthy accuracy.

Our business units' capabilities with SPSS Modeler is high. They no longer waste time on modeling and algorithms, meaning they are not coding any more. For example, segmentation projects now take one to three months, rather than six months to a year, as before.

I can't say that our go-live has changed compared to a previously problematic base process.

It has not yet improved my organization.

We are using this product for analysis of an exhibition center. It is releasing a lot of media and Modeler assesses how the shows were in the last six months. When it comes to a relative comparison between two quarters, this tool gives you very accurate numbers, which are then reflected in the media release that goes out.

It has provided us a lot of small wins that we could bring to our leadership and it has given them confidence with what we were doing in regards to analytics. We have used this to help us pursue bigger, better products, such as IBM PureData. It was a stepping stone, a launching off point, for much bigger products with IBM.

Our go live process has change a little bit compared to a previously programmatic process. We are still getting it built out right now so we are not quite where it is completely mature.

This is a new installation for us. We have not implemented it fully. It is going live now. Therefore, the impacts have yet to be determined. We are anticipating a more streamlined process.

Customer related tasks, such as: lifetime value, acquisition, retention, cross-sell and up-sell, segmentation. Fraud detection, recommender systems, sentiment analysis, and many more.

Modeler is the program, which based on the CRISP-DM process, to cover the whole data mining process. It can be modified for the machine learning algorithm by using R or Python code. 

Automated reporting data preparation: A lot of jobs, which were stuck in Excel due to huge numbers of rows, are now tackled pretty quickly.