We use the solution for data mining.

Mostly, the solution is used for business analytics, including for several CSV files and Oracle databases, in order to sort of create flows for financial reports.

The solution is used for analyzing data and also for training.

We use the solution for generalized linear models. We use it for modeling and analyzing real data using models. 

I used SPSS Modeler for doing both predictive analytics, advanced analytics and more specifically to create and maintain machine learning predictive models.

I use IBM SPSS Modeler for teaching data science, and for data mining. I use it for my research.

The solution can be used for many projects, mainly for sophisticated data. It's applicable when you're dealing with a lot of data, for example, we're now dealing with a cancer related project, trying to figure out how we can build a predictive model. We're also using it at the university to predict the courses our students will follow. It helps with preparation related to capacity. We use the solution for both educational and research purposes. I have some students that I'm supervising for data mining and I teach this solution. I'm interested in data science in general. I'll be publishing a book on the subject in Arabic. I'm an associate professor of statistics and we are customers of IBM. 

We use IBM SPSS Modeler for data analysis, prediction, segmentation, etc. We use it as a data management tool.

We just started using it for analytical performance. We're still in the testing phases of building a couple of different projects, proofs of concept.

So far, it's good. We're probably going to do a comparison with Watson, to test two different products, to see which one gives a better response.

Right now, I think we have about five or six different machine learning proofs of concept, using real-time data. We're running them on Bluemix, IBM Cloud.

We use this solution to generate and deal with data. We have feeders which have the methodologies and the data gives us leverage to deal with that and provide the graphic. 

I'm an application architect and we are customers of IBM.

Creating analytical models that we put into production: Everything ranging from pricing to just-in-time inventory management.

We have had multiple models go into production. We are at around roughly 10 models right now. We were able to quickly transform and move existing models into the SPSS environment, so we saw increases in accuracy resulting from this. Therefore, we are running faster and more accurately.

This is batch. We are using models for safety and to predict what drivers are likely to leave (i.e., just-in-time inventory management), so grows it across the enterprise.

We're using a public Azure cloud. We are not deploying apps, but we are doing the analytics. We are pulling the data in with it, then we are writing the tables.

It has performed as it should. I have not had any issues.

I use it for quick prototyping. I get my students to use it every now and then but we don't actually use it in a class, or there would be more users.

The performance is fantastic. Version 15 used to have some bugs but the newest version, version 18, is a lot better. I really like the functionality that includes R and Python nodes. SPSS should have added that years ago.

Primary use case is feature engineering on a pre-prepared data set and mostly doing predictive modeling. Used on desktop. If it comes to ETL and data prep the tool is a waste of time...

Our primary use case is analytics. 

We are putting less than 10 machine learning models into production, and do not currently run our models on a cloud environment.

We use it for data modeling like arithmetic modeling, bank modeling. We have different models such as loan models. We use three products, SAS, R, and SPSS Modeler to do predictive modeling. We are a big IBM shop.

I'm not sure how many machine-learning models we are putting into production. I'm new, I've been at the company for five months, but I would say this year there should be at least five or six models. We do a PoC on modeling and, based on what fits better, that's what we go with. So the bottom line is that a handful of models will go live but we'll be trying 10 to 15 models to do the predictions and see what best suits the company.

This is batch. We do monthly modeling, we do weekly modeling. It's not daily. We run weekly model reports too. We also change the parameters that we enter based on the industry, as things change.

We don't have cloud, it's all on-prem.

I use it for my classes. One of the classes I teach is Advanced Analytics for students in the actuarial sciences area. My students are also using it for projects that they have to do as part of the process leading toward their degrees.

Before that, I was using it when I worked for IBM, as a consultant. I was doing a project for IBM in their analytics.

Building predictive models, including customer churn and lead generation.

Performance has been great. I've used it for about eight years or so, lots of flexibility. It continues to be a very flexible platform, so that it handles R and Python and other types of technology. It seems to be growing with additional open-source movement out there on different platforms.

We aren't putting that many machine-learning models into production. This is not the primary tool we use. This is more for me in terms of data exploration and knowledge discovery, that kind of thing. I really haven't done any production models in my current role. In previous roles I have.

In terms of cloud environments, it's actually a combination. Long story, but it's a combination of different things.

It's more for data, as a data repository. 

My experience so far using Modeler is good. I haven't noticed any issues with our current solution.

The primary use case is to augment our sales processes, to help our call center determine which customers to call, which products to push to those customers. 

Thus far it's been pretty effective. In a recent sample that I pulled, it successfully predicted two-thirds of our sales in a given week.

We're running batch, overnight, and I believe we have three machine-learning models in production at the moment.

We have separate models for our US call center and our UK call center. Each one is designed to do a customer recommendation, where it determines which customers should be ready to buy today, based on the recency of their last purchase, how frequently they purchase. And then it scores the opportunity with that customer, based on how much money they spend with us. It gives the salesmen a ranking of which customers are their biggest opportunity on that day, and they just go down that list and call them. It generates pretty good sales.

And then we have a second model that does item recommendations, based on some association modeling. The association model tells the sales rep what product that customer should be buying, based on their sales purchase history.

We're on-prem. I find the on-prem to be a pretty seamless experience, it flows directly from our data warehouse into the Analytics Server, and then we're able to deploy it back to the data warehouse for deployment into our CRM system.

People data, survey insights, HR analytics, nominal data, relational data, SEM modeling, logistic regression using nominal or ordinal groups.

Customer segmentation and churn analytics.

We get best results in customer segmentation and churn analytics and we have retained our customers. Our retention score has improved as a result of these projects.

We haven't used machine learning solutions yet.

Rapid prototyping, pre-production of models before roll out.

We put very few machine learning models in production, but we test a lot of them though. Nothing is real-time.

We are in the early stages of its use, therefore we are trying to discern predictive analytics on it. We are using it either for workforce deployment or to improve our operations.

We are using on-premise to run our models (on machine). We did different prototypes, but it is still in its early stages. 

I used it mostly for the PCA, the principal component analysis, and I have been using that for my bachelor's thesis. It performed pretty well for my task, for the goal of my task.

Our primary use case is the analysis of economic impact.

We use it to try to do predictive modeling and data exploration. I have a team of people that are working with the tool right now. We have gone through some SPSS training, so primarily we take the data and figure out what they need to try to predict or what they are trying to figure out, then we use the tool to normalize the data, maybe doing some text analytics. We are trying to get into doing some identity resolution with it, so we are using the professional version (the higher version) with it.

It has performed well. We are a bit limited because we are using it on a desktop, but we are moving it into a server architecture so we can have a little bit more horsepower for it. Also, we are getting licenses to do an SPSS server on the back-end, so as to offload some of the work off the desktop. This will help it perform a lot better. However, so far, it has worked pretty well.

We're doing real-time right now, but we are doing batch once we get the server product up and going. In terms of models, we are getting it off the ground. We have been using it for about six months, and we have been just playing with getting our models up and going, so we actually have the whole pure data and Hortonworks analytics products that we are going to be deploying in the analytics environment, that's where our server product will go, then we will have all of the governance pieces in place to start doing production deployment. So, we are almost there.

We are all completely on-premise. It has been fine on-premise, because we host a whole lot of IBM products. Sometimes it gets a little bit convoluted with the licensing. Right now, we just have the fixed user licenses that we deployed. We are trying to get some floating licenses out there to expand the use of it to a bunch of other people.

Pricing data analytics.

We are putting seven machine learning models in production to start. We may expand up to 10. This is real-time as we are pulling data out of Cognos BI server every morning. We manipulate and reload the data throughout the day based on parameters that come in from the field, then that gets put back into the system and refreshed for the next day.

We have a private cloud, which is our corporate cloud. Everything is done off of a shared server. 

To date, working with IBM SPSS Modeler has been very good, our installers and trainers have been excellent. The product seems to be quite robust and doing what we need.

Evaluation for training and consulting.

SPSS Modeler is a friendly interface for a beginner user. This program covers all data preparation and pre-processing techniques. The model can be selected from the recommendation of the program, semi-automatic with predefined parameters for each model (or user-defined), and tuning the appropriated model.

We are primarily interested in the supply chain data analytics, focusing mainly on procurement. We believe that there is a lot of value in spend analytics because of the following:

• Understanding and identifying opportunities.
• Reduced prices.
• Ability to better do negotiations.
• Understanding better the categories. 

I use the product for my research purposes in running analysis on data.  

I use it for research. It is able to do the basics but does not offer much variation. There are not a lot of options.

I used SPSS for statistical hypothesis analysis and it performed well. 

It helped me in that I didn't need to write them by hand, and I could get a result in one or two minutes. That helped me a lot.