Megan Sun, Data Mining Analyst, Marketing Department at Genworth Financial
I have 6 years using SAS and other statistical software to conduct academic and business projects. I started using SPM to build predictive models in May 2014. Our team mainly uses SPM TreeNet to build models for direct mail campaigns. I think the SPM software (Salford Predictive Modeler) is S.P.M. - SMART, PRODUCTIVE and MANAGEABLE.
Fast with big data
We use a lot of data. Most of the time, our model data sets have hundreds of thousands records and thousands of variables. SPM can handle these large data sets super-fast and builds predictive models in as short as few minutes. It also gives out pop-up messages if it finds some data issues so that I can identify problems more easily.
Powerful Battery tools to reduce variables
When I build predictive models with thousands of variables, I find one of the hardest tasks is to reduce the number of important variables. My goal is move from over a 1,000 variables to fewer than 20.
Models with fewer important variables without losing much lift are much easier to implement in our business environment. SPM provides 31 powerful Battery tools to do this for me. The top 3 Battery options that I most often use are Shaving, LOVO and Keep. All three can help you remove those least contributed variables from the model in order to maintain those most important predictors in your model.
Machine learning for missing values
While I build TreeNet models in SPM, I don’t need to spend lots of time dealing with missing values because SPM can take care of this for me and automatically learns the pattern from the build data set and then assigns proper values for the missing records. This feature saves me lots of time and manual work.
User friendly interface and no programming required
SPM makes model building easy for me even though I’m not programmer or statistician. With its user friendly interface design, it is easy to build a robust model in a few minutes. No hard coding needed to build models. It saves me lots of time in programming and code testing.
Build not only good, but reliable models
SPM provides many algorithms like Cart, TreeNet, and Random Forests, which I can choose to build different models. For instance, instead of building one decision tree one time, TreeNet is able to build hundreds of decisions trees in minutes and find the optimal one for me.
The CVR battery tool also helps me validate the model performance by building 20 or 30 models with different cross validation sets. This has helped tremendously in improving reliability of our models when I have thin data.
Easy scoring even with millions records
The scoring feature from SPM makes scoring data super easy. If I need to score data that has a relatively smaller size (hundreds of thousands records), I can get it done with SPM on my Windows environment in a couple of minutes. If I need to score a large data set that has millions of customer records, I can export the model from SPM into SAS and then do the scoring on SAS server without any problem.
Manage modeling process
The statistics summary file helps track the modeling process, report error message and show descriptive statistics information to help me manage the modeling and scoring process.
Helpful support team
The support team from SPM has rich knowledge in model building and is very helpful when I have questions. They take my questions or requests by email or via phone calls and always get back me in a timely manner with helpful answer. Additionally, they provide useful resources that help me better understand the topics.