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1. Your data is not being sent to any third party. Our predictive modeling is all done in-house.
2. Our models only see anonymized data, not personally identifiable information (PII).
3. Your data is never exposed to any other partner.

We do not use any third-party vendor(s) and all our work is conducted in-house. We do leverage open-source code libraries to implement well-known algorithms, such as Neural Networks, Gradient-Boosted Decision Trees, Random Forests, and Support Vector Machines.

With respect to our Predictive Modeling service: our ability to build an accurate predictive model is predicated on training the model against a vast amount of anonymized data from over one thousand colleges, universities, and independent schools. However, one Partner’s (customer’s) data is never exposed to another Partner (customer).

With respect to our Predictive Modeling service, we gauge effectiveness based on real-world results (e.g., did the model reliably predict the desired outcome) and metrics. For example, for classification models used for GC Smart Segments, we use the following:

• AUROC (Area Under the Receiver Operating Characteristics Curve): AUROC (or AUC) is one of the most commonly-used measures of performance for machine learning classifiers. AUROC is derived from the ROC curve, which plots how well a model can strike a balance between maximizing true positives and minimizing false positives. It is represented as a percentage from 0-100%, where 50% is the expected performance of a “random” model.
• Precision: Of the constituents that the model predicted to be in the positive class (e.g., likely to convert), what percentage were actually observed to be in the positive class (converted)?
• Recall: Of the constituents that were observed in the positive class (e.g., converted), what percent did the model predict to be in the positive class (e.g., likely to convert)?