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Predictive Modeling

We were building and deploying advanced predictive models before it became cool.

Data science, predictive modeling, and machine learning improve business.

Many do-it-yourself modeling softwares let marketers, analysts, and IT departments sort through large amounts of data. Without the right expertise, these models can end up haphazardly with invalid predictors. Bad models generate misguided and false interpretations. It not only wastes capital, but can have tremendously negative business implications. We build advanced predictive models that maximize accuracy and robustness. Our work stems from our years of experience in sound statistical practice.

Building great models is just the beginning.

Interpretation, implementation, and evidence-based strategy development are key to realizing the true value of predictive modeling.Whether we are building response models to improve marketing ROI for direct marketing programs, survival models to predict employee attrition, or classification ensembles to predict patients-at-risk for hospital readmissions, operationalizing the model and tracking the results is key.

When we build a model, you can trust it.

Successful predictive modeling relies on a deep understanding of theory. At Splash Analytics, our data scientists, data analysts, and statisticians are unrelenting in their attention to statistical validity and soundness.

Every predictive model we build starts with understanding the stochastic processes that generated the data and selecting the appropriate technique predicated on statistical theory. We employ statistically sound variable and model selection techniques, and validate each model utilizing the appropriate internal or external validation procedures. We understand the broad spectrum of modeling techniques from generalized linear models and generalized estimating equations, to artificial neural networks and genetic algorithms, to geospatial kriging and hidden Markov models.

Effective data interpretation relies on an up-to-date understanding of industry knowledge. We keep up with the latest academic research in statistical inference, machine learning, and computer science and frequently showcase our work at academic and industry conferences.