by Dr. Steve Poulin

Those who are working with Predictive Analytics are always trying to find a better more effective way. We have chosen a field that that can get better and better every day. And in the last few years, with the development of new technologies and approaches to acquiring data – we are finding the spotlight on us to do just that: find better ways. Traditionally, there are three primary ways to develop models and algorithms for predictive analytics: (1) the expensive SAS solution, (2) the cheaper but just as effective IBM SPSS, or (3) the open source “R”. We, at CompassRed, think there is a fourth: Leverage the best of all.

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AuthorPatrick Callahan

by Steve Poulin and Patrick Callahan

As a Data and Analytics company, we at CompassRed have seen the full cycle of leveraging data for insight with each phase of development being just as important as any other. Deployment is the part of the predictive analytics process that puts the predictions to work.  Predictive analytics requires a very time-consuming process of data preparation and a very complex process of finding the best algorithms (also known as “modeling”) for producing predictions.  However, all of this effort is for naught if the predictions are not used by an organization to meet their objectives, which typically means increasing revenue and decreasing costs

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AuthorPatrick Callahan

By Patrick Callahan and Noah Baker

A major component to CompassRed’s  predictive analytics capabilities - or any data analysis, is the method of delivery for the insights we unlock from a customer’s data. . Gathering insights without making them useful is a waste. Delivery can come in many flavors: via application programming interface (“API”) integration into your company’s software, custom triggers that alert via email/mobile notification/tweet, or in most situations, through dashboards delivered via “Business Intelligence (BI)”. 

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AuthorPatrick Callahan

In our CompassRed Data Lab, on behalf of our clients, we are always looking for a better way to “Predict” with our algorithms, including Machine Learning (ML), and Artificial Intelligence (AI). As data becomes more ubiquitous and complicated, and as the systems that manage the data become more fluid, the process and methodology become ever more important (as long as they’re flexible). (read more)

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AuthorPatrick Callahan

How CompassRed’s Dr. Steve Poulin and his team leveraged Unstructured Data and External Data to uncover success and save $10 million dollars for every percentage point reduction in turnover.

When 92% of the agents hired by a life insurance company were leaving the company within their first year, this large Insurance Company realized their current approach to retention was not sustainable. This was causing significant expenses for continuous recruitment and hiring costs.  Using unstructured data from the applicant’s resume and US Census data about the area in which they live, a predictive analytics process was developed to identify which applicants were most like to sell the amount of insurance required to become a successful agent with the company.  (read more)

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AuthorPatrick Callahan