Automated self-service modeling: predictive analytics as a service
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Research into service provision and innovation is becoming progres- sively more important as automated service-provision via the web matures as a technology. We describe a web-based targeting platform that uses advanced dynamic model building techniques to conduct intelligent reporting and modeling. The impact of the automated targeting services is realized through a knowledge base that drives the development of predictive model(s). The knowledge base is comprised of a rules engine that guides and evaluates the development of an automated model-building process. The template defines the model classifier (e.g., logistic regression, multinomial logit, ordinary least squares, etc.) in concert with rules for data filling and transformations. Additionally, the template also defines which variables to test (‘‘include’’ rules) and which variables to retain (‘‘keep’’ rules). The ‘‘final’’ model emerges from the iterative steps undertaken by the rules engine, and is utilized to target, or rank, the best prospects. This automated modeling approach is designed to cost-effectively assist businesses in their tar- geting activities—independent of the firm’s size and targeting needs. We describe how the service has been utilized to provide ‘‘targeting services’’ for a small to medium business direct marketing campaign, and for direct sales-force targeting in a larger firm. Empirical results suggest that the automated modeling approach provides superior ‘‘service’’ in terms of cost and timing compared to more tra- ditional manual service provision.
The article of record as published may be found at https://doi.org/10.1007/s10257-011-0185-1
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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