Decision Analysis

The goal of decision analysis is to effectively manage large amounts and types of data in order to help our clients understand their strategic options and make informed business decisions.

Our approach: We combine significant open-source data with smaller volumes of client-specific data to develop insights relevant to market expansion and customer affinity. Examples of open-source data include Federal and State data repositories, consumer trends, geo-coded behavioral data, environmental data, and more. Examples of client data include transaction volumes, insurance claims, clinical charts,  customer feedback, and more.

Our method includes assessing costs, estimating risks, and simulating probable results under varying conditions. We use decision analysis to gain insight into the factors that influence business outcomes. Areas of analytic focus include healthcare technology, market entry, investments, marketing campaigns, and customer engagement. The ultimate goal is to help clients maximize performance within a defined budget and timeframe by implementing a comprehensive, innovative, analytic process.

Our Toolkit

Marketing Science Marketing science comprises the basic toolkit for analytics, including segmentation, targeting, and feedback measurements. For example, we use sociometrics to understand patterns of social contagion of depression or obesity.
Data Science and Algorithm Development Scriplogix has developed a wide range of statistically validated and proprietary algorithms with variables ranging from competitor actions to changes in the business environment.
Predictive Analysis We help organizations evaluate likely outcomes. We construct models—focused on both marketing and medical topics— and develop scenario predictions.
Market Sizing and Risk Analysis Estimating market opportunities for new product launches or new country entry is a refined process— part art, part science. We build our estimates from deep and extensive knowledge of the domain, drawing on a wide range of macroeconomic, environmental, cultural and regulatory factors. One example is the modeling we did to facilitate the launch of world-class diagnostic services in India. To accomplish this we drew heavily on our understanding of patient profiles and physician practices in India—a key emerging market.
Customer Modeling As part of our Customer Modeling program, we focus on understanding the composition of existing and target customer bases.  We create affinity profiles that identify loyal, transitory, or opportunistic behavior. For example, with respect to medication adherence, we identify proactive, confident, questioning and skeptical behaviors, and based on analysis of past behaviors and other loyalty metrics, we predict likely future behavior. Two examples include helping a client predict   patient responses to a marketing campaign, and on the physician side, tracking physician response to targeted marketing of a new medical device.

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