Strategy Science yields data-guided, not data-driven decisions. The methodology provides an empirical framework for taking maximum advantage of both math and human judgment in the design of decision strategies.
This balance is important because decision strategies are aimed at improving future results, and data tells us only about the past. Strategy Science does, of course, incorporate scores from predictive models, which use customer historical data to determine the likelihood of specific future behaviors. But the scope of decision analytics is so much bigger—incorporating dozens of predictive scores and a web of actions, reactions, outcomes, tradeoffs and constraints—that data is only a part of the picture. Human experts need to fill in the rest using judgment and intuition. Strategy Science facilitates this process by making the critical components of a decision and the relationships between them visible, understandable and editable. Advanced optimization, visualization and simulation tools let you adjust the decision model to reflect what you know or suspect about the market.
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