Predictive analytics go to work

Contrary to popular opinion, you don't need a huge budget to get started

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Follow these best practices to ensure a successful foray into predictive analytics.

1. Define the business proposition. What is the business problem you're trying to solve?

2. Recruit allies on the business side. Having the support of a key executive and a business stakeholder is crucial.

3. Start off with a quick win. Find a well-defined business problem where analytics can deliver measurable results.

4. Know the data you have. Do you have enough data -- with enough history and enough granularity -- to feed your model?

5. Get professional help. Creating predictive models is different from traditional descriptive analytics, and it's as much of an art as it is a science.

6. Be sure the decision-maker is prepared to act. An action plan alone isn't enough -- someone has to carry it out.

7. Don't get ahead of yourself. Stay within the scope of the defined project, even if success breeds pressure to expand the use of your current model.

8. Communicate the results in business language. Talk about things like revenue impact and fulfillment of business objectives.

9. Test, revise, repeat. Conduct A/B testing to demonstrate value. Present the results, gain support, then scale out.

Sources: Guy Peri, P&G; George Roumeliotis, Intuit; Dean Abbott, Abbott Analytics; Eric Siegel, Prediction Impact; Jon Elder, Elder Research; Anne Robinson, The Institute for Operations Research and the Management Sciences.

-- Robert L. Mitchell

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This story, "Predictive analytics go to work" was originally published by Computerworld.

Copyright © 2012 IDG Communications, Inc.

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