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Thursday, June 28, 2012

Ten steps to predictive success

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

1. Define the business proposition. What is the business problem you are trying to solve? What is the question you're trying to answer? Think like a business leader first and an analyst or IT expert second.

2. Line up a business champion. Having the support of a key executive and a stakeholder is crucial. Whenever possible help the stakeholder to become the initiator and champion of the project.

3. Start off with a quick win. Find a well-defined business problem where analytics can bring value by showing measurable results. Start small and use simple models to build credibility.

4. Know the data you have. Do you have enough data, enough history and enough granularity in the data to feed your proposed model? Getting it into the right form is the biggest part of any first-time predictive analytics project.

5. Get professional help. A statistical background and a little training aren't enough: Creating predictive models is different from traditional descriptive analytics, and is as much an art as it is a science. Get help for that first win before striking out on your own.

6. Be sure the decision maker is prepared to act. It's not enough to have a prescribed action plan. The results may dictate actions that are counterintuitive. If the business decision makers won't act or aren't in a position do so, you re wasting your time, so get a strong commitment up front.

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. Good analytics sells itself, but overextending can result in an unreliable model that will kill credibility.

8. Communicate the results in business language. Don't discuss probabilities and variances. Do talk revenue impact and fulfillment of business objectives. Use data visualization tools to hammer home the point.

9. Test, revise, repeat. Start small, test, revise and test again. Conduct A/B testing to demonstrate value. Present the results, gain critical mass, then scale out.

10. Hire me to implemment above steps with success :)

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