Syndicated from BeyeNetwork
Gary Cokins had a great post – Fill in the blanks: Which X is Most Likely to X? in which he identifies some great uses for predictive analytics.Increasing employee retention, increasing customer profitability and increased shelf opportunity are classic uses. What Gary does so well in this post, though, is point out that a prediction is not enough – you must take action. For example, knowing which employees might leave will not help unless management intervenes. All too often I hear folks talk about predictive analytics as though the prediction is the end game. And when I hear this I always say “so what?”. For instance:
- We can predict which customers are at high risk of churn – so what? What decsion(s) will you make differently as a result?
- We can predict which products are most profitable – so what? Can you change the way your website makes offers to promote the ones that are more profitable?
- We can predict which transactions have high fraud risk – so what? Can you mix this risk with policies and regulations so that you can intervene effectively and legally in a real-time process?
This whole area was the focus of a webinar I gave for bettermanagement.com on Putting Predictive Analytics to Work and you can watch the recording on the website. You might also like the White Paper of the same name that’s up on the BeyeNetwork site.