Today’s topic is Predictive Analytics and this is the first in a series of audio podcast interviews on this topic. Dr. Eric Siegel is a former computer science professor at Columbia University and the President of Prediction Impact.
Here are questions that I asked:
Q: What is Predictive Analytics and does it accomplish?
A: Predictive Analytics is business intelligence technology that produces predictive scores for each individual customer or prospect. What you need to do before employing predictive analytics, is first decide which customer behavior will be most valuable to predict, such as predicting which customer is most likely to respond to an offer or which customer is most likely to cancel their subscription. And the next thing you need to do is prepare the data. Your data, which is, essentially, your organization’s collective experience, is leveraged by predictive analytics to produce predictive models and in so doing you’re actually learning from experience.
Q: What kind of investment in infrastructure required?
A: Well you can start with a pilot initiative for very little, in the way of hardware and software requirements. And this is also, you know, a place to start, to achieve a proof-of-principle, demonstrating what kind of return-on-investment can be achieved, such as improved customer retention, or increased profitability of a campaign. In this case, the core predictive modeling can usually be done with free evaluation software licenses or with a free open-source tool.
Having said that, however, it’s important to note that what you do need is expertise in predictive analytics, either by way of internal resources, and/or employing professional services. This expertise is needed to optimally position this technology, in order to determine the kind of behavior that’s going to be most valuable to predict on the business side, and then, more technically, what data’s require to achieve that prediction goal, and how you really need to prepare that existing data you have now in the right form, so that the resulting predictions you end up getting will be valuable in that they’re accurate and business-actionable. And then you apply what’s learned by way of directing, let’s say, a retention campaign, or selecting targeted content on a per-customer basis as far as what’s the product or message each customer is most likely to respond to.
Please follow the link to http://predictiveanalytics.org/?p=6 for the podcast audio interview.