Dr. John F. Elder heads a data mining consulting team with offices in Charlottesville, Virginia and Washington DC. Founded in 1995, Elder Research, Inc. focuses on scientific and commercial applications of pattern discovery and optimization, including stock selection, image recognition, text mining, biometrics, drug efficacy, credit scoring, cross-selling, investment timing, and fraud detection.

Dr. Elder has authored innovative data mining tools, is active on Statistics, Engineering, and Finance conferences and boards, is a frequent keynote conference speaker, and is General Chair of the 2009 Knowledge Discovery and Data Mining conference in Paris. John’s courses on data analysis techniques – taught at dozens of universities, companies, and government labs – are noted for their clarity and effectiveness. Dr. Elder was honored to serve for 5 years on a panel appointed by the President to guide technology for National Security.

Check out the three minute interview on the Predictive Analyticswebsite.

Transcript:

Bill Cullifer: I am on the phone with Dr. John Elder, CEO and Founder of Elder Research out of Virginia. Good afternoon. Thanks for agreeing to this interview.

John Elder: Thanks for having me.

Bill Cullifer: You bet. You recently provided a presentation at the conference entitled the High ROI of Data Mining for Innovative Organizations. Can you summarize that session for the subscribers of this podcast?

John Elder: Sure Bill. I just gave a talk about nine of the kind of more interesting success stories we have had at Elder Research over the last decade or so and we will get them at a higher level. So, it handled the way data mining really helps and I have broken it into three parts, approving and emphasizing good, detecting and eliminating bad, streamlining and automating are… I gave examples of how each of those had worked [Voice Cross Over]

Bill Cullifer: Can you cite an example?

John Elder: Sure. One kind of interesting one was Anheuser-Busch[Phonetic] pictures of beer on the shelf, they like to correlate how the positioning of their product related to [Inaudible], they also want to know if they are missing any stock [Inaudible] so, that’s a relatively laborious process, to take a picture and turn it into some [Inaudible] interpretation of what exact product and what exact orientation is there. So, of course… so we did a pilot process, automated identification of what[Phonetic] product was there. Recognition is a very hard [Inaudible] is one that’s well lived, well under[Phonetic] good condition. Products are trying to make themselves be recognized a whole lot easier than say identifying somebody who just canceled on a [Inaudible] might be for instance but we were able to get 90% accuracy which was our goal instantaneously essentially meaning that they would spend one tenth as much time editing it as they would doing it by previous metric.

Bill Cullifer: Time is money. Right?

John Elder: Exactly. So, that would save them millions of dollars there. So, that was an example of automating and speeding up. There were a couple of examples of return by eliminating, but that there… the examples [Inaudible]. We had projects we did for the IRS and projects that we did for a large [Inaudible] electronics firm that saved tens of millions of dollars, actually the numbers were astoundingly large compared to the [Inaudible].

Bill Cullifer: So, if I… and the subsribers of this podcast were considering entering into the predictive analytics realm, any high level recommendations on first step?

John Elder: Yeah, the… one of the extreme[Phonetic] things that came out as I was putting this talk together were you know what were some characteristics that were common to some of these successful projects and all of them were technically successful, but not all of them were completed and that was interesting to see what were the differences there. For instance, on the Anheuser-Busch[Phonetic] one, they never put it in production because the day that we were to sign the development contract was 09/11; that was definitely a whole lot of a hell of a tragic event and there were some other times when a project didn’t see the way to appreciate it[Phonetic], but most of the nine examples I gave complete business successes saw their way into implementation and one of the key characteristics that we needed was a champion who would take the business risk inside the organization to see it all the way through and what you really want[Phonetic] there is nothing harder than starting something brand new because all those who talk about the old way of doing it will hold it and those who talk about the new way only lukewarmly support it and can still do [Inaudible]. So, we found that the innovative organization part of the title was really [Inaudible] organization that allowed it’s… that rewarded success for its people, not just punished because that is the environment in which a project will thrive and the return can be [Voice Cross Over]

Bill Cullifer: Sounds like excellent advice and we certainly appreciate your time.

John Elder: Thanks for having me.

Today’s topic is Predictive Analytics and this is a continuation 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.

Q: Dr. Siegel Could you provide an example case of deploying predictive analytics?

A: The two most established places predictive analytics is applied are 1) increasing the response rate in direct marketing and 2) improving customer retention by predicting churn. Let’s discuss a case of the first of these two applications, improving direct marketing response — which is also known as response modeling, since the thing predicted for each customer by the model is whether or not they will respond to the offer.

Predictive analytics supplies the predictive score for each customer, and it’s really simple how these scores can be used used to improve response rate and campaign profit.  First, you sort your list by the scores, with customers that received a higher predictive score up top. This means the first customers in your list are now the most likely to respond.  Then you target your campaign only towards customers higher up on the list.

So, the next question is, where do you draw the line going down the list – how many do you contact?  Well, suppose you are contacting a million prospects, and it costs $2 per contact (this example could be for either a mailing or a phone campaign). The average profit per response, let’s say, is $20, and a little over 7% of those contacted respond.  Under these conditions, a campaign will not be profitable — with a little arithmetic, you can see that it actually loses $566,000, because the response rate’s just not high enough relative to the cost that you’re spending and how much you make on those that do respond.

But with a predictive model, this can be turned around and you can take a loss and make it a profit.  If the cost is expended only to contact the top, let’s say, 25% of the customers, which contains the lion’s share of responses anyway, the campaign will turn out to be profitable, and could land a profit approaching half a million dollars. So knowing which customers to contact can make all the difference in the world.

Let me make one last note about this.  The predictive model that you use to do this can continuously be improved, as long as the responses to each campaign, even a campaign that’s targeted with the model in the first place, are tracked.  Each customer’s response or non-response, if you’ve tracked it, then becomes a learning case with which better predictive models can be developed.