Dr. Zeller manages the strategic direction of Zementis, a software company focused on predictive analytics and advanced decisioning technology.

In this interview, Dr. Zeller explains how organizations increasingly recognize the value that predictive analytics offers to their business. The complexity of development, integration, and deployment of predictive models, however, is often considered cost-prohibitive for projects. In light of mature open source solutions, open standards, and SOA principles we propose an agile model development life cycle that allows us to quickly leverage predictive analytics in operational environments.

What you will learn:

* Leverage predictive analytics in real-time
*Accelerate time-to-market for decision models
* Reduce cost with SaaS

Transcript:

Bill Cullifer, WOW I am on the phone with Dr. Zeller, CEO of Zementis out of San Diego, California. Dr. Zeller, good afternoon and thanks for agreeing to the call.

Dr. Zeller: Thanks for having me on here. I think excellent educational website. So, I am glad to contribute.

Bill Cullifer, WOW: I appreciate that. You have experience as a computer science fellow and you have been doing predictive analytics for a number of years. You and have a session coming up on the San Diego Computer… Supercomputer Center High Performance Scoring of Healthcare Data as it relates to predictive analytics. Can you give us a couple of walk aways from that session?

Dr. Zeller: Absolutely. It’s going to focus on the ease of deployment for predictive analytics, which really is also the core focus of Zementis and our product line. Taking predictive models that you have developed in various different models and bringing them into a production environment so that any other IT system can easily leverage it, utilize and kind of shorten that time to deployment, time to market predictive analytics.

Bill Cullifer, WOW: And who exactly benefits from that statement? Are we talking about the department itself organizationally, what… where is the return on investment?

Dr. Zeller: Well, the return is really across the enterprise, bringing it into an operational infrastructure and much easier than it has been done in the past. Most of the time they are great tools for data mining, analysis, and model building, but the main hurdle today actually is really the deployment of predictive analytics and…

Bill Cullifer, WOW: Yeah, interesting.

Dr. Zeller: So many companies are not willing or couldn’t spend you know the time and cost associated with such a project. So, it has been I think a limiting factor for predictive analytics in general, just not being able to easily move things into a production environment at a reasonable you know very cost effective way.

Bill Cullifer, WOW: Yeah and how has that changed?

Dr. Zeller: Well, what we are doing is really leveraging the predictive model markup language (PMML), which is the widely known standard in the industry and supported by really the major industry players. We are really focusing on open standards and utilizing that in order to transition from any tool, commercial or open source, into our deployment In addition to that, we actually leverage a new trend that is a very hot topic currently in the industry. It is called computing. So, we leverage for example the Amazon Elastic Compute Cloud to deliver our software as a service at a much, much lower cost than you have ever seen that before.

Bill Cullifer, WOW: Excellent and that’s a subscription model.

Dr. Zeller: Correct yeah. Instead of spending $50000 or $100000 or more on software and hardware licenses, here you can get started at less than a dollar an hour.

Bill Cullifer, WOW: Excellent and where might people go to get more information Dr. Zeller?

Dr. Zeller: Our website, www.zementis.com should really give kind of all the details, you can sign up, you can really get started within five minutes, launch your instance and deploy your models and be in production literally within you know a few hours.

Bill Cullifer, WOW: Excellent. Sounds really good and Dr. Zeller, we certainly appreciate the information and your time today.

Dr. Zeller: Very much my pleasure.

Today’s topic is Predictive Analytics and this is 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.

Dr. Siegel, good afternoon and thanks again for agreeing to this interview and for your time today. Could you describe a specific client deployment for Predictive Analytics?

Sure.  Last time, I illustrated the value of response modeling for direct marketing, which applies in very much the same way across companies.  As we saw, if there’s no method in place to score or segment customers, predictive analytics makes a tremendous impact on profit and response rates.  But let’s move to a more unique application of predictive analytics which competed against an existing legacy system.

A successful information portal in the educational sector, which is used by 1 in 3 college-bound high school seniors, wanted to increase advertisement response rates by predicting which promotion each user was most likely to respond to.  The existing system selected between hundreds of ads by way of “A-B testing” — or, more precisely, “A-B-C-D… testing.”  It measured which ad was most popular, universally across users, but separately for each web page, which, in this case, closely corresponds to the lifecycle of the user. This legacy system was also improved by not showing the same ad to a user more than once, since, if the user is interested, she or he would likely have already responded, given that the ads are embedded prominently within the website’s content.

This made for a formidable system over which to improve, given that a small number of the ads were much more popular than the rest.  This made it a challenge for predictive analytics to confidently say, “Hey, this user is much more likely to be interested in this relatively unpopular ad than any of the really popular ones.”

We generated 291 predictive models, one per ad — ads that hadn’t been tested enough to learn about what kind of user is more likely to respond to it were omitted.  These models were generated over 10′s of millions of web transaction logs, which encode which user was solicited with which ad, and whether they responded.

The results of deploying these predictive models were strong.  They increased the revenue of the website by almost $1 million per year.  More specifically, the response rates increased by 25% and revenue increased by an observed 3.6%, later reported at 5% by our client. (There is a difference between the response rate and revenue improvement levels as a result of different pay levels for different ads.)  Since this business was already successful, earning a monthly revenue of $1.5 million from ads, this incremental but significant improvement amounted to a lot, and provides a great ROI for this predictive analytics initiative.

This is the kind of impact that you get from predictive analytics even in many cases where you already have a well-engineered system in place.  For example, in the insurance sector, there’s a great win when predictive analytics successfully improves actuarial methods to reduce the loss ratio by, say, 2 to 5 points.