Maximize Marketing Impact with Net Lift Models: Interview with Kim Larsen, Charles Schwab & Co (1 of 3)

by lowes1 on March 31, 2009

 
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Kim Larsen is the Director of Advanced Analytics at Charles Schwab & Co. with headquarters in San Francisco, CA.

In this first of a series of interviews, (1-3) Kim shares his thoughts regarding Maximizing Marketing Impact with Net Lift Models. Net Lift Models are designed to maximize the incremental impact by targeting the undecided clients that can be motivated by marketing.

Check out the three minute interview on the Predictive Analyticswebsite.

Transcript:

Bill Cullifer: I am on the phone with Kim Larsen, Director of Advanced Analytics, Charles Schwab & Co. with headquarters in San Francisco. Good morning Kim and thanks for agreeing to the interview.

Kim Larsen: Thank you.

Bill Cullifer: Kim, you recently presented a case study regarding maximizing marketing impact with net lift model at Charles Schwab & Co.

Kim Larsen: Yeah.

Bill Cullifer: Could you summarize that session for the subscribers of this podcast with a walk away?

Kim Larsen: Yeah. Sure.

Bill Cullifer: Thank you

Kim Larsen: We call it the net lift model and net lift model is really motivated around when whether it is [Inaudible] or a resale or when you launch marketing, you measure the impact of the campaign by what we call increments [Inaudible] a test group of people that get a offer that’s related to your product. Then you will have core[Inaudible] groups for that book[Phonetic] like those people that did not [Inaudible]. The ultimate test comes of how well your program worked, is what’s the difference in performance in the[Phonetic] test group, i.e., for that [Inaudible] offer versus those that did not offer. Problem is that many times marketing says targeting strategies are not time[Phonetic] optimized increment impact. So, people might not [Inaudible] and make it a great response rate, but your core group[Phonetic] does this as well that simply [Inaudible] bottom line. That’s the motivation. The purpose of this talk was basically to talk about [Inaudible]. We basically think of your client base as consisting of three base segments.

You have a segment of clients that are not interested in whatever you are trying to sell. So, those, just leave alone and in fact if you market to them, it might have an adverse effect. Then, you have some clients that we call self selectors. These are clients that will mostly likely purchase your products on their own and marketing does not give motivation and actually leave them alone so to speak and they will buy the products regardless. The third segment is called the swing [Inaudible] and you know this sort of… this name is sort of derived from a parallel that I have been drawing with presidential candidates. Think about it. When you look at for example the latest [Inaudible] Obama, they spent most of their money on TV time in the swing[Phonetic] State of Ohio for that [Inaudible]. If you live in California like me or in Bay Area of California like I hadn’t seen a single McCain ad or Obama ad. However, when I went to Las Vegas for a conference, turned on CNN and I saw four ads for an hour. So, the presidential candidates have already figured out that spend your marketing money on the swing voter, the undecided and leave the decided alone and optimize market spend. So, that’s… we had the technical swing [Inaudible] they certainly have a tendency to buy the products by themselves, however, they need motivation. So, the purpose of net lift model is to find swing [Inaudible]. Two options when people do targeting, they end up finding the self selector. So, they build typical targeting models where really identifying self selectors, those models are those that… those models are typically done by looking at the clients that you offer the products to and then became converted, but what net models do… net lift models do that they don’t just look at that population, i.e., the population you mail to or contacted.

They also look at the core[Phonetic] group. Those are the people that you did not contact. Many of them also converted. Understand what’s different in the profile of self selectors versus the average converter and if you have those dimensions, you can focus in and find the swing [Inaudible] that will… it’s not always easy to do, but when you do it, it can be your impact of your marketing direct[Phonetic]. I have seen… we can talk typically about Charles Schwab itself, but I have seen campaigns where a typical targeting model to get to a incremental impact of about say five basis point that could essentially be that the people who you contacted converted at say 1%, so 1% of them bought products, but the people who you didn’t contact, they converted at say 98[Phonetic] basis points, so the difference was just two basis points which would not cover your cost. I think cases like this where a net modeling gains your net… your net impact into something very possible.

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The High ROI of Predictive Analytics for Innovative Organizations: Interview with John F. Elder, Ph.D. CEO and Founder, Elder Research, Inc.

by lowes1 on March 25, 2009

 
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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.

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Predictive Analytics an Historical Perspective: Interview with David Katz, Founder & President, David Katz Consulting

by lowes1 on March 15, 2009

 
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David Katz has been in the forefront of applying statistical models and database technology to marketing problems since 1980. He holds a Master’s Degree in Mathematics from the University of California, Berkeley. He is one of the founders of Abacus Direct Marketing and was previously the Director of Database Development for Williams-Sonoma.

Check out the three minute interview on the Predictive Analyticswebsite.

Transcript:

Bill Cullifer: David Katz, Founder and President David Katz Consulting in Oregon. Good morning Mr. Katz and thanks for agreeing to the call.

David Katz: Well, thanks for having me.

Bill Cullifer: Mr. Katz, you are a veteran of the statistical data model and database technology market and I am curious if you could share with the subscribers of this podcast an historical perspective of predictive analytics?

David Katz: Well, sure. You know looking back early days of you know computers in business was all about cost control or cost reduction. You know, people would do applications primarily based upon the idea that they are going to get more work done and use fewer resources too. Over time, with the growth of processing power and storing technology, this… the perspective gradually shifted. You know, the first step was more of a reporting, business reporting; you know getting more information about what is happening in the business and then a further evolution comes in to the idea of predictive analytics or any kind of analytical approach through the database that is developed. So, now the focus is much more on serving the customer better, getting strategic insight, and being able to be agile in the market.

Bill Cullifer: Yeah, great summary and historical perspective. Where are we today in terms of the opportunity in the market? Is this something that enterprise understands completely and very well and are taking advantage of it, is it on the other hand going mainstream?

David Katz: Well, I think people have a lot more awareness than ever before that there is opportunities there. I mean people understand that there are tools and what I see is really a wide disparity from one company to another and from industry to another. Obviously, the people who are involved in Internet technology or engineering, they tend to be much more savvy about the possibilities of predictive analytics. Direct marketers have been really in the forefront of it, but you know there is a lot of areas where they are just waking up to this healthcare and education. You know there is a lot of these industries are just at the point where they need to standardize the information, to make it more available for analysis. So, there is a wide variety of places in different industries and different companies they are at[.

Bill Cullifer: Thanks. I appreciate that perspective. If I were a CEO of a mid sized company today, what would you like me to know about predictive analytics?

David Katz: Well, really you know the heart of it is, you know, most reporting for businesses are for finding purposes. You can only look at a few variables at a time. Predictive analytics really comes into its own when you have you know a volume of data with many interacting factors that are kind of hard to tease out without the right tool. So, you know, you find that a lot of times you have many, many factors that might be associated with the outcomes that you want, but they are all correlated with each other and so you need some expertise and some tools to help you make the most of that data and that’s just kind of a rough introduction, but I think that you know the key thing is when there is more data that you can take in, in some simple report, that’s when you want to start thinking about more sophisticated tools.

Bill Cullifer: Excellent. Well, I certainly appreciate the information and your time today Mr. Katz.

David Katz: Well, it’s been a pleasure. Thank you very much.

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Predictive Analytics Interview: Gary Katz, President, Marketing Operations

by lowes1 on March 10, 2009

 
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Mr. Katz is a visionary and thought leader in the emerging Marketing Operations field. He is a veteran with more than twenty years of marketing and change management experience in the technology industry in corporate, agency and entrepreneurial positions, where he directed corporate marketing, communications, public relations, lead generation and qualification, investor relations, and employee communications programs.

What you will learn:

* Marketing Operations::The Engine Behind Predictive Analytics

The holistic application of predictive analytics to create a smarter, more agile enterprise is unquestionably attractive, even sexy. But predictive analytics cannot fulfill its considerable promise without a strong Marketing Operations (MO) organization behind it to give it direction, meaning and opportunities for practical application.

What is Marketing Operations?

A Definition:

Marketing Operations is a thorough, end-to-end operational discipline that leverages processes, technology, guidance and metrics to run the Marketing function as a profit center and fully-accountable business. It reinforces Marketing strategy and tactics with a scalable and sustainable enabling infrastructure, as well as nurturing a healthy, collaborative ecosystem, both within and outside the Marketing department, to drive achievement of enterprise objectives.

Transcript:

Bill Cullifer: The mobile phone Gary Katz founder and CEO of Marketing Operation Partners good afternoon Gary and thanks for being in the call.

Interviewee: Welcome.

Gary Katz: Gary you have given a presentation [Inaudible] conference, the title was the engine behind predictive analytics can you tell us a little bit about that?

Interviewee: Yeah, basically my focus is on emerging the older discipline of marketing operation and basically how bad it is now and the effectiveness of marketing operations within the enterprise is to see how effective predictive analytics will apply.

Bill Cullifer: Fair enough, could you expand on that a little bit.

Gary Katz: Sure, well marketing operation is increasingly becoming a function that organizations with the complex marketing requirements and that are spending a fair amount of money in marketing in a lot of marketing [Inaudible] in order to run the marketing function more like a fully accountable business profit on value [Inaudible] but basically what marketing operation does is leverage profit from the technology [Inaudible] giving the chief marketing officer effectively, a chief of staffing make sure that the operational issues [Inaudible] discipline and also that marketing plays a role in [Inaudible] and it change have [Inaudible].

Bill Cullifer: What would be a couple of the higher level walk away?

Gary Katz: Absolutely that is a good question. Well, it depends on the you know what the [Inaudible] role is obviously, but you know at the C level it in not only embrace marketing operations and they have it all ready, but if they are already doing marketing operations then do it more broadly and more of a holistic strategic and operational approach, but make sure that the execution of the marketing is up as effective as the strategy and the vision that they come up with firstly if there is good alignment between the two and if there is good alignment in the marketing function and other [Inaudible] functional organization that [Inaudible] play a key role whether it is marketing efforts.

Bill Cullifer: Is there a technical component to this or is it all strategy?

Gary Katz: Well, there is always a technical component, but I am not diving into the nut bolts and you know I don’t think I [Inaudible] against that [Inaudible] and so…but marketing operation is the way to lay the ground work, so that the infrastructure [Inaudible] and the different approaches work well, so whether that infrastructure happens to be in a way a predictive analytics approach that includes both subject matter expertise and some kind of technology [Inaudible] but also enterprise marketing management type of technology [Inaudible] technology digital asset management technology and management technology [Inaudible] so marketing operations basically is that it [Inaudible] both technology initiative to make sure that they are that technology adopted are within the organization understood utilized [Inaudible].

Bill Cullifer: Fair enough, well thank you for your time today Gary excellent summary.

Gary Katz: My pleasure Bill. Analytic is a very [Inaudible] area and I think it you will have a lot to do with the [Inaudible] marketing to be more scientific more predictable, more consistent which is the kind of thing we are looking for in order to prove the operational performance of marketing and thus have a bigger impact on the [Inaudible] of an organization to accomplish this.

Bill Cullifer: Very well said.

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Predictive Analytics Interview Dr. Zeller, Zementis

by lowes1 on February 23, 2009

 
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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.

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Predictive Analytics Interview with Andreas S. Weigend, Ph.D

by lowes1 on February 19, 2009

 
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Andreas S. Weigend, Ph.D. was Chief Scientist at Amazon.com, where he specialized in understanding how people behave online, helping Amazon build its customer-centric, measurement-focused culture.

Has built his expertise is built on my academic background in predictive modeling, machine learning, cognitive science, and behavioral economics. 15 years of consulting have enabled me to apply this knowledge successfully in the real world. He now works as an independent consultant with firms including World Economic Forum, Alibaba, Lufthansa, MySpace, and Nokia, helping some of the most brilliant executives around the world leverage user data to produce innovative products and business models. He also teaches at Stanford and UC Berkeley, and is often invited to speak at international events.

Check out the three minute podcast on the PredictiveAnalytics.org website.

Transcript:

Bill Cullifer, PredictiveAnalytics.org: Good afternoon. Dr. Weigend Bill Cullifer, calling with predictiveanalytics.org

Andreas S. Weigend, Ph.D: Hello.

Bill Cullifer, PredictiveAnalytics.org: Thanking you for agreeing to the call.

Andreas S. Weigend, Ph.D: Sure.

Bill Cullifer, PredictiveAnalytics.org: On the topic of predictive analytics, for the subscribers of this podcast us with a couple of specific walk aways.

Andreas S. Weigend, Ph.D: All right. So, let’s unpack the term predictive analytics. One element is analytic, the other element is predict. What predictive analytics for me is the focus on prediction; in the ’90s, a lot of emphasis has been on given a set of data, what insight can we get. Often those insights were not actionable. Then in the late ’90s, people focused on making predictions, but still predictions off dead data for dead data and I spend lots of tutorial time for top Wall Street quantitative traders explaining to them how to do backtesting and cross validation and all of those things and then in the last two years as the corporate communication has dropped dramatically and we actually are now able to do experiments very cheaply, we move from what I call dead data to live data. So, the problem has been no longer to be given a set of data what insights can we get, but given the problem what data can we get. So, that is the shift which I elaborate on and if you think about companies here, of course is a beautiful example, where people just give lots of data about themselves and if you reveal your intention then it is often much easier to make predictions than if we just have your data from the past. If you say, you want to go on a date on Valentine’s day, then that is information, that is we try to figure out from your clicking behavior on Facebook what you might want to do on Valentine’s day.

Bill Cullifer, PredictiveAnalytics.org:: Very well said. I am curious to know are there barriers to entry at this point or is it just that simply an educational process for those decision makers to understand how predictive analytics.

Andreas S. Weigend, Ph.D: Well, it truly is a paradigm shift and I typically call it the consumer or the customer data revolution and it is no longer about which analysis package you buy or which algorithm to use or what the mindset that CRM, customer relationship management, which we are at the end of predictive analytics seeing one who they should be sending their mailing to, what’s is called CMR, not CRM, but CMR, customer managed relationship, where the customer is now at the center and manages the relationship with potential vendors. Sometimes, I also call it that we move from e-business where the firm in the center to me-business where again the customer is in the center. So, it is not really about buying software packages, but it is about companies realizing that the customer is willing to give them information if he gets value out of it and not just to help the company, but ultimately it will help themselves.

Bill Cullifer, PredictiveAnalytics.org: Excellent. Well said. Terrific summary. I certainly appreciate the perspective and your time today.

Andreas S. Weigend, Ph.D: Absolutely.

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Solution Provider Interview-Mary Grace Crissey, Global Marketing Manager for Analytics from SAS.

by lowes1 on May 7, 2008

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Today s podcast is a continuation of the coverage of the topic Predictive Analytics, To help us better understand the topic from a solution provider point of view, I interviewed Mary Grace Crissey, Global Marketing Manager for Analytics from SAS.  To listen to the entire interview follow the “click to play” link at the bottom of this post.

Transcript of Interview with Mary Grace Crissey

BILL CULLIFER: Greetings WOW Members and Web Professionals everywhere! Bill Cullifer here with the World Organization of Webmasters (WOW) and the WOW Technology Minute. Today’s podcast is a continuation of a series of podcasts on the subject of predictive analytics. To assist us in better understanding the topic from a solution provider point of view, I’m on the phone with Mary Grace Crissey, Global Marketing Manager for Analytics for SAS. Good afternoon Mary Grace and thanks for agreeing to this interview.

MARY GRACE CRISSEY: Well thank you for inviting me.

BILL: You bet. Mary Grace, SAS recently announced the company supercharges predictive analytics. What exactly do you mean when you write that SAS supercharges predictive analytics? And can you explain to the listeners and the viewers of this podcast what predictive analytics means to you?

MARY: Certainly. Well, the word supercharging was tossed in there because it shows my enthusiasm on this topic here.

BILL: All right.

MARY GRACE: I really am a firm believer that you can get some magical power, some turbo-charge and super-boost out of your day to day business, be it making decisions as an executive leader at the company or as a Web professional designing and promoting your webpage. All of these tasks that are commonly done in businesses today can be supercharged if you take advantage of predictive analytics. So when I say predictive analytics I’m talking about looking forward into the future with a strategic mindset. Analytics is a term that represents numbers and metrics and when you want to find patterns, you’re digging through data that you have collected, say of people who visited your website, or demographics of who these customers are, you can look at historical data and there’s a lot of valuable analytic information that you can find. But when you want to look forward and project onto the future, that’s when you’re talking about predictive analytics.

What was once reserved to say an Ivory Tower of the scientific investigations where the academic analyses were punching and forming and computing these number-crunching, data-mining technique, now it’s catching the eyes of successful business leaders. So armed with this latest technology we can all benefit today from crunching word and numbers at the same time. We can automate manual tasks that used to take a lot of people power and actually make informed, fact-based decisions.

BILL: Excellent. Excellent explanation. I’m curious to know Mary Grace, from your point of view, how much of this is preparation and how much of the process, if you will, is technology?

MARY GRACE: Good question because actually the technology and data-mining is sometimes looked at as one little piece of building a predictive model, but SAS has an enterprise intelligence platform that actually spans from the beginning where you collect the data and where you present the information on the webpage, or Web analytics, and where you make the models and actually where you make the decisions. SAS is talking about a solution which truly does entail consultants and experts, people power, behind the software.

BILL: Excellent. Well said. Thank you so much Mary Grace for your time today and for your terrific explanation of predictive analytics. We appreciate it. Bill Cullifer here with the World Organization of Webmasters (WOW), on the phone with Mary Grace Crissey, Global Marketing Manager for Analytics for SAS. Thanks for your time Mary Grace.

MARY GRACE: You’re welcome.

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Predictive Analytics-Risks and Failure Rates

by lowes1 on April 3, 2008

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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.

Q: We’ve introduced  Predictive Analytics  the Business Benefits, an Example Business Case and a Client Deployment and for today’s topic I would like to ask that you address what are the risks when deploying predictive analytics and how often do they fail?

A: Well, the main risk is actually on the business process side, which is that buy-in and support for the deployment of a model won’t hold strong once that model is complete.  Now this risk is averted by following a standardized business process model known in one form as CRISP-DM, which stands for Cross-Industry Standard Process for Data Mining.  And it’s an iterative process involving both technical phases and decision-makers of various ilk to ensure that the technology will be positioned in a way that produces business value, and it will produce a model than you can produce over the data available, and the model will produce predictive scores that are actually actionable with regard to what’s possible on the operational side.

Now as far as the technical deficiency of a model — that it just doesn’t predict as well as you’d hope — the risk of this can be kept at a real minimum basically in three ways, all three of which are integrated within the standard process model:

1) The first of the three is that when you evaluate a predictive model, you don’t evaluate over the same data that you used to create it — that’s called the training data, the learning data.  The data used is held-aside data, called test data, which therefore gives you an unbiased, realistic view of how good that model really is, so you know that if it it’s not doing well on that data, you need to revisit during these pre-production technical phases, change the data or change the method until you get a better model.

2) Once you have a model that looks good and it’s time to deploy it, once again, here, the second of the three aspects of the process to help mitigate risk is to only deploy it in a small dose.  So keep the current method of decision-making in place, and then perhaps 5% of the time, or, let’s say, limited only to an hour or a day of processing, deploy the model so that it stands in contrast to how decisions are made currently, so you see can whether indeed the effect of the model is proven, and that profits have increased or that responses have increased, or what-have you.

3) And then, likewise, in the third manner, that same sort of “duality” of, basically, A-B testing — you’re testing “use this model” versus “don’t use this model” — you always keep that going, ideally, so that you have a small control set that keeps doing things the old way, or, in any case, keeps doing things in a way that does not require a predictive model.  So you have that as a baseline against which you’re constantly monitoring the performance of the predictive model, and you can decide, this predictive model has been doing well for a while, but now its performance is degrading — it’s time to produce a new model over data, let’s collect some new data and start the cycle over again.


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Predictive Analytics Interview-Client Deployment Example

by lowes1 on March 7, 2008

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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.

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Predictive Analytics-Example Business Case

by lowes1 on February 18, 2008

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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.

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