PredictiveAnalytics.org

To benefit from Predictive Analytics, there are potential pitfalls to avoid along the way. In this interview with Vijay Desai, Ph.D., Principal Scientist, SAS, Dr. Desai shares his ideas and solutions as they compare across multiple industry segments.

Transcript:

Bill Cullifer: I am on the phone with Dr. Vijay Desai, Principal Scientist for SAS, S-A-S. Dr. Desai, good afternoon and thanks for agreeing to the call.

Vijay Desai: Thank you for having me.

Bill Cullifer: You bet. You recently participated in a panel discussion entitled Cross Industry Challenges and Solutions regarding predictive analytics and the theme included hurdles to jump and barriers to avoid when considering benefiting from predictive analytics. Can you summarize that discussion and what you walked away with?

Vijay Desai: Sure. Some of the things that we discussed were #1 analytics is becoming more and more useful in different industries and so as we go from industries where analytics has been used for a long period of time, in new industries there are some challenges. Some of these challenges include first convincing the users that predictive analytics can be of help, that predictive analytics is not going to take away their jobs, but by increasing their productivity it’s going to make them more valuable and help them generate more value and then even the data is becoming more and more available in different industries, you do not have what I would call targets. So, for example, in the case of fraud, you… target would mean identify the accounts that are good or fraudulent accounts. So, when you do not have targets available, it makes your job more difficult. So, in credit card fraud, targets are easily available because if your credit card gets stolen, you will report to the credit card company pretty soon that it has been stolen and so the credit card company can tag your account as being in a fraudulent state whereas in other industries where we are trying to use predictive analytics now say for example tax under filing, the tax under filers are not going to report that, “Hey, I am under filing my taxes this year.” So, you do not have that target information. Also, to take things further if you are… I have also worked in the area of network intrusion detection where you are monitoring a computer network, you don’t know which particular activity is malicious and you know which one is normal. So, again, you do not know the targets. After the fact, using forensics, you can identify some of the activities as being malicious or not, but you still are not sure you know whether you caught all the malicious activity or not and then going even one step further for bio surveillance; fortunately, we do not have any examples of bioterrorist attacks and so when you are building models, when you are trying to use predictive analytics for something like bioterrorism or bio surveillance, it is a much more of a challenging problem.

Bill Cullifer: Thank you. I certainly appreciate your perspective and for your time today.

Vijay Desai: Most welcome.

 

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In this third and final interview of Kim Larsen, Director of Advanced Analytics at Charles Schwab & Co. Kim shares his thoughts regarding strategies tp Maximize 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:

Kim Larson: That approach is my own sort of favorite because it’s very easy to do and it’s very intuitive. It’s using a k-NN, it’s like a nearest neighborhood classifier. So, it’s very simple essentially. First you identify say 10 or 20 variables that you think are great net lift variables meaning that they can differentiate conversion rates between test and control group and you can think of many ways to do that, but that’s actually quite easy for a single variable to see is this variable really a variable that can differentiate the self selectors from the… just the three other converters. Then, once you have… let’s say you got about 20 variables or so; based on those 20 variables, you find… you would go to a client, you will find say the 100 clients that look most like that client. So, you will basically find the 100 closest neighbors to that client like the “friends” of that client and then we are in that neighborhood of a 100 people, you basically just do a voting so to speak. You calculate the net lift weight for those 100 people and that net lift weight is the estimated net conversion rate for that client and then so forth. You can use SAS to do this or any other software that provides a k-NN classifier. In SAS, you can use [Inaudible] and I am sure about [Inaudible] and all those other packages have similar… [Voice Cross Over] it’s very easy to do, that’s the last one.

Bill Cullifer: Great.

Kim Larsen: Tends to give good results as well.

Bill Cullifer: Question for you. Is this a function of a business unit or information technology or of combination of both?

Kim Larsen: The LIFT model?

Bill Cullifer: Yeah, I mean, LIFT Model.

Kim Larsen: Yeah. I think typically you would… these models would be undertaken by some analytical team within your company like the team I worked in. I think typically most companies will have a modeling team and they would be the ones doing that. They would need to work with the marketing people to implement this into a campaign and the biggest challenge there is when you have got… when you have got a net model and you are telling people, “Well, these are the clients that I intend to target.” People will notice that these are not the typical people that you target. What I always tell people is that if you forget about all the fancy modeling and all the fancy stuff, there is a general rule of thumb that I have found. Your most engaged, wealthy, happy clients that love you the most, they are not the ones you need to hit with direct marketing. They are going to buy your stuff regardless, because they already like you, they already think you provide great service. Where you need to put your money is on the fence sitters, the people that are right below your best clients. Those are the people that are sleeping a little bit. They have all the propensity and the wealth and all that’s needed to really… to drive your bottom line, but they are not doing it fully because something… maybe they are not engaged with you. They don’t know what you offer, etc. So, that’s a general rule of thumb. Now, you as the analytical manager or the analytical analyst, you need to convince your marketing colleagues that that’s the way to go and that sometimes… I mean at Schwab it’s never really been an issue for me, but I know some other places that that can be an issue.

Bill Cullifer: Yeah, fair enough, I bet it can be.

Kim Larsen: Because you think…

Bill Cullifer: Then it’s an huge issue, right?

Kim Larsen: Yeah.

Bill Cullifer: And part of it is an educational process.

Kim Larsen: Exactly.

Bill Cullifer: Yeah, fair enough. Well, that’s an excellent perspective and I appreciate you sharing that as well and for your time today.

Kim Larsen: You are welcome.

 

 Maximizing Marketing Impact with Net Lift Models-Interview with Kim Larsen, Director of Advanced Analytics at Charles Schwab & Co (3 of 3): Play Now | Play in Popup | Download

In this second in a series of interviews (2-3) with Kim Larsen, Director of Advanced Analytics at Charles Schwab & Co Kim shares his thoughts regarding strategies tp Maximize 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.

A full transcript to follow in forty-eight hours.

 

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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.

 

 Maximize Marketing Impact with Net Lift Models: Interview with Kim Larsen: Play Now | Play in Popup | Download

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.

 

 The High ROI of Predictive Analytics for Innovative Organizations: Interview with John F. Elder, Ph.D., is the CEO and Founder, Elder Research, Inc.: Play Now | Play in Popup | Download

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.

 

 Predictive Analytics an Historical Perspective: Interview with David Katz, Founder & President, David Katz Consulting: Play Now | Play in Popup | Download

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.

 

 Marketing Operations and Predictive Analytics : Play Now | Play in Popup | Download

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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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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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.

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