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.

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.

Today’s topic is Predictive Analytics and this is the first in a series of audio podcast interviews on this topic. Dr. Eric Siegel is a former computer science professor at Columbia University and the President of Prediction Impact.

Here are questions that I asked:

Q: What is Predictive Analytics and does it accomplish?

A: Predictive Analytics is business intelligence technology that produces predictive scores for each individual customer or prospect. What you need to do before employing predictive analytics, is first decide which customer behavior will be most valuable to predict, such as predicting which customer is most likely to respond to an offer or which customer is most likely to cancel their subscription. And the next thing you need to do is prepare the data. Your data, which is, essentially, your organization’s collective experience, is leveraged by predictive analytics to produce predictive models and in so doing you’re actually learning from experience.

Q:  What kind of investment in infrastructure required?

A: Well you can start with a pilot initiative for very little, in the way of hardware and software requirements.  And this is also, you know, a place to start, to achieve a proof-of-principle, demonstrating what kind of return-on-investment can be achieved, such as improved customer retention, or increased profitability of a campaign.  In this case, the core predictive modeling can usually be done with free evaluation software licenses or with a free open-source tool.

Having said that, however, it’s important to note that what you do need is expertise in predictive analytics, either by way of internal resources, and/or employing professional services.  This expertise is needed to optimally position this technology, in order to determine the kind of behavior that’s going to be most valuable to predict on the business side, and then, more technically, what data’s require to achieve that prediction goal, and how you really need to prepare that existing data you have now in the right form, so that the resulting predictions you end up getting will be valuable in that they’re accurate and business-actionable.  And then you apply what’s learned by way of directing, let’s say, a retention campaign, or selecting targeted content on a per-customer basis as far as what’s the product or message each customer is most likely to respond to.

Please follow the link to http://predictiveanalytics.org/?p=6  for the podcast audio interview.