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 Solution Provider Interview-Mary Grace Crissey, Global Marketing Manager for Analytics from SAS.: Play Now | Play in Popup | Download

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 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 above.

Transcript:

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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Greetings WOW Members and Web Professionals everywhere!

Bill Cullifer here with the World Organization of Webmasters (WOW) and the WOW Technology Minute.

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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Greetings WOW Members and Web Professionals everywhere!

Bill Cullifer here with the World Organization of Webmasters (WOW) and the WOW Technology Minute.

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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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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Today’s topic is Predictive Analytics and this is the second 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:  How do companies benefit?

A:  Well, the business case for a predictive analytics initiative is clear because having a predictive score for each customer is so clearly actionable.  For example, knowing which customers are worth targeting for an offer is about as actionable as business intelligence can get.

To be more specific, let’s say we generate scores to predict the risk each customer has of canceling or defecting from a subscription-base service. Targeted customer retention has impact on such businesses — such as ISPs, magazine subscriptions, online dating, you-name-it.

Retention offers - say a discount - are usually expensive.  You can’t extend the offer to all subscribers.  The only way to target a retention campaign precisely where it’s needed is with predictive scores that earmark which customers are most likely to leave.  When you follow these predictions, you don’t expend the cost of the offer on customers that don’t need it. So, the campaign ROI is much better, growth rates improve and bottom-line profit increases.

So, in general with predictive analytics, each customer’s predictive score informs actions to be taken with that customer.  You just can’t get more actionable than that.

To listen to the entire interview visit: http://predictiveanalytics.org/?p=7

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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?

Â: 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.