Gary Zellerbach's Weblog: Focus on eMarketing and Electronic Software Distribution

Thanks to my colleague Paul Strupp for bringing a recent New York Times article to my attention: "Two-Thirds of Americans Object to Online Tracking." This is the conclusion of a recent survey conducted by professors at the University of Pennsylvania and the University of California, Berkeley. As I'm currently the Project Manager for an online marketing system that leverages online tracking, this was obviously of interest to me and our team. The title of this post does a good job of summarizing the general negative attitude of those surveyed, and I won't rehash what's in the article, trusting you'll give it a read if interested. After reading the article as well as the full study, here are some of my thoughts.

My first reaction is similar to this quote in the article from Stuart P. Ingis, a partner at the law firm Venable who represents the industry trade groups' self-regulation coalition: '"Just because many Americans are not in favor of something does not mean it should be banned," he said, citing negative feelings about taxes.' 

I think the tax analogy might be a tad extreme, but you could survey Americans and find a zillion things they don't like but that no one has any intention of banning. Ask me how I like the fact that TV ads are now inserted in the shows themselves (instead of just in between the action, where you can mute or fast-forward through them), and I could give a pretty good rant about how much I dislike that practice (OK, it's a personal pet peeve). But it's part of the price we pay for free TV, and I don't see any calls to make it illegal. I acknowledge that personal privacy and TV commercials aren't necessarily of equal weight in the "big scheme of things," but hopefully you get the idea, so I'll leave it at that.

Bottom line, businesses derive value from these systems, and at Sun, we see measurable positive results from our use of predictive analytics, many of which rely upon online behavior data. (The results from our recent release of Sun Machine Learning Engine v2.0 are even more positive, but I'll cover that in a subsequent post.) While we do aggregate data from Sun-owned domains, we don't aggregate from non-Sun domains like many ad networks do, and users in the study objected slightly less at least when it was all within the same company. 

We also have direct feedback from Sun customers who participated in a recent usability study about our project, and their attitude was quite different. They said they had an expectation that a large sophisticated enterprise web site would track their online visit, and if we used the data to provide them recommendations that were accurate and helpful, they had no issue with it. (The nature of our business was important too -- they said they'd feel differently if we were their financial institution, for example.) They noted how large our site is and that it can be tricky to navigate -- if we can help them find valued information more quickly, they were actually quite supportive. Granted, this was a very small sample and not scientific like the survey, but I just wanted to point out that some in Sun's audience have a different attitude.

That said, I'm as paranoid about my personal information and privacy as any one -- I always disallow third party cookies, opt-out of advertising networks cookies (as best I can), shred everything, etc. So I totally get it. Further, I agree the onus is on the businesses using online tracking to do a much better job of assuaging concerns and communicating. I suggest three best practices:

First, transparency is critical, and we must be up-front about what we do. Sun has an extensive Privacy Policy that I feel is understandable, honest, and forthcoming. We went a step further as well to add specifics about the online tracking we've implemented recently, which you can see on the new "All Recommendations" page:

It's tricky to try to explain this adequately in as few words as possible (on the assumption that users typically don't want to read that much). Hopefully in this short blurb, we communicate at a high level the way in which we've implemented our recommendations in relation to tracking.

Second, users should own their own data and be given control over it. I can't say we've totally got this right yet, though we do allow a general cookie opt-out which will prevent us from tracking anonymous online behavior. I think technology has a ways to go here, as making your "average" user manage their cookies at this level isn't a great solution. There's room for improvement and advancement in this whole area of customer data management and empowering them to manage it painlessly, though I think Sun is doing pretty well in comparison to many other companies.

Lastly, like any change you want folks to accept, you must answer "What's In It For Me?" It's a negotiation -- we're asking users to let us track their online behavior in order to make recommendations that we believe will benefit them (and us of course), potentially in a number of ways:

• Fast, (usually) free access to relevant information, including informative white papers and no-cost software downloads the user might not otherwise locate
• More direct acccess to potentially valuable promotions, specials, and offers 
• Aggregation of all the info into a single, simple user interface

To explain benefits explicilty would require yet more words, and that's a bit of a conundrum for the web experience. Hopefully the experience itself conveighs the benefits adequately (and customers can always read my blog to get the full scoop ;) .

Again, relating to this personally, I know that by disabling my ad network cookies, I might miss some targeted ads that might resonate with me, but that's my choice. On the other hand, I wouldn't want to deny Netflix the opportunity to recommend movies to me, so I explicitly tell them what I like and don't like and don't worry that they know about every movie I watch. Similarly, I don't try to turn off product recommendations on Amazon that I often find helpful. This is a trade-off that works for me. I'm hopeful that as we get further down this path, we can do a better job all over the Internet of transparency, data management, providing customer data control, and commuinicating (and delivering) benefits. If we do, maybe someday in the not too distant future, only "One-Third of Americans" will object!

I wrote an overview earlier this month about Predictive Analytics (PA), what it is and what sorts of benefits it offers. I've also written about the Sun Machine Learning Engine (SMILE) Project, putting PA to work on Sun's web sites. Today marks the end of SMILE Phase 1, as we are in the midst of releasing SMILE v2.0. You'll see the results on www.sun.com over the course of the next month, as we gradually enable more and more of the site with our new "Recommendations for you" carousel. Here's what you'll be seeing soon!

So I thought today would be a good time to touch on the results from SMILE 1.0. The initial release was simply about serving small text-only ads in the right hand column on many sun.com sites. Here's an example of a SMILE-served ad (outlined in red):

To evaluate results, we calculated standard metrics, such as impressions (number of times the ad was displayed), clicks (number of times the "call to action" link in the ad was clicked), and CTR (click-through rate -- number of clicks divided by number of impressions, as a percentage). I'm not able to share the detailed CTRs at this time, but suffice it to say they're low. That's not unexpected -- these are small ads, easily overlooked or ignored, and you shouldn't expect a lot of clicks on this type of banner ad. 

However, we also calculated Uplift, and that's where we looked to measure the power of our analytics. We did not have recommended ads for all customers, just return visitors with anonymous cookies that we recognized from earlier visits. Thus, we served many default ads, as well as many recommended ads. By comparing the CTRs of both, we can explicitly measure the influence of our predictive system, measured as "uplift." Here's a simple example:

• On a web web page, we show 100 SMILE-recommended ads that get 15 clicks, for a 15% CTR.
• On the same page, we show 100 default ads (same size, same location, just not personally targeted), and they get 10 clicks, for a 10% CTR.
• The SMILE Uplift in this case is 50% ((15-10)/10 * 100).

We carefully tracked SMILE Uplift for the last five months, and we saw an average uplift of 58.3%. As we serve millions of ad impressions, that translates into 1000's of additional clicks generated by our PA system. The ads often point to downloads or white paper offers that customers sign in to get, and thus we collect 1000's more contacts and what they're interested in, which we can then (hopefully) turn into qualified leads and ultimately new customers. So we can see a definite ROI for this effort. And keep in mind this was "version 1" of the analytics, which we're continuously refining, enhancing, and developing -- we expect ongoing improvements in future results.

Actual weekly Uplift gyrated pretty wildly -- here's a summary chart:

You can see general improvement over time as we improved the algorithms, steadying for the most part in the 40-80% range. In the last week, we released SMILE ads on the Sun Download Center, which had (as you can see) an interesting impact on Uplift! SDLC gets a huge volume of visitors, and most users are there to download and nothing else. We also found a large proportion of users there for whom we did not have recommendations (either because they were new or they'd deleted their Sun cookies). The result was a pretty big dip in CTR for the default ads, while we held steady on the recommended ads, thus the skyrocketing Uplift score the last week.

With the release of SMILE 2.0, we're completely changing how we do our measurements (it's a long story), so we'll be tweaking our weekly measurement system and reporting. We'll have new functionality and new measuring capabilities, and I'm looking forward to seeing the results from our newest release.

As I hope these numbers portray, we've demonstrated solid benefit to our emerging PA technology. It's a great start, but there's still a lot of upside potential remaining -- we're optimistic of delivering even more dramatic results in the future. 

As I've written about previously, I'm currently the Project Manager for the Sun Machine Learning Engine (SMILE) project, based on predictive analytics (PA) technology we're developing in-house. While I have a lot of experience building and managing complex web systems such as this, I haven't worked with PA technology before. I set out to learn more about it, for two reasons:

1. Since I'm the PM for this project, it's generally a good idea to know what I'm doing and talking about!
2. ROI is very important, both for this project and for ensuring the ongoing application of PA technology in general at Sun. This matters, of course, to Sun's management, and as you might imagine, it can't hurt to convey these benefits to our soon-to-be new owners as well. 

So, I set out to learn more about PA, what it is, and what benefits it offers. In this post, I want to share and consolidate some of my findings -- hopefully this will be helpful to others who are considering or starting similar projects.

Now, before I get much further, credit where credit is due. The title of this post, "If You Predict It, You Own It", is a tag line I like, taken directly from Eric Siegel's Prediction Impact site. I recommend this site for an intro to the subject, as it offers many helpful articles as well as resources, such as the Predictive Analytics World conferences and training programs. 

So what is PA?

• Wikipedia has a good article on PA, starting with a concise summary definition: "Predictive analytics encompasses a variety of techniques from statistics and data mining that analyze current and historical data to make predictions about future events." The Applications section offers an excellent overview of the many uses for this technology. Currently, we're using SMILE primarily for  Cross-sell and Direct Marketing. The last part of the article focuses on Statistical Techniques, an area I'll leave to the statisticians and engineers building SMILE.
• Back to Prediction Impact, scroll down the home page and you'll find an informative section called "Prediction Impact Publications and White Papers." To get started, I recommend, "Predictive Analytics with Data Mining: How It Works."

That will give you a good, quick intro to PA. What about the results? What's out there we can leverage to help sell such projects within our organizations? I did some of my own research into this and was also fortunate to have assistance from Sun's Digital Libraries & Research staff in locating a few additional publications. Here are some representative quotes/stats/images that make strong "sound bytes" in support of PA!

Optimizing Customer Retention Programs
by Suresh Vittal with Christine Spivey Overby and Emily Bowen, Forrester
October, 2008

• "Marketers have long relied on analytical techniques to identify and reduce customer churn. For instance, segmentation models help marketers to better profile customers and understand behavior, while cross-sell and upsell modeling deepens relationships and creates barriers to exit."
• "Marketers who target all types of respondents, not just the positives, risk wasting valuable resources on indifferent customers or at worst even triggering churn. This is especially critical in this climate of pressure upon marketing spend."
• "Telenor found that by only targeting persuadables, it was able to reduce overall churn by 1.8%. A more telling statistic: These improvements were driven by only targeting 60% of the potential churners. The benefits of targeting smaller groups is clear — cost savings achieved from fewer contacts by telemarketing and lowering of customer fatigue through selective contacts." 
• "The combination of increased retention rates and lower cost means Telenor will realize an 11-fold increase in uplift campaign ROI when compared with existing programs."

• "While the Internet and new technologies aren’t crystal balls, the sheer wealth of information that can be gleaned about today’s customers—and then applied toward anticipated future behaviors—is staggering. Failure to take this information into account is like leaving money on the table, or worse. You could simply hand it over to your competitors....Today’s smart companies use data, and the insight gained from it, to predict customer behavior."
• "In one example, American Airlines used predictive analytics to better understand the relationship between various customer segments and differential flight patterns. They achieved sky-high ROI results of nearly 1,200 percent in a period of two months."
• "IDC report studied dozens of companies and hundreds of predictive analytics projects. It found that the median ROI for the projects that incorporated predictive technologies was 145 percent, compared with a median ROI of 89 percent for those projects that employed only traditional analytics."
• Nice summary chart from this article:

"Mob Marketing" Webinar and Presentation
Suresh Vittal, Principal Analyst, Forrester Research
Jack Jia, CEO, Baynote
December, 2008

• "Relevant and personalized interactions are critical for enhancing customer experience."
• Baynote quoted the following benefits for their recommendation technology: 
• 40% Lead Lift
• 20% Net Revenue Lift (40% profit lift)
• 400% Engagement Lift
• 1000% Search Lift 

A vibrant and active amount of commercial activity also lends credence to the power and value of PA, and here's info on some PA providers:

• I ran into the aforementioned Baynote at a trade show a few years ago and was intrigued with their technology.
• We were recently approached by Angoss, offering "Proven Results with On-Demand Customer Analytics." In perusing their site, I found a nice collection of "success stories."
• Newcomer Demdex sounded interesting, based on a recent TechCrunch article.
• Iterating.com has a long list of PA products and providers.

And finally, just last week IBM bought perhaps the "granddaddy" of enterprise PA providers, SPSS, for $1.2 billion in cash -- a very serious endorsement of the power and value of PA! 

As noted, we are taking a DYI approach here, and you might be wondering about our results so far. I'll let you digest this info first, then follow up soon with a post on how SMILE is performing...

While my job responsibilities remain essentially the same for now, our team was recently "re-org'd" into the Cloud Computing Marketing group at Sun. As such, I quickly learned that to those outside of our industry, they usually have no idea what I'm talking about when I say I'm in the "Cloud Computing" group. "Cloud" is of course the buzz word at the moment, but it's still not a term that has permeated much outside the tech realm. My colleague, Neave Connolly, was facing the same challenges describing "Cloud" to her friends and family, so she undertook some research to look for a good, simple definition we could use for the non-techies in our lives (yes, this means you, Mom ;).

Neave found some great information so I thought I would summarize and share.

First, though, I often lead with the "utilities" analogy, as that usually helps. (Remember when Cloud Computing was pretty much "Utility Computing" a few years ago?) It goes like this: When electricity was first invented, companies would have to build their own power plants and hire their own electrical engineers to manage them. After time, electricity became a utility, and companies were no longer burdened with providing it themselves. Computing is (in theory, at least) following a similar pattern. Why should every company have to build their own data center and hire their own system administrators? Why not buy computing as a utility delivered over the Internet, just as electricity flows through wires into our factories, offices, and homes?

And that brings us to the Cloud Computing articles for those who'd like to understand a bit more...

• The first reference is a good, simple, high-level overview and definition of Cloud Computing from TechTarget.
• Next up, Gartner Highlights Five Attributes of Cloud Computing.
• And finally, there is a great deal of Government interest in CC for the efficiencies it can offer, and the National Institute of Standards and Technology (NIST) has weighed in with their Working Definition of Cloud Computing.

All the articles basically define and describe these key attributes of CC:

1. CC is sold on demand. In many cases, you might only need a credit card to buy computing power and services.
2. It is "scalable" and "elastic," meaning you can use as much or little capacity as you need, there is no upper limit on what you can use, and you pay only for what you use (just like electricity).
3. It is provided as a service, fully managed by the provider. The customer need not know "what's behind the curtain," just that they are getting a service that meets their needs. (Sticking with the analogy, do you know what power plant provides your electricity? And even if you did, do you know how many generators are in it, what their capacity is, etc.? Do you care?)
4. The "Cloud" is shared. Multiple users may be serviced from the same infrastructure and systems (virtualization being one key, but that's another tricky definition I don't want to get into right now!). 
5. It's Internet based. All services are managed and provided over the Internet using Internet standards and protocols (though as a still-evolving technology, there are bound to be new CC-specific standards emerging over time).

There are many interesting debates ongoing about what is or isn't CC, how does or doesn't it differ from "older" technologies like Grid and Utility Computing, where's the line between "public," "private," and "hybrid" clouds, etc. It just points to the youthfulness of this technology, while the huge amount of interest, attention, and debate (within our industry, at least) point to the almost unlimited potential this "paradigm shift" in computing holds. Will company data centers go the way of company owned power plants? We'll find out in time, but meanwhile, you hopefully have a better idea of what is meant by "Cloud Computing"!

In my last post, I displayed an image of a new display widget for personalized content on sun.com web pages. I noted it was a draft, and with good reason -- nobody was ready to sign off on the design at that point! The main issue was that it took up a considerable amount of the most valuable "above the fold" space on sun.com pages, and the content owners weren't enthralled with having their content pushed down the page. Our excellent design team mulled this over quite a bit and came back with what we feel is a great compromise design. (I am a big proponent of "compromising" and try to do so whenever reasonable, so that all parties feel relatively satisfied with an outcome. That's probably one of the reasons I'm about to celebrate my 24th wedding anniversary! But I digress...)

Here's a wire frame of the new solution. ("Wire frame" refers to more of an "outline" picture of the solution, not the final coded and realized design. Ignore the numbers -- they cross-reference content in a design specification. While we have the final design complete and coded, I'm not at liberty to preview it publicly just yet. Suffice it to say this wire frame has been translated into a very cool new widget!)

So here we see the recommendations as more of an overlay rather than a full page-wide component. This offers the immediate advantage of making it readily clear there is content behind the widget (and in fact you can still see the whole left hand top of the page), and no content had to be pushed down the page. It's also attention grabbing, and we do want customers to notice the personalized recommendations we have for them. We keep the fashionable "carousel" functionality so that you can click to see more recommendations, and we make it readily apparent how to close the recommendations to view the content underneath. Once closed, it'll remain in a closed state until the user opens it again or until the cookie that tracks this preference expires or is removed.

I'd be remiss in not acknowledging and thanking the design team that worked so hard to find not just a workable solution, but what I think is an excellent solution as well: Margaret Brown, Chris Haaga, Sara Shuman, and Andrew Payne.

Now that I've whet your appetites, you're probably wondering when this will be live. Sorry, I can't publish dates for functionality that we're still building, but suffice it to say we're making great progress.

I wrote an intro previously on SMILE, the Sun Machine Learning Engine, our new personalization system that utilizes predictive analytics. It went live in January, 2009, and we've been carefully measuring results since. So far, we are seeing higher click-through rates for SMILE served ads versus default ads, which is what we hoped would happen. But overall click through rates on these ads, whether personally targeted via SMILE or simply default, are very low, so we need more bang for the buck. We believe that the small ads served by SMILE in the right-hand navigation are easily overlooked -- many users simply ignore banner ads and/or the right hand column, concentrating on the central content of interest.

So our next step is to provide personalized recommendations in the body of the page. Here is a draft mock-up of a "carousel" type approach we hope to use:

There are numerous challenges with this approach, as the page real-estate "above the fold" is highly valued, but that's where we need the recommendations to be if we really want them to be noticed and of value. A likely approach will be to make the carousel collapsible, so users can close it up if it's in the way, and then open it to look for personally recommended content. We'll also provide a new "All Recommendations" page that is solely dedicated to showing all recommendations for visitors.

We did a usability study recently to test these concepts and were pleased with the results. Customers told us:

• They like what we're planning. We have literally millions of web pages, so if we can use our analytics to help customers quickly find the products, services, and content of most interest and value to them, we are saving them time and making them more efficient. Definitely a "win win" for all of us.
• They would likely not notice the recommendations if they're not centrally located high on the page.
• They don't have serious privacy concerns. Because we are an enterprise business site, they expect us to use analytics to improve the experience, and they'd appreciate the benefit. They would look at things differently if we were their financial institution, for example.
• If the recommendations are not accurate or useful, they will quickly learn to ignore them. So the onus is squarely on us to ensure our system is up to the task of making accurate, valuable recommendations that meet our customers' needs.

This is an ambitious undertaking involving a whole bunch of teams across Sun, from Design to Publishing to Engineering to Analytics, so it's at least a few months away.  I'll keep you informed of progress and of course announce when you can check out your own personal recommendations!

I've actually been on my new job for a number of months now, so it's time I start writing about it! After 10+ years working in the ESD (electronic software distribution) space here at Sun, it was time for a change, and so I took on a new challenge in the eMarketing realm. I am overall Program Manager on the sun.com web team for an initiative we call "Contacts to Revenue" (C2R). The essence of C2R is that we have zillions of web visitors, and especially downloaders, and we need to do a better job of identifying contacts and hopefully "nurturing" contacts into customers. There are several primary initiatives now underway.

First, we're creating new content we feel is of value to our customers and asking them to login to receive it. (If you don't already have a Sun Online Account, then there is a brief registration form that must be completed.) We feel this is a fair value exchange -- you tell us who you are, and we provide valuable information in return (examples include white papers, blue prints, webinars, and downloads). Here's an example:  Deploying Hybrid Storage Pools with Sun Flash Technology and the Solaris ZFS File System.

Second, I want to discuss Project SMILE -- The Sun Machine Learning Engine. We're building a new analytics engine to help provide more relevant content to web visitors. I'm the Project Manager for SMILE and pleased to say we went live with "phase 1" in mid-January. For the initial release, our goals were modest, focused primarily on improving the relevancy of small advertisements displayed on our main web site. We used a good ad banner server program previously, but it lacked sophisticated segmentation and targeting capabilities, so we built our own. If you are a return visitor to sun.com, we may serve you small ads that are more targeted to areas in which you've indicated an interest. All the ads are unobtrusive, appear in the right hand column, and are of this format:

For SMILE Phase 2, we're working on the ability to more proactively serve "Recommended Links" and other content of value based on web visitor's interests in our products and services.

We realize that asking for a login to obtain content and using machine learning and analytics to serve customers may be of concern to some. However, the bottom line is that we're in business to make a profit for our shareholders. Times are tough (duh), and it's imperative we do a better job of converting our web visitors to customers in order to grow our customer base and increase revenue. It's not a one way street -- we will continue to provide exceptionally valuable and unique content in return as well as striving to make relevant content more apparent and easier to locate on the web. 

In conjunction with these changes, we have recently updated our Privacy Policy to more accurately reflect how we'll be using customer data in support of these programs (among other updates). Please read it if you wish to be fully informed on this subject. (We also make it possible to opt-out of web personalization if you wish.) In any case, as this blog post and the new privacy policy illustrate, we're openly communicating about these new programs and changes. Our intent is to better serve our customers and our shareholders.