By Gary Angel

There must be a word for an experience that is simultaneously exhausting and re-energizing, restorative yet draining. A word that captures at least roughly the way I felt this summer, kicking back with a brew after a day of white-water rafting. Empty but elated.

I can’t think of the word, so I’ll call it “xchanged”.

It really was fun.

As usual, I’m going to cover some of the ideas I got and thoughts I had in the next couple of posts. For now, though, I’m just going to take a “short” cruise over the last four days; what happened, what seems important, and what really struck me. If you were there, perhaps it will spark some pleasant reflections of your own and if you weren’t, I hope to throw in enough useful information to make the journey worthwhile.

X Change starts for me on Monday with our Think Tank Training day.

Here’s a true story…On Sunday night I remembered that last year, after teaching three 2-hour classes in a single day, I’d decided it was too much and swore that no one would do more than two the next year. Wish I’d thought of that a little earlier.

There’s no better way to start a day of teaching than by remembering how you stupid you are.

I started with a class covering the creation of an online data model; pretty much the same stuff I’ve been blogging about this year but the first time I’ve done the class. It went pretty well. I’m going to tune the class a little bit but on the whole I think it would pair beautifully with the class I gave last year on choosing a warehousing technology.

This year, however, I followed that up with a totally new class on creating a Customer Intelligence System – a complete integration of all Voice of Customer and Customer feedback systems. The idea is to create the kind of consistent QA, data classification, and reporting for verbatims (everything from Social Media, to online feedback and reviews, to traditional survey research and even call center) that we enjoy in our traditional structured data warehouses. Systems like this are the single biggest data opportunity in today’s enterprise and I covered not only the reasons why but the core components of a complete CIS.

I closed with the class I did last year (only slightly tweaked) on Use Case Analysis. This was last year’s highest rated Think Tank class and I’ll be disappointed if it doesn’t take top honors again. It’s a really good class and a joy to teach. It was also my biggest class of the day – so I’m hoping it’s a rare case of reality matching expectation!

Voice a bit strained (no more than 2 classes next year – remember!), we all headed down to the hotel’s dock and onto our ship. What could be better than a great conversation on Mobile Analytics with Stephen Robinson of AutoTrader as we watched the sky slowly darken, the city lights slowly emerge, felt the air slowly cool, and our champagne slowly gurgle away?

We had dinner (and great conversation though hardly a word about Web analytics) as the boat slowly glided back across the Bay and then trooped over to an open-air wine tasting and chocolate reception. I think it was lovely, but my recollections are a bit hazy…

Tuesday we kicked-off with Elea Feit of the Wharton Customer Analytics Initiative. The WCAI is one of the coolest programs I know of. They farm out large enterprise problems in customer analytics to teams of academic researchers – who will take 9 months to a year to come up with deeply researched, academic-quality answers. Fascinating stuff. One attendee told me that they had seen more hard facts in Elea’s presentation than in five years of reading Web analytics blogs. Then added with a slight tick – except for your blog of course. Hey, no tick necessary. Our clients don’t really appreciate us publishing their findings (it’s a requirement to work with the Wharton folks) and we would kill to be able to work on the sort of deep, deep dives they get to tackle.

Not only is Elea’s presentation worth checking out, you should go to, download, and then bookmark the WCAI website and all the research whitepapers that have been and will be published there.

It was the perfect keynote for X Change: thoughtful, serious, advanced and fascinating.

After that, of course, comes a day of Huddles. I started with David McBride of Comcast’s “Analytics in the Cloud” session. This was a true learning opportunity for me because Semphonic actually has a direct (not just client) interest in the cloud. We regularly take data feeds for one-off analysis projects and run then onto our machines and into SPSS, SQL-Server or SAS for analysis. We would much prefer to be out of the IT business and in the cloud for all of that activity, but we’ve been worried about issues like software licensing, cost-control on a project basis, and configurability. Pete O’Leary of Quantivo and Jim Hazen of SAS contributed some great technical insight into the problems and issues; and some of the enterprise experiences were illuminating. David’s experience in the cloud has been extremely positive and while their costs have grown, they work with significantly larger data sets than we typically have to process. I just don’t know of any other place than an X Change Huddle where you can get sophisticated, hands-on advice about things like the real issues of cloud-based deployments. Great session.

Next up was Matthew Fryer of Hotels.com’s “Getting the Data to Tell It’s Secrets Huddle” which is certainly more in my wheelhouse. This was my first chance to really talk to Matthew in-person and I thoroughly enjoyed the session. I will say, however, that my own focus was probably a little different than most other folks in the Huddle. I was hoping to focus more on repeatable techniques for analysis – of which there are a paucity in Web analytics. My view is that successful analytics (at least for a consultancy) need to have a high-level of repeatable success. Market Basket analysis is a good example. Every time you run a Market Basket analysis, the findings are unique but the method is identical, the findings are similar in structure, and the results nearly always interesting. We need some of that in our discipline and right now, only attribution analysis comes reasonably close (and that, as I complained in my recent Facebook note on ClearSaleing, isn’t typically done with Web analytics tools). I’m going to tackle some ideas I have (based on projects we’ve done) for repeatable analytic solutions in an upcoming post.

Last Huddle of the day was Ian Gruber of Walmart’s Huddle on Scorecarding. Both Ian and I lamented afterward that we didn’t have a projector to talk through examples; most Huddles don’t require that but it would have been nice here. Next year I’ll make sure that’s possible. Lively discussion here, though. As usual, Bob Page (eBay) is way out in front with his approach. They’ve scrapped most standardized reporting at eBay (except high-level financials) and have built an in-memory cube that the CFO and team use dynamically in meetings. This certainly addresses one of the big issues in the Huddle, which was how rapidly reports tend to age – and it solves a major BI problem which is the time-lag between information requests and answers. I have two concerns about the approach, however. First, like much of what’s done at eBay it requires a very sophisticated management team; that’s something not everybody enjoys. I’m also concerned that this approach is better for financial and operational reporting than online marketing and that there are some issues with embedding intelligence into such systems (like seasonality). I’ve got to think about it some more.

After that, we took a short break (my one chance to go swimming) and then convened outside by the Bay for sangria and our Spanish-themed dinner. Tapas may be a bit of a stretch for even the best hotel, but I was happy enough on a warm night with sangria, a great Flamenco group (that’s something I didn’t expect to write), an amazing burnt-copper moon hovering over Coronado, and an enjoyable table with Matthias Bettag of Bayer in Germany, Paul Phillips of Causata, and Gary Church of the Allant Group (fellow Indiana guy) talking everything from X Change in Europe (a gleam in my eye) to flag football for boys and how my daughters only seem to like very expensive sports. A few last conversations over a slice of Spanish Apple Pie, heave a long, contented sigh, and head off to bed. No late, late nights for this puppy.

Half way done and very tired!

By Gary Angel

I’ve hopped down to San Diego for X Change and between pre-Conference prep and the first week of the National Football League, it might be a minor miracle if I finish this post (but obviously miracles do happen). So if you read it sometime around the 18th, don’t be surprised! Tomorrow I start with Think Tank; I’m teaching classes on Use-Case Analysis, Creating an Enterprise-wide Customer Feedback Analysis System, and, of course, a class covering modeling online behavior based on this year’s work and blogs. All very cool stuff.

And speaking of cool stuff, you might be surprised by just how cool some of the current technologies in online data enrichment actually are. If, like me, you grew up in the database marketing world, data enrichment was about like fertilizer to a gardener: part of your everyday life and, of course, essential; but not the exciting fruit of your labors. Well…maybe that’s still true. Online data enrichment has some new wrinkles, however, that make it more interesting than you’d expect.

Connection Engine is a really good example of that.

I’ve known Connection Engine’s founder, Eric Kirby, for years dating back to his time at Merkle. Before Merkle, Eric was SVP at Doubleclick’s Email Solutions division; so he has a great pedigree in digital database marketing.

You can tell, because while Connection Engine is far from the only service of its kind, it’s really easy to use and well thought out. With Connection Engine, you can take your email lists, upload them, and seamlessly append and report on wide variety of enhanced data. It’s so easy, inexpensive and interesting that I’m at a loss to understand why ANY company wouldn’t take advantage of this type of capability.

Connection Engine sources data from some of the massive U.S. Consumer HH databases for their service – with a focus on breadth of coverage for email addresses. You upload your email list and within minutes, you can get fascinating demographic snapshot of your list:

If you’ve added a few key fields (like Customer Value or Campaign Source) you can slice all of your reports by those fields. That means you can quickly compare the demographics of, for example, emails collected via different campaigns or methods.

You can drill down on any of the metrics:

One aspect that I particularly like about Connection Engine’s reporting is the extensive use made of Baselines and Indexing. You can see, for any variable and any segment, exactly how your list compares to the Avg. U.S. HH list and you can see how any segment of your list compares to the whole. The indexicals make it a snap to quickly identify the unique aspects of your email list and each segment within it:

Core demographics like Age, Income, Family Status and Occupation are powerful and interesting.

That’s only a piece of what you can profile, however.

You also get basic GeoDemographics:

Because this data is based on HH Lookup, it’s more accurate and far more meaningful than what we are used to getting in Web Analytics. Imagine getting Home Value and Length of Residence statistics at the HH level for your list! In the right verticals, that’s golden.

The next slice of data is even more compelling – it’s Household Purchase data:

Note how the view is sorted compared to the baseline to instantly identify the highest relative buying proclivities for a segment or for the list as a whole. You can also take advantage of the vast amounts of data collected around HH interests (based on things like Magazine Subscriptions, Catalog Purchases, Memberships, etc.):

Connection Engine even provides a final “cheat-sheet” of the key indexicals for your list:

I think you’ll agree that it’s a very nice, clean interface and the data opens up a whole new world of possibilities. Keep in mind that this is a CONSUMER database – so Connection Engine may not be the best enrichment tool if you’re a B2B shop.

Everything I’ve shown here is part of the visible, front-end part of Connection Engine. This self-serve list enhancement for data mining is quick and very low-cost; you can even buy back those additional data items. But true to their roots, there’s another equally – maybe more – significant piece. Connection Engine provides modeling services that let you use the known characteristics and performance of your existing list to target new prospects. They also provide modeling to help you identify, on acquisition, the likely value of a new email.

Imagine that…a company that takes your data, enhances it and helps you actually use it to solve real business problems! What’s par for the course in the world of database marketing seems awfully innovative and exciting in the world of Web analytics. And by bringing a self-service online interface and some well-thought out baselining and indexing to provide direct access to a giant consumer database, Connection Engine has truly melded the best of both worlds.

Investing in your data is a critical step in the world of database marketing. Using data enrichment services, you can leverage your initial collection to create vastly more interesting data profiles. Those more interesting data profiles give you the opportunity to do real-world modeling and targeting. It’s the same in digital database marketing. Or, at least, it can be!

By Gary Angel

Before I get too derailed, however, I wanted to put out the next installment of my series on Database Marketing and Web analytics.

I often show a slide in my presentations that shows two big Venn-diagram circles. One is for Web Analytics and one is for Database Marketing. It looks like this:

In talking about it, I try to explain how our mission at Semphonic is to take the good stuff from the Database Marketing circle and move it over into the channels that actually matter these days (Digital).  So with a little animation, I slide everything over to look like this:

I don’t think anything better illustrates this dichotomy or serves as a more visible proof of how little use Web analytics data actually gets than the almost complete absence, until quite recently, of any attempts to really improve online data.

Contrast this with the huge market in data enrichment of traditional data. There are companies (very large companies) who have built a business of taking any piece of data you have and matching it up with what they have and then giving you back a whole bunch more data than you started with. Companies like Acxiom and Experian can take a single identifying piece of data (phone, address, email) and give you back hundreds – literally hundreds – of additional data items. To do this, they’ve built up vast Household databases drawn from almost any conceivable public (or acquirable) source. From census data to change of address records to phone  books to professional associations to property records, they have scoured out potentially interesting data and added it to their vast files.

Where data is worth something, companies INVEST in it.

Along with all this data comes, of course, a variety of services for cleaning and combining it. You can household records together (combining members of a family), scrub addresses and phone numbers, add gender (based on known data or even derived from your first name), age, income, occupation, ethnicity and even append a whole list of potential interests culled from catalog purchases, magazine subscriptions, and credit card data.

Until fairly recently, however, you couldn’t do much with online data and nobody was much interested in investing to improve it.

That’s changing.

Companies like Blue Kai and Blue Cava (not sure why Blue is a common motif) have created interesting data enrichment services around online data. Using these services, you can track (via 3rd Party cookie and other device identification techniques) a users site interests and you can weld together their behavior across multiple devices.

Services of this sort have come under fire from the privacy community – and certainly they come much closer to the “line” than traditional Web analytics. I’m not sure how I feel about this. I’ve always disliked techniques like “flash cookies”  that seem like willful attempts to bypass consumer control. It doesn’t seem to me, however, that 3rd Party cookie tracking falls into that category. I do worry that Web analytics practices will get conflated with the aggressive collection and sale of browsing information and that traditional Web analytics will end up the proverbial “baby” flying out the window with the bathwater. Still, I have to wonder if the online privacy community understands how much offline data has been made public, aggressively collected, and marketed wholesale. Like airport security, it doesn’t always seem like the lines we draw make much sense.

Indeed, the traditional Database Marketing companies haven’t been quiet in this realm. They’ve built up formidable email databases (email is the de facto key when it comes to online communication) and can provide a full range of services around this – from email append (you add emails to customers in your database where you have only traditional offline information) to full HH lookup services (you have an email and they provide everything else). Given legitimate collection and use, I don’t see how this practice is distinguishable from the collection and use of address or phone data and seems eminently acceptable.

In database marketing, data enrichment is a key part of the value chain. Adding, appending and cleaning data based on this type of enrichment greatly extends the value of each core piece of data. And for proof of how valuable such data is, you need look no further than the marketplace it supports.

For online data, however, the enrichment of data is even more useful.

To explain why, I’ll hearken back to one of my first posts in this series. The core method in marketing analytics is to create a bridge between the behavior we track and the customer and his intentions. This entire 2011 series has been an extended effort to explain how that can best be done with digital data. No matter how successful you are at this endeavor, though, it will still leave gaps in understanding – both about the visitor and the visit type. Data enrichment can fill those gaps – particularly around the visitor. If we assume that visit-intent is best assessed using behavioral techniques (and I think that’s right), there’s a strong argument that visitor-type is best assessed with a combination of your internal data (the Customer Data Warehouse) and external data via enrichment.

That’s the traditional path for segmentation based on visitor type and I see no reason why it shouldn’t hold true for online customers as well. It’s particularly important for online data because we often know so much less about our online visitors than our traditional offline customers.

Currently, we’re in the odd position of working hardest to enrich the data about the customers we already know the most about. That’s not entirely foolish (those are often the most valuable customers too), but it’s certainly not a comprehensive strategy.

At Semphonic, we’re starting to see vastly increased interest levels in the full range of online data enrichment providers. That’s the best evidence I’ve seen that companies are actually getting serious about online data.

So over my next couple of posts in this series (probably interrupted by X Change posts), I’m going to cover some of the types of data enrichment services worth considering and the companies who provide them.

[For a recap and links to most of the posts in the series, click here.]

By Gary Angel

It’s long been one of my ambitions to write a book. I do love to write (as every reader of my LONG blogs can probably guess), but as reliable as I generally am putting out words every Sunday, I have never yet put down word one in anything intended as a book.

My friends, on the other hand, have been prolific in this past year. Pamela Lund wrote an excellent book on the impact of social media on our lives. Bob Heyman wrote a book I’ve talked about several times on measuring ROI in Online Marketing. It’s a book that helped launch a new product line (Analytics Agency of Record) at Semphonic and to which I got a chance to contribute. Now, Marshall Sponder has released Social Media Analytics (McGraw Hill) to which I was also able to add.

I’m both admiring and envious of them all! They are each very worth reading and, at least for the foreseeable future, as close as I will get to being in print.

I wanted to drill-down into more detail on Marshall’s book; partly because I’m going to be writing quite a bit more over the next few months on Social Media measurement. I’m still going to be adding to my year-long series on Web analytics and DB Marketing Convergence, but a lot of the most interesting work we’ve been doing at Semphonic in 2011 is in the Social Media space. Some of it (which I got a chance to talk about a bit in my recent webinar with Scott) is around Social CRM and fits very well into my ongoing series. Other aspects of the work, however, really need to stand on their own.

Social Media Analytics is a great discussion of a very big topic and introduces a whole bunch of themes that echo my own interests and viewpoints. In some places, that’s natural. I got a chance to contribute heavily to the “Advanced Social Analytics” Chapter  (8) so it would probably be surprising (and distressing) if I didn’t like what I read there.

But that’s really just a small piece of the total effort. Marshall covers A LOT of ground in Social Media Analytics. The book starts with a broad discussion of Social Media but quickly dives down into targeting, handling internationalization issues, mining social intelligence, tracking Fans and Followers and understanding their value, measuring influence, scorecarding, content creation, monitoring technologies, and data convergence.

In almost every section, you get Marshall’s unique strengths as a writer: his enthusiasm for the topic, his hands-on approach, his passion for conversation and listening (the breadth of contributors to this book is pretty impressive), and his surprisingly practical perspective on things.

In the past few months, for example, we at Semphonic have been helping bootstrap a global social media effort for a giant technology company. We’re spearheading the measurement piece and, even in its early stages, it’s involved us in a complex set of novel and challenging issues around internationalization. So the problems of internationalization are fresh in my mind, and yet I would have never have expected them to show up as Chapter 3 in a book on Social Media analytics. Too practical and too problematic I would have thought.

No so. As in all of the chapters, Marshall not only talks the talk, he walks the walk. He used a variety of tools as he explored the topic and he talked to people (not me in this case) who were obviously deeply enmeshed in the practical difficulties of international, multi-language, multi-cultural social measurement. He gives a great overview of what types of difficulties you WILL encounter if you try to do something in this area.

Another of my favorite chapters is the one on Facebook fans and followers measurement. I’ve seen a number of studies of the value of a Facebook fan and I regularly get asked about these. All of these studies remind me of the old cartoon with physicists looking at a complex set of equations with a step labeled something like “Magic Happens Here.”  None of the Facebook fan value calculations I have ever seen are anything but subjective poppycock. Marshall deconstructs several of these measures, explains where the “Magic” happens, and shows how VERY subjective such calculations are. By putting several of these side-by-side, he makes it abundantly clear how flimsy they are. At some point in the near future, I hope to write a bit about how such a value COULD be derived. For now, I’ll say that Marshall’s discussion is the best I’ve read of why the current set of calculations SHOULDN’T be taken too seriously.

Interestingly, Marshall echoes Bob Heyman’s theme in another really good chapter – Monitoring Tools and Technologies. The discussion here is one Marshall is particularly well-suited for because he tries everything and so has a completely realistic sense of actual tool capabilities and claims.

The part I’m talking about, however, isn’t in the excellent discussion of why check-box approaches to product comparison/selection are inadequate (I particularly enjoyed the great discussion of the stupidity of comparing the number of sources crawled claims made by Listening Tool vendors as if they were real or meaningful), it’s the part about how we staff and hire social media measurement functions.

Listen to this: “…I have come to believe marketing and communications agencies are not the most appropriate entities to measure marketing or PR campaigns run on behalf of their clients, especially within social media. Too often there is an inherent conflict of interest, as MarCom firms measure their client’s online buzz, and data can be skewed, often unintentionally, to show the successful completion of agreed-upon campaign goals.”

That’s exactly what Bob and I walked away from our conversation thinking and talking about, and Marshall is right that it’s an EVEN BIGGER mistake in PR and Social than it is in the relatively “hard” disciplines of online marketing.

Social Media Analytics is a great overview of the field, with far more in-depth tips and tidbits than you’d reasonably expect in such an easy to consume package. In a field changing daily, Marshall’s ear-to-the-ground approach delivers something that is remarkably up-to-date and current. And his combination of hands-on approach (just getting the seemingly inexhaustible list of tool choices is valuable), careful listening, and clear-eyed perspective consistently deliver valuable insight on the real issues of social measurement.