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The Dawn of Convergence Analytics at SES NY, March 26, 2013

Written by Andrew Edwards. Posted in Convergence Analytics

Convergence Analytics at SES NY March 26, 2013

The Dawn of Convergence Analytics

The combination of “big data,” access to cloud computing, powerful algorithms, and unprecedented visualization capabilities has created an emerging new class of analytics tools for the marketer.

It’s being called “Convergence Analytics”. It’s the marketing equivalent of “one ring to rule them all.” Though still in its infancy as a discipline, there are many vendors in the market, and their goal is to pull together data sources from multiple touchpoints from the web and beyond. They’re also using advanced data gathering and data regularization strategies to create a correlative dashboard-like experience for the marketer.

Will all of digital analytics look like this in two years? Find out how fast the landscape is changing.



Universal Predictive Convergence Analytics

Written by Andrew Edwards. Posted in Convergence Analytics

A few weeks ago I wrote about a digital analytics phenomenon wherein many analytics tool vendors had begun making claims they could join data from multiple sources and display the results in a set of dashboards for marketers. I called it Convergence Analytics.

Evidence continues to mount that this has begun to transform the measurement industry. One major development in this regard is that Google recently announced the beta release of its Universal Analytics tool, which purports to join data from multiple sources and display the results in a single dashboard for marketers. Didn’t you just read about that somewhere?

The goal of all marketing analytics has been, and is today, to “get the right message to the right person at the right time.” Companies like [x+1] have been pursuing this goal for many years with success amongst their enterprise customer base, and they have created a “decision engine” to support their real-time recommendations. And while others may have had other ways of saying it and doing it, all analytics tools have been supplying the marketer’s need for insight toward this goal for many years as well. Today’s trend toward convergence analytics makes a general improvement in messaging more likely.

The reason I advocate for convergence analytics is twofold. And neither of them is because I am in love with the idea of data being collected about folks in general (and I do believe we continue to need a forthright national dialogue about what consumers should feel comfortable with, and what they should not).

The first reason for my support is that organizations spend so much money on digital channels but typically get so little insight into success factors that it seems they still need a better way to understand whether they wasted their money or not. I think the “messaging” industries have been exploiting this gap shamelessly, forever. It would be great if they could be held to better accountability – a goal that is part of the promise of convergence analytics.

This brings me to the second reason, and that is simply that much of what passes for “targeting” these days seems more like carpet bombing than corrective surgery. I consider myself a fairly typical Internet user despite it being a focus of my profession. Personally, I limit my time online because I still find things like daylight and river breezes oddly enchanting compared with the latest news on Mashable. But that is what makes me sort of average, I think.

So, being an average user, what is the average quality of messaging I receive online? It’s below average. In fact, much of it is ludicrous.

I am still trying to imagine what behavior of mine prompts messaging to me about “one weird trick” to lower my car insurance rate. Or why shocked-looking seniors are overjoyed to tell me that Obama is giving away money for education. Yes, I have car insurance. Yes, I have an education in my background.

My guess is that too many advertisers are relying too heavily on ad networks to understand this user, based on some overly broad, not very target-savvy algorithms. In fact, I would say the word “algorithm” is probably used much too often to describe what are very unscientific demographic-based “buckets” that have improved little since the last census and that continue to supply the “insight” behind the ad buys of many less sophisticated networks. My guess is that they are almost never relying on anything like actual “decision engines” or deploying any “predictive analytics.”

The latest technology in measurement will be focused on more robust views of customer behavior. The data will come from browser-based behavior, mobile interaction, app usage, social gaming, call-center information, chat analytics, and CRM and POS data. Like Google’s universal data collector, this breed of tools (which will come from both new and existing vendors) will help enterprises understand and correlate user behavior as never before, and will soon represent the standard way for marketers to review cross-channel activity.

I hope it will also successfully perform the weird trick that will save me from seeing yet another ad about a weird trick to get either better insurance, more sleep, or less fat in my diet.

Convergence analytics. It is coming on fast, and we will all be the better for it.



The Dawn of Convergence Analytics

Written by Andrew Edwards. Posted in Convergence Analytics

The combination of “big data,” access to cloud computing, fancy algorithms, and unprecedented visualization capabilities has created an emerging new class of analytics tools for the marketer.

I call this relatively new field “Convergence Analytics.”

Think of convergence analytics as the marketing equivalent of “one ring to rule them all.” It’s still in its infancy as a discipline. But there are several players in the market already and they’re working hard to pull together data sources from web usage, call centers, CRM, campaign data, demographics, competitive data, and anything that gets captured off a click, keyword, mobile tap, or any number of other customer touch points. They’re also using advanced data gathering and data regularization strategies to create a dashboard-like experience for the marketer.

For some, this will sound like “business intelligence” (BI) recycled and molded into a more shapely, marketer-friendly package. And to an extent they would be correct. Several entrants in the market are calling themselves “BI for marketers” and their DNA is in the quant arena where power users build cubes and drilldowns in now-rather-hoary tools like Cognos and Hyperion. These tools are familiar to the serious data consumer, are not point-and-click, not report-oriented, and probably give marketers the screaming fantods just thinking about them.

But BI for marketers promises to be different.

Companies like Anametrix, GoodData, and Domo are taking what feels like a large helping of web analytics (cloud-based, report-oriented, visually appealing interface) with more traditional BI data sources and wrapping them into software services that help you run your business by the numbers. GoodData says that “infrastructure is a commodity” and what it means is that cloud computing has made it possible for even smaller businesses to access enterprise-level parsing power – to rent big data software rather than buying a big rig of iron and a team of Ph.D.’s to run command line requests. Anametrix wants you to connect all your vendor data, analyze across channels, and uncover “entirely new revenue opportunities.” It refers to itself as a digital analytics company but I think that’s actually too modest. Like GoodData and Domo ( “the user experience BI has been missing for 25 years”), Anametrix is part of what seems like an industry in search of an identity.

But that’s just a marketing and positioning factor. The end-user may care less about the slogans once they learn what kind of power is now available in these new offerings.

They are converging around the idea that you can analyze multiple streams of data in a dashboard, and that you don’t have to have golden pockets or a propeller-hat to see patterns and make inferences from disparate sources. Much as web analytics, a decade ago, would have taken the world’s geekiest log file and spun it up into an array of dashboards and comparisons that today marketers depend on for insight about their digital assets; these companies and others like them are building toward a new paradigm for all data. Leveraging the relative openness of data these days, the ability to utilize stored procedures, and the ability to extract, normalize, and load data into common formats, they are creating new ways to see hidden insights heretofore available only to the few. We are witnessing next-generation analytics platforms rising to prominence. Older, more mature web analytics tools are adding features all the time: application programming interfaces (APIs) are helping them cobble more and more information sets into their already robust but somewhat aging infrastructures.

But as often happens, it’s those who have taken a fresh look at the landscape; who have built new schema from the ground up; who have no laurels nor old source code to rest upon (or be hindered by); it’s the new kids on the block who will start driving the message home for marketers that it isn’t just about digital measuring digital anymore. It’s the whole show. It’s one-ring-to-rule-them-all. They may not quite have it nailed yet, but they’re in early versions, and they’re raising money and building fast.

We can’t say for sure who the winners will be. And we won’t try to say it’s all drag-and-drop either. Just like with any enterprise solution (software as a service (SaaS) or not), you still have to plan right and configure well and get the technical nuts and bolts tightened before seeing anything worthwhile. But don’t be surprised to see tools like these taking the place of one or more traditional analytics tools in the not-too-distant future.

It’s the dawn of convergence analytics.


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