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.



Andrew Edwards talks ‘Convergence Analytics’ and the Benefits of Tag Management

Written by Efectyv. Posted in Convergence Analytics

After speaking at a panel of web analytics industry pioneers at Digital Velocity to discuss the value of tag management, Andrew spent a few minutes discussing “convergence analytics” — his new initiative with fellow web analytics pioneer Rand Schulman — and to expand on the benefits of tag management.


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.


Convergence Analytics 3.0

Convergence Analytics 3.0

Written by industry insiders Andrew Edwards and Rand Schulman, Convergence Analytics 3.0 is a Free Guide written for Digital Marketers interested in understanding the most important trends, technologies and practices in analytics today.


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