Are You Ready for the Coming Revolution?

Few would argue that the past few years in web analytics have been, well, intense. The emergence of Yahoo Web Analytics, multiple management shake-ups at WebTrends, Adobe’s acquisition of Omniture following Omniture’s acquisition of Visual Sciences, WebSideStory, Offermatica, Instadia, and TouchClarity, and the continued push into the Enterprise from Google Analytics. From where I sit we have seen more changes in the last 24 months than we had in the entire 12 years previous (my tenure in the sector) combined.

When I think about these changes, I find myself coming to the undeniable conclusion that our industry is undergoing a radical transformation. More companies than ever are paying attention to digital measurement, and despite my disbelief in Forrester’s numbers, an increasing number of these companies are forging a smart, focused digital measurement strategy. At the X Change, at Emetrics, and at Web Analytics Wednesday events around the world there is more and more evidence that this wonderful sector I call “home” is really starting to grow up.

And we’re just getting started.

If you pay close attention to the marketing you see from Omniture, WebTrends, Unica, Coremetrics, and the other “for fee” vendors you’ve surely noticed a dramatic change recently. Nobody is talking about web analytics anymore; the entire focus has become one of systems integration, multichannel data analysis, and cross-channel analytics.

All the sudden web analytics is starting to sound like, gasp, business and customer intelligence.

Eek.

Since it’s late and since this post will be over-shadowed by the hype around Google Analytics releasing more “stuff” on Tuesday I’ll cut right to the chase: I believe that we are (finally) on the cusp of a profound revolution in web analytics and that the availability of third-generation web analytics technologies will finally get digital measurement the seat at the table we’ve been fighting to get for years.

Statistics, people … statistics and modeling, predictive analytics based on web data, true forecasting, and true analytical competition for the online channel. Yahoo’s use of confidence intervals when presenting demographic data and the application of statistical models in Google’s new “Analytics Intelligence” feature are just the beginning. As an industry it’s time to stop fearing math and embrace analytical sciences that have been around for longer than many of us have been alive. It’s time to stop grousing about how bad the data is and actually do something about it.

Do I have your attention? Good.

Thanks to the generosity of the kind folks at SAS I have a nicely formatted white paper that is now available for download titled “The Coming Revolution in Web Analytics.” Just so you can see if you might be interested here is the Executive Summary from the document:

“Forrester Research estimates the market for web analytics will be roughly US $431 million in the U.S. in 2009, growing at a rate of 17% between now and 2014. Gartner reports that the global market for analytics applications, performance management, and business intelligence solutions was US $8.7 billion in 2008—roughly 20 times the global investment in web analytics. Among their three top corporate initiatives, most companies are focusing their efforts online, expanding their digital efforts Internet to increase the organization’s presence in the least expensive, fastest growing channel.

Today, a majority of companies are dramatically under-invested in analyzing data flowing from digital channels. Even when business managers have committed money to measurement technology, they usually fail to apply commensurate resources and effort to make the technology work for their business. Instead, most organizations focus too much on generating reports and too little on producing true insights and recommendations, opting for what is easy, not for what is valuable to the business.

Web Analytics Demystified believes this situation is exacerbated by the inherent limitations found in first- and second-generation digital measurement and optimization solutions. Provided by a host of companies primarily focused on short-term gains in the digital realm, not long-term opportunities for the whole business and their customers. Historically these companies worked to differentiate themselves from traditional business and customer intelligence, focusing on the needs of digital marketers. Unfortunately, as the need for whole business analysis increases, many of these vendors are playing catch-up and forced to bolt-on data collection and processing technology as an afterthought.

The current state of digital analytics is untenable over time, and Web Analytics Demystified believes that companies that persist in treating online and offline as “separate and different” will begin to cede ground to competitors who are willing to invest in the creation and use of a strategic, whole-business data asset. These organizations are using third-generation digital analytics tools to effectively blur the lines between online and offline data—tools that bridge the gap between historical direct marketing and market research techniques and Internet generated data, affording their users unprecedented visibility into insights and opportunities.

This white paper describes the impending revolution in digital analytics, one that has the potential to change both the web analytics and business intelligence fields forever. We make the case for a new approach towards customer intelligence that leverages all available data, not just that data which is most convenient given the available tools. We make this case not because we believe there is anything wrong with today’s tools when used appropriately, but because we believe digital analytics should take a greater role in business decision making in the future.”

Since I pride myself on the quality of my readership I sincerely hope that each of you will download this document and take the time to read it. More importantly I’d love you to share it with your co-workers, friends, and followers on Twitter. I believe we are at a critical juncture in our practice’s history where the skills that have served us all along are not going to serve us for much longer, but I am always willing to admit that I’m wrong and more than anything I love a spirited debate.

Are you ready for the revolution?

Posted Sunday, October 18th, 2009 | 27 responses | Add a Comment | Share, Save or Email

Matthew Tod

A very thought provoking post and white paper.Thank you.

I think it is clear that as an industry we are reaching a turning point – moving from ‘one size fits all’ tools to highly bespoke systems that are aligned with the analytics goals and capabilities of the business.

Even now data collected in simple tools is being used to change much more than just the customer experience or marketing activities, but when full alignment and integration is achieved expect digital insight to be used in boardrooms around the world as part of business as usual.

The work required to collect the ‘right data’, clean it and integrate it with other data sources, analyse it and then use the insight in an automated fashion cannot be under estimated in my opinion. So the next few years are going to be challenging as it takes time to build support for such a major change in the level of investment being made in digital insight.

These are but hurdles to be overcome – the future looks to be more interesting than ever for those of us who love data.

Matthew Tod
CEO
Logan Tod & Co

richard foley

Eric,
Love the white paper…totally agree Web Analtyics finally seen for what it is and what it needs to be.

Though i am courious about the investment numbers in Web Analytics. This is what I have seen.
- Web Analyitcs is extremely commotized market…GA gives it away for free and OMTR has given away or cheaply priced their WA deals for market share…I suspect Web Trends has done the same….Whereas, traditional Campaign Management programs can cost up to well over 100k per sale.
- A BI sale will cost over 100,000 as well.
- I have seen web analytics data go to the statisticians, which is seperate from the Web Anatyics department. One reason, a chinese wall to protect against PII, another reason give it to the people who already have the data.
- The cost to deliver over the Web is, last I check, 1000 times cheaper then traditional marketing.
- finally, I wonder if outsourcing doesn’t play a role. IT has to focus on other areas and they always have a seat at the table.

Therefore, investment in Web Analytics and Digital Media will be a lot lower then traditional media, but that doesn’t mean it is not considered the most important channel and receives a lot of attention.

Just curious what your thoughts are on this.

Christopher Berry

Eric,

We have a massive labour supply side problem within the web analytics world as it is.

It’s awesome that the demand side for advanced analytics has shifted to the right. Finally. This might be the key in attracting and retaining top talent in the web analytics industry. (As opposed to sucking in and spitting out talent through the reportage grind).

I think that many web analytics professionals are ill prepared for this next wave.

I welcome it with arms wide open.

I caution that this is going to be more painful than most might suspect.

eric

Matthew: I fully agree, but I would offer the caveat that the changes are likely to occur painfully slowly (see Chris’s comment.)

Back in 2005 before maturity models were all the rage I documented what I believed to be a normal distribution of companies using web analytics tools based on a reasonable measure of sophistication. Ideally over time that curve would shift towards the right (more sophistication on average) … except it hasn’t, at least in the aggregate.

The reason we’re not seeing the shift is because of the huge number of net new participants in the data set; the Google Analytics crowd as it were. Huge numbers of companies are now streaming into web analytics which is persisting a normal distribution (at best) or perhaps shifting the whole thing to the left, not the right!

What I suspect we’ll end up with is a bifurcated end-user market, one where a small (but growing) number of companies are using truly sophisticated systems such as those I describe in the white paper, and a much larger number of companies will continue to use “one size fits all” solutions.

Not that there is anything wrong with that … you have to start somewhere. I’m just trying to temper my own enthusiasm for the kinds of amazing things you can do when you bring deeper tools to the table.

Thanks for your comment!

Richard: I usually see interest in the web driven more by revenues and less by costs. Companies do express an interest in the lower costs in the online channel, but attention does not fully seem to manifest until the online channel has grown to a significant portion of whole-company revenue.

Ironic, huh? If you paid close attention to the web you could grow your online revenues faster and lower your total costs of goods sold.

Such is life.

Chris: Agreed on all fronts. At Web Analytics Demystified we are working on a solution to the problem. I can’t tell you more right now, but I hope to be able to soon ;-)

Thank to all of you for comments.

Alex Brasil

Agreed entirely with Chris on this, and will add the additional caveat that I think you’re jumping the gun a bit here Eric. Let me make it perfectly clear– I agree this is the future, and it WILL happen, but I disagree with the timing.

From my rabbit hole, companies are just now going through the whole “we need Web analytics” and progressing to “What the F is web analytics and how do we do it?”. There is a massive labour shortage as Chris eluded to, and there IS currently a very specialised set of skills required to get a lot of info out of WA.

In short, the vast majority of companies that can say they have web analysts, are already well ahead of the curve. If they’re getting anything more than data dumps, they’re the elite of that curve. If they’re progressing to a true integration of WA tools and traditional BI, and doing it well.. well, that’s like the bleeding edge of the bleeding edges’ edge.

eric

Alex: In terms of the timing you certainly could be right. Plus, you’ll note that I’m not saying anything in the white paper that hasn’t been said before for the most part. The difference between this same statement five years ago and now is that ** now there are companies who are actually executing and benefiting from the vision I describe. **

Between Teradata, Unica, SAS, and SPSS there are a growing number of companies who have powerful multichannel (whole customer) stories to tell. Seems like every conference I go to I meet someone new who says, “Yeah, we’ve been integrating web logs into SAS and building models based on the result.” At X Change it was Kaiser Permanente, before that it was a well-known Financial Services firm (I have agreed not to same) … I wonder who it will be at Emetrics this week?

But yeah, most of us are just poking around at the edges of what I’m describing and still trying to get our arms around the basic problems. John Lovett alludes to this in his post today at Analytics Evolution. Even those companies who have web analysts really aren’t doing much in the way of the activities I describe in the paper …

… but they will.

Thanks for your comment!

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Oliver Schiffers

I think another vital part of the change of our industry is not only in the tools but in the whole area of people, methods and technologies that are able to understand and act on online behaviour, campaigns and transactions.
So I think key for us will be leveraging our assets in utilising the data collection, data understanding and insights / actions towards more integrated technologies driving the whole online experience and transactions. Analytics is the hub to all of this. I would say if we stand up as experts in this area we can drive the vision of personalisation, targeting, customer satisfaction and -experience, decision engines and customised transactions and offerings much more complete than any other group in the online industry.

Marcos Richardson

As usual, good white paper Eric!

I agree with Alex Brasil on timing and I think Eric you have hit the nail on the head with ‘attention does not fully seem to manifest until the online channel has grown to a significant portion’

There are a lot of other business critical systems at play such as CMS and CRM, email and sales systems and accountancy packages, as per you point out in your white paper. Roles within a company tend to be mostly targeted to either increasing revenue or holding on to the customer, and far less analysing BI. In truth unless the business relies on Search Engines for the majority of its revenue Web Analytics is just a ‘nice to have’. Today most business critical systems have very good reporting built in.

The number one mistake that Analytics Vendors make is thinking they are the bee’s knees. There are a lot of other business critical systems being used by companies that impact the bottom line ‘now’. For example, SalesForce.com, once you have filled in all the sales information opening up an API to Google Adwords is pretty powerful stuff. On the other hand Eloqua is a very intuitive email system which allows you to instantly respond to user behaviour and integrating eloqua into Webtrends or Omniture is particularly useful etc. I found the one thing that all of these Vendors of business critical systems have in common is they are all vying to be the central dash board for all KPI’s. In conclusion I agree with the rhetoric of the white paper but maybe it should be called ‘the coming revolution of Business Intelligence systems.’

Marcos Richardson
Head of SEO & Analytics
RBI UK

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Ashley Menter

Very interesting. I was just at a lunch with one of the co-chairs of the Wharton Interactive Media Initiative yesterday and he was saying essentially the same thing: customers now have the data they just aren’t using it to create deep insight.

I be curious to know if you’ve had any interlock with the folks at Wharton – it seems like they are heading down the same path – looking at ways to apply modeling, predictive analytics, and so forth to interactive media.

Elias Dabbas

Most of the stuff described in the white paper confirms that we are definitely in the stage where it is not about how well you know the technical tools (although it is becoming increasingly sophisticated), it is how much insights and action you can come up with, which is not “analysis”.
The analysis, number crunching, charts, graphs, etc. is done by the tool, and what we needs to be done is web “catalytics” instead of analytics. We need to be able to see those trends, make meaning and recommend actions.
The challenge for analysts, is that the tools are starting to do the catalysis for us (e.g. GA’s recent launch of ‘intelligence’).
Brings me to the very old question of, “will the machine replace us?”
I think we still have a very long way till we are replaced, but surely many of our tasks are being replaced by the machine, and we’d better be happy about it, and elevate our thinking and focus more on the human side of this discipline.

Fulton

“Proof that Consumers Will Surrender this Data: Google”

It’s hard see where this conclusion comes from as probably very many consumers are clueless to the data they are actually surrendering to multiple data collection systems when visiting websites.

Rather the title of the section (page 3) should be:

“Proof that website owners will surrender this data: Google”

Eric T. Peterson

Oliver: Agreed. The underlying thesis is that we’re all going to have to give serious consideration to the work we do and how we do it. And while this vision will not come to pass right away, when it does I think a lot of us might find ourselves unprepared from a people, process, and technology standpoint.

Marcos: Impossible to argue with you, nicely put. Think “Business Intelligence 2.0″ and we’re on the same page.

Ashley: I haven’t talked to the folks at Wharton … do you mean Peter Fader and that group? I’d love to engage in a conversation with them if you can bridge an introduction.

Elias: “Will the machines replace us …” is an interesting thought, kind of like Terminator for digital measurement huh? In short I think the answer is “no” given the simple fact that IMHO even with more advanced (third-generation) tools it will still take human smarts to analyze the data. Plus, it will always take a human being to explain the data to the rest of the company and make the case for taking action.

Even in the case of automation (testing, personalization) there is a human element — we design tests, evaluate scores, etc. My hope is that with “Business Intelligence 2.0″ we can get away from some of the wasteful arguments (”are these numbers right?”, “is this significant?”, etc.) and start to have a more productive conversation about how to respond.

Great question!

Fulton: Good point, and I could certainly have used your section header. But I’m not sure that the average consumer is really that mindless about data collection — certainly those folks actively deleting their cookies have a notion about data collection, the folks in the UK who figured out what Phorm was doing, anyone listening to the FTC here in the states wonder about data privacy online, etc. I think this is becoming a larger part of the everyday conversation …

But do you think for a second that if people knew exactly what Google knew about them it would ** really ** stop them from using Google? I don’t doubt there would be some saber-rattling and complaining, but would consumers really turn away from the awesomeness that Google gives away every second of every day?

I doubt it.

So both headlines are correct without a doubt. Great point!

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Tom Erik Støwer

You emphasize the importance of working real time in your paper. Do you think the “revolution” will have similarities to the shift towards agile methods in software development?

Also, what are your thoughts about web analytics for journalists and editors within news publishing? Is anyone writing about it (except me)?

Thanks for the white paper btw, I thought it was an excellent piece.

Adrian Palacios

Eric,
This was great. I think my eyes were glued to the paper all the way from work until I put my keys in the door of my apartment (good thing I take the subway home).
Me and a few others are trying to move our firm forward in the analytics arena. We’ve made the successful transition from using GA as a “hit counter” to analysing based on goals and enabling our media planners to make advertising decisions based on that info. Recently we implemented GA with a lightweight CRM system, and have been pursuing discussions of integrating all of that with a CMS system to do more of what you outlined towards the end of the paper.

I am not a statistician, so I have to ask others for some insight (or even pointing me in the right direction) on my situation: how do you conduct BI, or Analytics 3.0, for a firm that manages websites that garner only about 5,000 to 10,000 visits a month? I’ve had another analytics’ industry leaders tell me by email that with numbers like that, instead of doing any fancy analysis I need to focus on just getting more visits period. Yes, more visits is a goal, but I feel our situation is unique. We advertise luxury real estate, with price points starting just below $1M. I feel the potential pool of visits is therefore very small, and the amount of leads smaller, and the amount of walk-ins to the site smaller yet, and then the number of buyers smaller still (we advertised for a building that had *16* homes total, all well over $1M).

So, maybe the question should be this instead: are we chasing the pot of gold at the end of the rainbow, or is Analytics 3.0 is possible for a situation like ours?

@Elias: I’ve thought about your question a lot. I’ve seen some awesome tools for things like analyzing in-store displays, like how website activity is analyzed, and it made me ponder the impending end-to-end measurement of our lives (gives new meaning to Eliot’s Prufrock measuring out his life with coffee spoons); however, my initial reaction is that there will always be outliers, and thus a need for human involvement. Just a thought :-/

eric

Tom: I think the biggest (or at least most noticeable) shift will result from the blending of web analytics and traditional business and customer intelligence tools. Deserved or not, BI tools get a bad rap for being slow and complex, and are best known for providing depth in the results they provide.

Conversely, web analytics tools, at least the best of them, are known for providing fast (even real-time) results from relatively easy-to-learn systems, but get dinged for not being particularly deep.

My vision of “Business Intelligence 2.0″ is the convergence of these types of tools — “easy” to use applications that also provide depth in their results. You see some of this out there today in applications like Quantivo and others who bring a more traditional BI focus to the digital landscape. I suspect we’ll see A LOT more of that in the next three to five years (maybe sooner!)

Regarding journalism, while a little dated you can have a look at a paper I did for the Newspaper Association of America (NAA) awhile back. Free download here: http://www.webanalyticsdemystified.com/sample/KPIs_for_Media_Properties_-_Newspaper_Association_of_America.pdf

Adrian: I’m glad you weren’t driving, that would have been a disaster no doubt! ;-)

Regarding your point about small sample sizes and statistical analysis, I’m not a hardcore statistician but I have worked with enough of them to know that you can surely conduct a more robust analysis with small sample sizes. What will be limiting is the kinds of questions you can ask and your ability to segment the resulting data, but based on the work I see people do all the time you should be fine.

Best example? Have a look at Google Analytics new “Intelligence” feature, fully grounded in good, old-fashioned statistics. If they’re able to provide this feature to low volume sites (which many GA sites surely are) then you should be good to go, at least with some questions worth asking.

Another good example is the use of Voice of Customer solutions like Foresee Results and OpinionLab. These solutions provide great insights based on relatively small samples all the time. In fact, if you’re not already using a survey tool that might be the best way to get deeper insights from the audience you’re targeting.

Let me know if this is helpful and if not I’m happy to hop on the phone to discuss.

Thanks to both of you for your comments and positive feedback!

Tom Erik Støwer

Thanks for the link Eric, I still feel that the specific problems that concern editorial staff aren’t addressed, at least not directly. Or maybe I’m missing something. Interesting paper, nonetheless.

I agree that intelligent depth is the next step, and necessarily tailored for each user “persona”. At least, in large organizations, developers, designers and content producers care about different things.

Adrian Palacios

Eric, I’ve been thinking about this and realized that I really do not have a good idea of what, if anything, I would like to start modeling/predicting at the Analytics 3.0 stage.

I know for certain that I want to start quantifying each stage: X amount of dollars for X amount of leads for X amount of people to the sales office for X amount of people closing on a new home, and the costs associated with each stage of that funnel.

But what to model? Hmmm. I know the biggest thing is to map out a sort-of “efficient frontier” of advertising dollars spent vs. closings. In tandem with that, I want to know what creative and advertising sources produce the people most likely to close. Beyond these, with the limited data set we produce, I don’t know what else I could know/want to know :-/

Sidenote: I wonder how many others there are like me, who, reaching this stage, don’t even really know what they want?

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Matt Gershoff

This is a really good post. A couple of quick things to think about:
1)Online is real-time. Okay, so that is painfully obvious but it does mean that some of the approaches from database marketing /BI and what SAS is talking about might need to be recast a bit.
2)Just in time vs Batch Learning. One potential advantage with real-time environments is that each request provides the opportunity to provide the user with the ‘best’ response based on the most current information. In order to effectively provide this capability the organization must in some sense balance learning (trying out different options/policies) with exploiting (making use of the learning) – this trade-off is often referred to as the explore/exploit problem and is often looked at within the context of the Bandit problem (a wiki search for either ‘Bandit’ or ‘explore/exploit’ will provide a good start).
3)Most current formal approaches learn in a batch mode (e.g. AB/MVT but can include other supervised (regression) and non-supervised (segmentation) learning methods). Batch methods spend some time collecting response data, and then analyze/model the data. For hypothesis testing approaches, after a certain confidence threshold is reached the ‘best’ option is applied to the majority of user requests. Issues with Batch approaches:
a. Opportunity Costs. The longer the learning / data collection phase takes the longer the users are receiving suboptimal responses (see ‘Regret’).
b. Learnings are often perishable – one implicit assumption of batch approaches is that the environments are stationary. For the analytics situation, this just means that learned relationships tend not to change much over time. If this assumption does not hold, you can spend resources (time, opportunity costs) formally establishing a relationship that is actually unraveling over time. These unraveling relationships can be anything from the simple (e.g. what is the best banner to use) to the more complex: (e.g. a fancy Gaussian mixture segmentation model or look-alike targeting models (think regression or ANN)). Methods that continuously update these relationships will be needed.
4) Sequential Decision Making –websites are complex objects and methods that are able to account for this structure will be able to learn faster and more accurately. This is closely related to attribution. Desired behaviors may take place downstream in the decision path. Proper attribution improves the selection of decisions that take place several steps before the goal behaviors.
As we move forward there will be more pressure for real-time learning methods and to move the intelligence closer and closer to the decision points within each application.
Eric thanks for getting the discussion going and putting up the SAS paper – it is good stuff.

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