Companies in the web analytics industry tend to position themselves as an agency – integrating analytics in a traditional core offering (often in the strategy/planning or in the evolution/optimization phase) –  as specializing in a tool/implementation or as a dedicated analytics consultancy. I think it is about time to move beyond this in several ways.

It is time to reiterate some things about where we as an industry should go and to extend some others

• We have to integrate our still limited reporting-analysis-optimization approach into two additional main areas of our agencies or the online departments we work in: Strategy on the one hand and Customer Experience Management on the other. I personally find the latter more useful and rewarding to customer/business objectives.
• Tools are one part of the equation, an important one, and an aligned methodology is the other part. Most companies in the industry decide on one or the other. It has been emphasized before: Working on an ecosystem of tools is essential, working with this ecosystem based on a aligned and defined methodology is the second important step. In my opinion it is useless to redevelop what is already there on the market but it needs to be intelligently combined, integrated and customized. The methods and people should not be underestimated, but the tools and their power shouldn’t be either.
• The industry and customers still tend to see analytics mainly as a vehicle to improve navigation, conversion, campaign clicks. We need to clearly go beyond this by:
  • Using data also to inform cost- and efficiency-based decisions when producing content, digital assets and media. This goes far above campaign analysis or attribution;. We are also deciding about internal and external production costs based on successful usage, sharing and customer value. (Important piece for ecommerce and Marketing Services Platform)
  • Using data to drive other production and cost-relevant decisions in product areas. User choices and behavior should inform product design, product cycles, production and inventory levels.
• For years,  we have analyzed data from specific behaviors to derive insights: Onsite navigation, choices, paths, conversion and offsite media usage/responses. Important insights will also come from ignoring (!) media and content, metadata around content and data, data sharing, user generated content production/consumption and more.
• We have to go beyond traditional agency boundaries. In Germany we still have large chunks of optimization efforts driven by specialized agencies with inadequate tools that focus only on their small word of campaigns or assets. Becoming holistic here even goes beyond campaign attribution or integrating all campaign data, to include all onsite behavioral and transactional data. What online businesses really need is a measurement and analysis agency or department to completely own and continuously enrich data sets on customer segments, behavior transactions and opinions.
• In this we need a new breed of analysis and insights, blended teams of data experts, information architects, usability and user experience specialists and researchers/planners. This will be a real differentiator and competitive advantage few agencies, consultants or customers take advantage of right now.
• We have to get ahead of privacy and legislation. The right path in the middle addressing customer concerns and business needs, in the middle of fulfilling legal obligations and questioning them when they are overdone is as important as open communication about data usage and its benefits to the users instead of hiding actions and intent.

Important hint: Germany’s most important digital conference “Next” has chosen its topic for 2011 – It is (and I really dig it) “Data Love”

The main reason why many companies are still hesitating to try out these options is that they are uncertain how to gauge their effectiveness. As usual, marketing executives have to show exactly how much ROI their initiatives will bring. There is a widespread tendency to measure the effectiveness of social media marketing using conventional online-marketing KPIs, and the current IAB proposal is not going far beyond this. I tend to say that this the wrong way to go. Conventional online campaigns have a set length of time and are judged on things like Visits, page impressions, Duration of Stay or conversions. Social media initiatives, on the other hand, are usually designed for the medium to long term, plus they pursue different goals, like developing relationships with users and positioning influencers for the brand. Instead of subjecting social media goals and ROI to standard measures of campaign success, marketing professionals should apply metrics used in customer service and CRM. After all, customer satisfaction and customer retention are good signs of the success of social marketing campaigns too.

Defining goals and developing a systematic approach

Before a marketing department can measure the success of an initiative, it is essential for it to define the goals of the initiative and to know what the company wants to achieve with, say, an application. What is it that will determine success? Will it be a certain number of followers or influencers? Or will it depend on reach? In addition to precisely defined goals, it is important to establish and follow a systematic approach that will enable comparisons with other social media initiatives.

A framework of this kind should at least include four components:

• Engagement: How will users engage and interact with content, apps and media in the social network?
• Impact: What effect will my content and topics have on users? Do they consume, interact or share?
• Stickiness: How long and how often are users interacting with the content or application?
• Quality of Reach: What is the quality of user profiles or user networks? Do they have large networks and can they or their networks be classified as influencers in the relevant area?

These items are important when defining metrics for different social media areas and tools, However, analysts and strategists will need to break down special indicators individually, depending on the goals of the company and the type of application.

Indicators for social media measurement

Social media – as a collective term – is not a single, neatly defined area. Each site has different goals and expectations, so different indicators are necessary to measure success. Just because you’re talking about Twitter or a Facebook application or even a banner on MySpace doesn’t necessarily mean you’re talking about the whole of social media. Standard KPIs like number of downloads are fine for a Facebook application, but very different indicators are needed for Twitter and online communities. A better approach for Twitter for instance would be to use listening tools and text-mining technologies to classify buzz. They give companies information about brand or product-related conversations, including how much is being said, how engaged the participants are and how serious the discussion is. More advanced tools like Twitalyzer can go far beyond that toward the quality of influencers and participant of the conversation. They do not ony reveal how frequently people send tweets on the subject to their friends, what keywords are being used, how often influencers contribute, e.g. by “re-tweeting” as well as the number of listeners and how they are being reached. Last but not least, they provide preliminary information about the tone of tweets. The posts of influential Twitterers can be monitored by a company and integrated in an internal workflow for further processing. Marketing professionals can measure the success of this strategy based on the quality and quantity of tweets posted on a certain topic and on the degree to which influencers “influence” their followers.

The most important thing, though, is that each platform needs its own individual set of metrics that take into account all four key components– Engagement, Impact, Stickiness and Quality of Reach. We still mainly see definitions of Reach and Engagement parts but this is not enough.

Aurelie said at emetrics: “Today Web Analytics Demystified becomes a truly global practice, and I am happy to be leading the way in Europe. Joining forces with Eric, one of the most renowned and respected consultants in the field today, is thrilling,” said Mrs. Pols. “I look forward to being able to leverage my tactical measurement experience and working with Eric to expand the strategic use of digital analytics around the world.”

I first read about Aurelie when ox2 came to my knowledge as the first Webtrends Insight Partner in Europe. We began commenting on each others blog and exchanged some friendly emails. When we finally met personally at emetrics Düsseldorf in 2006, I learnt fast that we are not only both passionate about our business but also soulmates in many areas beyond Web Analytics.

Eric Peterson stated: “At Web Analytics Demystified our goal is to help companies maximize the value returned from their investment in digital measurement technologies. In the past we have helped companies like Best Buy, Lowes, Disney, ESPN, LEGO, NATO, Shurgard, Anheuser‐Bush Inbev, ING, Toyota Motor Europe, Shering Plough, Bayer, Nestlé, Deutsche Bank, and more make significantly better use of complex technology. Aurélie is an excellent addition to the team and her ability to add value to our clients is immediate.”

Aurelie is clearly one of the people one rarely has the chance to meet: sincere, considerate, intelligent. I recommend working with her to everyone being serious about measuring and optimizing the online business, and

- Aurelie: I wish you all the best for your start into this new era!

Fantastic 1 – process

The overall approach of revamping Web Analytics always has to include a strong look at the process of Analytics and Optimization inside the organization. How does the process of implementing requests or needs for analysis or reporting look like? Does it include a cycle that questions and implements their relevance towards business or unit goals? Are KPIs and web metrics comparable and consistent across all units? What happens next? Is there a process to suggest optimization steps? Are these monitored and re-measured?
All these parts can but do not have to be implemented at once, you can always start with smaller steps and get mature in each of these areas as soon as you have insight about their real life performance.

Fantastic 2 – data

Yes, data. Not customers, events, interactions just pure data. Everything else is developed from it. You have to work on the overwhelming part of the Web Analytics picture that still consists of data collection, tagging, fragmented content creation and delivery, passing additional parameters to tags via different techniques, content/event mappings, integrating other data sources (or even –oouuh- logs), looking at privacy issues when getting a clearer picture on customers than what cookies deliver and all that stuff. Don’t underestimate the effort to roll out all these on a bigger portal that may consist of several applications and suites that deliver content, user experience, and functionality to people. It is rarely as easy as: Just implement this code in your CMS footer and you are done. It is not always hard but be aware that it might be, anyway.

Fantastic 3 – competence and people

Should we establish a center of competence with analysts who know the hell of a lot about web data, understand the business goals of all our units and can make sense of the connection of both? Or should we try to build up analysts in each of our units who are also subject matter experts for their part of the business?
I still haven’t decided what might be the best approach. But either approach is hard and means more to your human resources development than just sending people to some vendor trainings. You need to hire, bring in or develop people who are passionate about the web, about analysis and about business and you have to keep them with you.

Fantastic 4 – a solution and even more competence and people

In the end, or in most cases unfortunately at the beginning you will have to figure out that you need a solution that is able to cover your process, your fragmented data collection and is able to tell you all you might ever want to know from that data. So the challenges of all 3 steps mentioned before cumulate in your solution and vice versa.
You will require assistance from the vendor in terms of Web Analytics consulting also. Take a strong look at their professional service team and make sure they are not only capable of implementing and supporting but also in developing your people with their expertise and to support and enhance your process. Consider an independent consultant if they don’t.
If you got the other pieces right, you have clearly moved on from reporting to web analytics if your solution vendor can deliver on these pieces. As soon as you got the first 3 right, you will need a solution that is strong in combining every possible dimension of data, in segmenting and in filtering. You will also need to have adhoc analytics capabilities because otherwise your optimization processes can not succeed because of the iterative nature of web analytics: Every KPI you get and every analysis or report brings up new questions and new needs for filters or segmentations or new combinations of web usage dimensions or attributes and customer data from other channels.

Perhaps you will need new analysts if you covered all these steps and built a “Fantastic 4” web analytics solution. Perhaps what you called web analytics before was in reality reporting or marketing performance optimization. Iterative Analysis and true online business optimization is way more sophisticated than web reporting. Deriving value out of segmentation and combination of trends and behavioral attributes is another league than looking at KPIs.

News from Eric Peterson at the Emetrics summit and thoughts on the next maturity level of web analytics

4 thought leaders, all founders of their own business now

So now all 4 Gurus presenting the like-named sessions at emetrics are self-employed consultants finally. Eric Peterson announced a few hours ago, he had incorporated  Webanalyticsdemystified to work vendor agnostic and primarily in web analytics business processes from now on. Congratulations, Eric.

Not only from an U.S. but also from an international perspective I think all of us practitioners owe to Jim, Eric, Bryan and Avinash for taking the industry to the maturity level it is today.
What could be the next necessary steps to take?

4 practitioner gurus
would help to grow the industry thoughts further from an inside perspective. They would be able not only to contribute their experience (20%, of practitioners, Eric’s survey revealed, have more than 5 years experience) but also contribute insight about internal obstacles, the level of understanding and actionability and rate and success of undertaken actions in of web analytic results.

4 European gurus
would help to grow development in a market that can become even larger than the U.S. They would help to go the next necessary steps from an European perspective, not only to learn from our north-american colleagues and experts, but to develop own market specific knowledge and a network of experts over here. I already have 2 candidates in mind, evenly distributed in gender, one is Dutch and one is Swedish.

4 dimensions of enterprise web analytics success
Avinash came up with 2 necessary dimensions of web analytics in a 90/10 distribution: people and technology. This will remain true forever, I think, however, mainly for companies taking their first or second step in web analytics.
Eric today expanded to 3 requirements of web analytics success: process, people and technology. I always found technology to be a key for overall success, once you have entered true strategic decision making on web analytics data and the need to segment on all possible, complex questions that pop up after each questions you answered in what I would call an iterative process of advanced web analytics.
Tools delivering on this level again promote the need for completely new analysts, analysts who can understand, develop and drive this huge, iterative process leading to new questions, new answers and after a while to strategic decisions, tools that do not limit the analysts mind inside the possible segments and combinations provided typically.
These new analysts would also need to be able to pull in any new dimension they need from key/value pairs, meta information on the page, and external sources in the organizations or inside prepared sets of data representing outside influences like weather, seasonality or political/social events. Understanding all this types of additional data and its relevance and interpretation is key to the new analyst. The analyst also needs to correlate the new data with every already known dimension without technology obstacles like reimporting, new cubes or even ROLAP limitations.

The definition of Web analytics business processes of course is a key success factor on this level. New Technology, that is able to challenge the current analysts to develop completely new expertise based on the ability to now iteratively develop, test and answer new questions after every answer they had before, grows them to completely new Web Analysts. A solid understanding and vision for understanding and correlating new sets of data and attributes to the current pool of information completes the dimensions needed for the next level of enterprise web analytics success.

Web Analytics is also a user centric art, as are many of the other disciplines that focus on several Website Goals that are always connected to the user and what he does or doesn’t do, what we offer him to do, and what he does instead.

A common visualization of such touch points and influences is given here to drive the discussion about these influence factors and overlaps as well as to allow other definitions of the big picture being added or suggested instead.

This visualization understands targeting and personalization as 2 very active subjects in online user centricity that rely on user behavioral data and its metrics or attributes.
On the other hand, user experience and optimization is a subject matter for some of the interactive agencies that is a key driver in their interest towards web analytics (Sapient, Wunderman, razorfish and others). Another user centric, but older and currently fulfilling thesis of markets that might also be conversations, brings another substantial part into the picture.

I would like to know: Do you see other subjects or overlaps? Other parts of the picture or missing sub-disciplines?

Or do you want to add your own idea for a completely different big picture by modifying this visualisation? Everything that you sent here or to my email will be published back and discussed further here.

The Visualisation is a friendly plagiarism of the famous Tag Bubble Visualisation of Web 2.0.