Who other web analysts admire


I was having an email conversation with Joseph Carrabis, Chief Research Officer at NextStage Evolution, and we were discussing an opportunity with the WAA, which basically boiled down to, “Which web analysts do other web analysts admire, in theory?”

I really had no idea who Joseph was or what his company did, since we had just met in a virtual sort of way. And so, I was somewhat awed at the depth of his response, which is right here:

I used our technology to analyze 50 postings (selected at random from those I’ve received since I joined the message board) to the WA message board in order to determine 1) how the posters would define and recognize expertise and 2) if they would accept said expertise as necessarily greater than their own.
The answer to 1 is to have an individual with the following characteristics

  • 35-45 years old
  • has been analyzing websites for 10+ years both in and out of corporations
  • has been doing web analytics for 5-10 years, ditto and also for large and small businesses
  • has spoken/presented at major conferences
  • has “hands-on” knowledge of at least 5 different analytics platforms
  • is product neutral (no commercial affiliations)
  • is patient with ignorance

The more of these you can hit the better. There are other salient characteristics but they don’t contribute as much to the necessary persona as the above do.
The answer to 2 is yes, they would accept expertise as necessarily greater than their own provided said expertise was demonstrated by

  • knowledge of transitions in the field
  • understanding of related fields’ impact on web analytics
  • industry knowledge

Much to my chagrin, “knowledge of related research” didn’t show up. Darn.
Hope that’s helpful.

I got this analysis back in about 15 minutes. I hope Joseph will get on my blog to tell everyone about the technology and how it predicts personas. And now I am dying to read his book, Reading Virtual Minds (I don’t think it has been published yet.)

Robbin Steif

Our founder, Robbin Steif, started LunaMetrics in 2004. She is a graduate of Harvard College and the Harvard Business School, and has served on the Board of Directors for the Digital Analytics Association. Robbin is a winner of a BusinessWomen First award, as well as a Diamond Award for business leadership. In 2017, Robbin sold her company to HS2 Solutions and has since retired from LunaMetrics.

  • Dylan Lewis

    Hi Robbin,

    Some of the qualities that Joseph has defined as pertaining to posts from admirable experts seem incorrect to me.

    The age requirement is probably the most problematic. I have met many admirable people (read: experts) in the analytics industry that don’t meet this first requirement but meet many of the below.

    At one time, believe it or not, even Eric Peterson was under 35!

    I am unclear how age is measured and calculated by analyzing web posts. Maybe by looking at the name and locating it on the web to piece together a timeline or age?

    The length of time required for analyzing websites is also somewhat troubling because there were far fewer websites, web analytics applications, and web analysts 10 years ago – maybe that is the key reason why they are admirable experts?

    Here is an interesting thread about the history of web analytics vendors that outlines vendors that had tools available in the early 90’s and beyond.

    Finally, the quality missing from Joseph’s list is passion for web analytics – but maybe that is implied in the analysis of 50 random posts on the WA Yahoo group?

    My (under 35 – 45 year old) thoughts on admirable analysts,


  • Eric Peterson

    Two comments:

    1) I only turned 36 last week so technically I was “under 35” a relatively short time ago.

    2) The link in Dylan’s post is broken. I took the Visual History of Web Analytics vendors down when I joined Visual Sciences, speculating that someone, somewhere would not want me using their logo now that I was a “competitor”. Stranger things have happened.

    Eric T. Peterson

  • And I thought women were hung up on their age….


  • Joseph Carrabis

    Tell everyone about the technology:
         I’ll start with a clarification. Neither NextStage nor our technology (“Evolution Technology” or “ET”) does what I think of as “web analytics”. When people ask if we do web analytics I voice a solid “No” followed with the names of web analytics products I’ve heard through the years. NextStage’s products and technology can be used as adjuncts to traditional web analytics. For example, clients use our WebSight product to learn why things happen, in essence to understand why the numbers are what the numbers are. A better statement would be that NextStage does behavioral analytics, but again not the way the market defines behavioral analytics. I think the current usage of behavioral analytics would make an ethologist bust a gut laughing. The best example of ET I could give is that it creates a blind which allows you to both observe live behavioral interplay and create environments from which desired behaviors will emerge.
         That stated ahead of time, what is ET? ET is about fifty mathematical formulae calculating over 80 variables (technically, mathematical engines calculating over channels) to understand how different people interact with information. What ET doesn’t understand already it learns by observation and interaction. This is documented in the patent app and in the online excerpts of Reading Virtual Minds. Examples of ET learning how people interact with information and a discussion of engines and channels are also available.
         Clients use ET for a number of purposes. A smaller client used two of our ET based products, WebSight and TargetTrack, to increase conversions by 100% in two months and then to modify their site design to market successfully additional products and services. A Global 500 client used WebSight to determine why their competitors were beating them in a specific market. A financial institution used TargetTrack to perform an analysis of one of their product paths against similar product paths of four of their competitors. TargetTrack revealed aspects of how their information was presented which was causing visitors to lose interest and have a negative reaction, and offered suggestions for modification. A pda manufacturer uses TargetTrack to help develop new marketing campaigns. In our initial meeting, they had already invested US$500,000 and six months work with focus groups, etc. TargetTrack analyzed the different campaign options they were investigating in about two minutes of computer time and came up with identical results — including suggestions for better targeting — for much less money. This caused one of the senior people at the pda manufacturer to tell their partners to use us and we’re now a strategic item in this company’s business plan. A Fortune 1000 used TargetTrack to determine which partners were going to be top revenue sources for the coming year before making any investments.
         Our technology has also been used to help shape political campaigns, determine potential employee suitability for a position, modify training materials to increase cognition and retention, determine why “well designed” marketing fails or wins, …
    How it predicts personae:
         TargetTrack made the determinations I sent to Ms. Steif. One setting of TargetTrack can determine who a “best audience” for information (a given presentation, be it webpages, a PPT, a product demo, software interface, political campaign, …) is, another setting can determine a “best presentation” for a given audience. I used the latter setting to generate the qualifications, which is how I was able to articulate a persona. TargetTrack “read” various emails I had in a folder then I “asked” it “Who would the writers of those emails consider an expert?”
    Hope that’s helpful.
    Thanks for this opportunity to share.

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