History Is Overrated. (Atleast For Us, Atleast For Now.)

big and smallThis week history is on everyone's mind, especially in our little world of web analytics.

Yes history when it comes to the economy (no one likes a recession!), the war (ok no one like this for sure!), the elections (raise taxes! no, no, cut taxes!) or the Super Bowl (go Giants!!).

But with all that I am positive this week the thing top of mind for many web analytics practitioners is what to do with their historical web analytics data.

Do we switch or do we not switch? What happens to my tags? What about my contract? Where do I log it? Is it time to panic? I have seven years of history, help!!

For the first few questions I am afraid you'll have to answer for yourself.

[Although: While old habits / tools are hard to give up, it "takes about three weeks for a new habit to be hard wired in your brain" (source). That should give you some solace!]

This post tries to take a position on your last worry, historical data and its value.

We have all been brought up to cherish data. To love it, to adore it, to propose marriage, to stick together for better or for worse, to build increasingly vast and complex systems to keep it around and to tap into cloud computing along with a small army of people in your company to keep that data happy.

Most of the time while this sounds like a good mindset (/marriage), especially in the traditional world of ERP and CRM etc systems, on the web unfortunately this is can be a deeply sub optimal mindset (/marriage).

My proposal for you is to divest yourself of this mindset of keeping Web Analytics data around forever. If you, or your HiPPO's, have this mindset then a quickie divorce might be greatly helpful.

The über thought to keep in mind is that from the moment you collect it your web analytics data starts to decay and lose value. Oh it is useful on the first day, and the first month, but less so in six months, and click level data is nearly valueless in a year or so.

Why you ask?

  • Your Visitors "change" too much.


    Remember that at the end of the day almost all of us collect anonymous non-personal data from our Visitors. They swap browsers and machines and upgrades (if not outright blow your cookies away every day!) the data is less useful in identifying any usage trends and patterns tied to people.

    This is less a problem in traditional Data Warehouse environments.

rapid change 1
  • Your computations change too much.


    We were all on third party cookies and then you moved to first party cookies (say yes to this one!!). Most of your visitor stats just became uncomparable.

    You shift from logs to tags to tags of a new vendor to tags of your newest vendors and now you are going tagless! You are now comparing a bowl of chopped apples to fruit salad.

    Vendors and practitioners have changed basic formulas for measuring the core stats every every so often. They rarely reprocess history (too hard!) making it hard to provide continuity.

  • Your systems change too much.


    At the end of the day three things are captured by your analytics tool. The Referrer. The page URL. The Cookie.

    As you evolve your web site platform, from Interwoven to ATG, or move it around, hosts / servers, or add remove functionality like internal search or recommendation engines or multivariate testing or behavior targeting or other such things it usually impacts all three of those critical things that make up your data.

    Resulting impact can make your data disjointed.

    And I am not even touching changes from static html to dynamic html to personalized content to flash to flex to ajax to RIAs (Rich Internet Applications) etc etc (all of which again impact the three pieces you collect).

  • Your website changes too much.


    This is perhaps the biggest thing that most of us don't reconcile with. Most websites are on the Yahoo! "paradigm" and not the Google "paradigm". . .

yahoo google home page evolution 1

      . . . and that is quite ok (though the image above might suggest otherwise).

      Your home page three months ago is not your home page now (is it? hopefully not!). You killed your have product line pages last year, opting for product detail pages for SEO reasons. There was no PayPal last week. Maybe 2007 was your first year with the support and ecommerce sites merged into one.

      In the last six months you have learned so much about your business, about your data, about your visitors, about how fast you are being left behind (or are ahead of everyone else!). . . and your web presence has changed accordingly.

      Every change above changes the data you have and what value you can get from me three months from now (when you will have changed even more). It is important not to forget this.

  • Your people change too much.


    Sad as it might be the hardest people to find now are web people. Not just great web analysts, which we know are scarce (!), but web people in general. Front end, back end, middle tier, thin, overweight, rich, poor, newbies, experienced, all kinds are hard to find.

    As people come and people go their actions have a subtle but important impact on all aspects of your data ecosystem.

You have a few years of historical web analytics data. Give me the benefit of the doubt, for just five minutes, and think of the above five items with a kind of sort of open mind. Would you still keep terabytes of data from two years around?

The pace of change on the web is so tremendous (pages, sites, business rules, experiences, applications, data capture, what's right and what's wrong, what's doable). In this hyper fast environment all the detailed data, perhaps you'll agree, is not very useful because it has decayed too much.

opportunity chineseIt's the decay that is the root cause.

But at the same time it is also an opportunity. Because it means that you are not tied to the past in a egregious manner. It means you can think smart and move fast. If what you have now will be of less value soon then you will cherish the now more try to get something out of it.

It also means that it gives you the freedom not to be tied to legacy systems or legacy tools or legacy data. You can move forward to the next and better much faster than our Sisters and Brothers in the traditional world have been.

It means a lot more fun because you get to learn and adapt and get value and move on. It is damn exciting and damn liberating!

Yes, yes, yes, you knew this was coming . . . . .

Keep some history around. Aggregated data. Historical markers.

Weekly trend (counts) of Visits and Unique Visitors. Top ten referrers to your website by month. Monthly Bounce Rate. Weekly + monthly trends for Revenue and Products Sold. Perhaps Conversion Rate trends for your site Overall and important campaign categories. Top groupings of content consumed on the site.

Aggregated data, for your critical few metrics (that won't become less important with time!). And some revenue stats just to prove that you're worth it!

aggregrated data

Keep that around as long as you have it. It will all fit on one tab in a Excel Spreadsheet. That is all you'll need. They might be slightly different for you than above, but I assure you it will fit in a spreadsheet.

Or if you prefer here is another suggestions. . . . .

Keep your "click level" (detailed) data around for a year (assuming seasonality!) and your "session level" (aggregated) data for as long as you want to / have to (and it will fit in a spreadsheet).

In closing:

It is extremely difficult to get anything out of your web analytics that you can action right now. I humbly recommend that in the drive to conquer history that you don't forget the present and ignore the price that you'll pay every day that will come for every day that has gone by.

History is important in other context, but in web analytics tools, for now, change on the web reduces value from old data. This might cease to be the case at some point, but that point is some ways away.

Before you cut a big chq for your consultant, consider the above, consider what you are actually buying.
 

Oh and those of you worrying about switching tools & losing data, worry not (too much): Go forth and prosper!
 

As always it is now your turn. . . .

Agree? Disagree? What am I missing? Am are in Antarctica all by myself on this one? Am I wrong to think this is our own version of "an inconvenient truth"? What's your experience?

Please share your perspectives, critique, bouquets and brickbats via comments.

[Like this post? For more posts like this please click here, if it might be of interest please check out my book: Web Analytics: An Hour A Day.]

Comments

  1. 1

    Agree totally with you on this front. The HiPPO (which was sometimes, alas, me) always seems to worry about the backward trend. As if what happened two years ago in Q4 matters in relation to this year's Q4 performance or next year's Q4 performance. Especially when everything's changed–it has, because you're aggressively testing, right? (If not, give me a call, I can help with that…)

    The technique to address the concerns of changing things is to ask "so what?" As in, "so what if you can't exactly compare conversion rates YOY for this week? what negative impact will that have on your decisions right now?"

    You typically get answers that fall into one of two categories:

    * A Linus-like need for the security blanket of the old data.
    * An aversion to getting rid of GEHE stuff (Great Expense, High Effort
    to acquire)

    In the first case, you get responses like "we MIGHT want to look at that, so it's important the data is consistent" and in the second, the responses are more around "darn, it cost us $X big money to build and collect the data and now we can't compare it to anything."

    One surprisingly easy technique I've seen to help people get over all that is the asterisk.

    Make the change and when you're showing that graph or trend, simply put an asterisk next to the data point where you changed the systems, data collection process or what have you. The asterisk says "due to data changes, results are not directly comparable." It's amazing how that one ASCII character gets people over change!

  2. 2
    Joe Teixeira says:

    I'm going to have to agree here with you Mr. Kaushik. However, it seems popular for top-level executives to want a year in review of the previous year, and of course January is when they want to do it. What happens is this is how they determine if their marketing departments continue to get funding for internet marketing or not (I know, you wouldn't think that in 2008, that internet marketing is still something that's in question by people, but it is very much so unfortunately).

    So, for this one very big-name client, I had to bring out some historical data, and show that over the course of the year, their marketing efforts have justified them signing off on another year of online advertising. Another client's CEO wants to see the last two years (2006 + 2007), and wants to basically know "was it worth it", in dollar$.

    I don't mean to get off-track on the subject, but I guess that in the offline business world, this is how CEO's and top-level executives measure success and ultimately do business, by comparing large date-ranges to a previously large date-range, and determining whether or not to make an additional investment for the entire following year. And, I guess they've brought this mindset over to the online world, even though they are applying offline measurement tactics, like, looking at really old historical data.

    I'm curious to know if you (or anyone) experiences the same type of thing with your clients or bosses.

    Thank you Avinash!

  3. 3
    Tim Wilson says:

    Avinash, the response to this post will give you a strong sense for the audience of your blog! I suspect you will get a resounding "Bravo!" from the web analyst types (see Mark and Joe above). But, as Joe points out, the pressure to keep historical data tends to come more from the casual users of the data. These are the same people who get their knickers knotted when you change from one tool to another. And, they're the ones who are horrified to discover that, after the site has gone through a major redesign that has dramatically improved the site's usability…the data looks a little screwy when trended!

    Joe really nailed it in his comment when he wrote that the pressure is coming from people (I'd go beyond simply executives — I've seen it with managers, product managers, and marcoms) who are either used to using measurement against more stable (definition-wise) success metrics (revenue, net income, etc.), or people who have been picking up on all the hype of the last five years about bringing data-driven decision making to Marketing.

    In the case of the former, it's a matter of education. Go through your own web site and assess all the things that have changed in the past 18 months that make that data largely irrelevant (while keeping, as you noted, the handful of aggregated KPIs that are most likely to remain relevant). Point those out. Tell a story. It's worth it to go through the effort of that education process!

    In the case of the "but I want to use data, and I need 3 years of data to trend it…" it's a different form of education. In my experience, this comes from people who don't really know *how* to use the data. They assume that being data-driven is 95% about capturing the data and only 5% about interpreting it. In reality, being data driven is more like 50% about being clear as to your objectives, 20% about capturing the data, and 30% about analyzing the data against those clear objectives. "I need a long trend to know how I'm doing," is, all too often, a cop out.

    You've covered both of these issues on this blog before, so anyone who is looking to dive deeper, should just poke around here! On the latter point, I posted a while back on the myth of "More Data Is Better" which is on the same underlying issue: http://www.gilliganondata.com/index.php/2007/10/02/more-data-is-better/

    Great stuff, Avinash!

  4. 4
    Jen says:

    I agree too (where are the disagree-ers?!). My favorite "historical" view is an annual shape graph. I have one that goes back 8 years(!) through changes in implementation and everything. The shape is important to let me detect an anomaly. If I plunk this year's shape on top of my annual shape graph I can tell people "this is normal for this time of year" or "we better investigate this". (This is particularly important for non-commerce sites whose shapes are not necessarily dependent on marketing push/shopping seasons.)

  5. 5

    "Forgetting" is a feature of human brain, not a bug.

    It's important for us to forget useless information, so we would deal only with important information.

    You are right, old detailed [web analytics] data is useless.

    You are saying "History is important in other context". I don't agree: all detailed history data decays, whether it's web analytics or not.

    We want to remember from the past only the most important pieces, usually these pieces are totals/summaries. And you already covered this: "Keep some history around. Aggregated data. Historical markers."

    Bottom line: History data decays everywhere, not only in web analytics.

  6. 6

    Avinash, your posts are quite informative and I always look forward to them. I'm a new Web Analyst in Oregon that is just soaking all of this stuff up. The concept of data decay did not occur to me until now – obviously logical, but nonetheless still a blind spot. Anyway, thanks for your posts!

  7. 7

    I find your arguments convincing, and yet I also find myself wondering how much of this mindset is due to vendors making analysis of historical data hard. You can't get data from before you started using that solution; in some cases you can't get different types of data without changing your tags; reprocessing the data may be hard or impossible.

    Let me put it this way. If we were all using logfiles and reprocessing data were easy and cheap, would your answer still be the same? Are we like the fox and the grapes, or would you still process no more historical data even if you could do so effortlessly?

  8. 8
    Steve says:

    I'll strike a humble (feeble?) blow for the nay-sayers. ;-)

    * Audit/Archives ACT(s). We have no choice BUT to keep the data around for at least 7 years, and it's entirely possible under the Archive ACT(s), forever. That's the law. Obviously YMMV. :-)

    * Computations for us have changed, sure, but not as much – for us – as you intimate. So I quite simply go back to year dot, recalc and expose new insights – which I do drag into my current analyses. Also, I find something NOW – was this THEN as well? – New behaviour vs simply unnoticed. This ties into Stephen Turner's comment. I am using logs, and I can easily do this.

    * Website changes? Nil event for us. Yes there are changes, but the individual clickstream stuff has never been important, rather it's the summary styles that matter. Grouping individual pages into the categories – we have massive amounts of metadata/thesaurus-trees and the like, so this is a doddle for us.

    * Seasonal Variations. This has been pure gold for us. To be able to see the impact over the longer timeframes of having our budget cut to our primary marketing methods for the site.
    We would never be able to even prove a case with even a years worth of data.
    This falls into your one tab rule Avinash, but to be able to regenerate this with updated filters (apples with apples), I still need the original logs.

    * Server Performance Forward planning; Disaster Recovery planning and all the techo side. I can, have and often do have to go back to the logs around major… events and extract server "hammered" type numbers. X connections per second. HTTP 1.1 vs 1.0? KeepAlive Enabled? Bandwidth? One takes these numbers from THEN and extrapolates to NOW and in the FUTURE. To compare usefully, we also need the same data from NOT around major events.
    As we approach new issues with newer systems, questions that were irrelevant THEN are mandatory NOW.

    But I think the key nay for us, and possibly others. This whole hyper-speed "Internet Time" mindset is … (deleting several phrase/word choices… ;-) ) irrelevant. For us. We are emphatically not the sort of site that does or even needs to change like that. I would even go so far as to strenuously argue that it would be lethal for the site as a going concern and ourselves to try and change that quickly.

    I don't disagree, fundamentally, with your core premise here Avinash – more the degree of. But do be careful not to cut your nose off to spite your face eh! ;-)
    You said it yourself that finding stuff now is hard. So why make it harder by removing the ability to go back and see if the same hypothesis held true back then? I'm not implying a perfect alignment, but the broader brush can expose confirmation. :-D

    Cheers!
    - Steve

  9. 9

    Hmm, I got to feel a little uncomfortable here.

    You're right to say that things can change a lot. However, I have performed numerous longitudinal analyses that went back 24 – 36 months of web sites that could certainly be analyzed over such period of time. And the learning is always great. Add to this that, without some historical data, it is not possible to do any solid regression work. At some point in the not so distant future, we will need to get into the predictive bandwagon.

  10. 10

    Mark : I love the "Linus security blanket" metaphor, it describes around 98% of the grand projects and investments in conquering "history".

    This is certainly a deeply prevalent sense of "if I have it then I am informed and intelligent".

    I'll admit to having that mindset myself – after a while reality and having gotten little business insights or ROI taught me a tough lesson.

    Joe : A large part of the current belief in magical powers of historical web analytics data comes directly from traditional BI and DW and Finance worlds. It is entrenched in the current generation of leaders, most of whom have moved from there to the web.

    Part of the reason for this post is to provide them with a thoughtful and compelling alternative point of view. But I don't expect change overnight or with most people – it will take a generation.

    Tim : I absolutely loved your 50-20-30 work split! Brilliantly put. I wish I had thought of it! :)

    Dennis : [Sorry everyone this is becoming a "lame" comment stream. . . ] That is a genius quote. I have to figure out how to work that into all my future presentations.

    Feature indeed!! :)

    Dr. Turner : Excellent point.

    That would not change my mind. Having had access to some pretty heavy iron and even prettier software.

    For example in the context of say VisualSciences (Discover OnPremise now?) were if money is no barrier than the amount of history (/data) is irrelevant. Any answer you want can be processed really fast (quick answer from sampled data in seconds and "accurate" answer when the query is done).

    Or with Neteeza – throw in gobs of "dirty" (imperfectly organized) data into that box (again expensive but) and the answer will come back fast.

    My hypothesis is that the change in business execution and priorities, website architecture and content, data collection and processing, pace of web evolution are the root cause that make historical data of little value.

    Especially granular "click level" data (all pages and url stems and every click for ever session and . . . . ).

    "Session level" and higher data is if some value, but that does not require massive data stores or hiring of three full time staff expensive analysis solutions. Extract high level trends for key session level metrics into a spreadsheet [or ok into a Access (!!) database] and be done with it.

    Most analysts underestimate how fast things are changing around us, but few business people underestimate it – because we the Marketers are living in the middle of it all trying to acquire visitors and convert them into satisfied customers.

    At some point things will settle down. Business on the web will be routine. Website experiences will be stable (think html, dhtml, php, dynamic, flash, ajax, whatever). We will all collect close to exactly the same amount and type of data. Then perhaps we can store more history and it might be of more value – because things will be routine.

    We will get to a point of stability and consistency – where other decision support systems are. But it will take a while. And that is partly what makes web analytics exciting and fun!!

    Steve : I am sadly not a expert on Australian, or other governmental, requirements and do hereby request all readers to please follow the law.

    But I sincerely hope that no one is certifying their Web Analytics numbers to the SEC, we can't quite count on them, numbers, to be that countable (as the SEC expects of all numbers!!!).

    On your other point you are right, if your website never changes and your business never changes and other elements of your online presence and measurement don't change then you can find value in history.

    I doubt that that is most people. In fact I doubt that any website, public or private, that is not evolving to visitor preferences and making use of latest technology to make lives of the teeming masses easier is actually delivering value to the extent it should.

    If you listen and prepare for each generation of web users and leverage technology to deliver to their expectations then you will change, all of the above applies, and data be dammed you'll still win! :)

    Jacques : You are a expert in integrations and I will seek you to educate myself on your experiences – perhaps you'll be so kind as to share your experience.

    My humble experience shows that in this context, historical data and its value, it is consistently a case of over promise and under deliver.

    And I stress it is less our ability to throw hardware and software, it is a result of the business dynamic and changes outlined above. For now.

    With regards to the predictive bandwagon, I am afraid the promise land faces tough challenges (Type of data, Number of variables, Multiple primary purposes, Multiple visit behavior, Missing primary keys & data silos & lack of holistic datasets, Pace of change).

    I outline them here in detail:

            Data Mining And Predictive Analytics On Web Data Works? Nyet!

    Thanks everyone,

    Avinash.

  11. 11

    Thank you for your thoughtful reply, Avinash. I was thinking of occasions like when you devised a new — maybe highly seasonal — KPI for your business and wanted to see how it had changed over previous years.

    Actually I suspected it wouldn't change your mind, and I think I agree with you in most situations. But I still thought it was worth considering whether the historical data is really valueless or just too expensive to extract.

  12. 12

    Dr. Turner : You are right, for key metrics on your website you would want to keep them, especially in a aggregated format, so that you can trend those over time. Visits, Visitors, Bounce Rate for your site, Share of Search etc would be a few examples that come to mind. For any business there won't be many but enough for you to put some thought and store them to get context.

    Thanks so much for the comments, I consider it a privilege for myself and our readers to get lessons from a thought leader such as yourself.

    -Avinash.

  13. 13
    Greg Moore says:

    This jumped out at me:

    "…almost all of us collect anonymous non-personal data from our Visitors."

    My wish is that you would write something about the other side of the coin, about the benefits, potential problems and trends involved in collecting personal data from visitors, such as email addresses — how web analytics changes when we have data that personally identifies our visitors.

    Thanks for turning out so many great articles. Your efforts are really appreciated.

  14. 14
    Ned says:

    Great timing Avinash — especially for those customers thinking about a switch from "Visual Sciences" to "Omniture" :-).

    I too have to agree that history is often given *misguided* more weight than is necessary. I think some of it has to do with the 'Nature vs Nurture' aspect of the the HIPPOs and the webanalyst. As you touch on in your post, in traditional analytics, historical data is given a premium – for analysis, forecasting, customer modeling, segmentation, discriminant analysis etc… And when folks who has been "nurtured" on this mindset crosses the divide over to the Web, there is a natural tendency to bank more on historical data.

    The difficulty I have sometimes is convincing folks on the 'morph-rate' of the webdata. In the traditional world, data (content,collection, and computation process) is static (or infrequent) for all practical purposes. So historical analysis makes sense as we can 'stitch together the conclusions' and project it into the present and future. However, (as you mention)on the web the rate and depth of change is of a different magnitude [not to mention the increase in the number of dimensions/metrics]. Everything from your content, #pages, and offerings can acutely change in a short time-period based on customer experience analysis & review, and so whatever "learnings" you are able to decipher from the past — well, you might just have to just sleep on it as it might not be applicable to the present scenario :-).

    Having said that, I agree that there are moderating factors like industry where history might make more sense in one industry and be practically useless in another. Again, thanks for a great post.

    "To know the truth of history is to realize its ultimate myth and its inevitable ambiguity" –
    — R.Basler

  15. 15
    Rahul Deshmukh says:

    Great post Avinash! Just to add to the agony, marketers try to compare pathing analysis for data that is 2 years stale to make decisions for their new design. Saving aggregate clickstream data including some campaign information can be valuable for trending purpose, but other than that looking at history is not worth it.

  16. 16
    Dave Rogers says:

    Hi Avinash,

    Thanks for the always great posts. I have 1 seemingly small user-friendly recommendation: could you move the 'print this page' link closer to the top of the page so that I can easily open and just print, then read later while getting home? It would save me time and I would be more likely to do so, then to scroll down and look for it each post. (I wonder if others feel the same…?)

    Thanks.

  17. 17
    michael says:

    i completely agree that history is overrated. no company i am aware of has made a decision based on data more than a year old. it just takes up space.

  18. 18
    Kristen Nomura says:

    There's a little trick you learn about cleaning out closets if you've ever watched any of the shows on TLC, and that is "if you haven't worn it in a year, then get rid of it."

    Seems like the same approach could be taken to web analytics data. There may be some items from last year you want to keep, and even some to keep for many years, but a lot of it could easily be let go.

    Yes, there's always the risk that you'll finally have another opportunity to wear that purple, sequined prom dress and wish you'd kept it. But not many :)

  19. 19

    Hi Avinash,

    As always, great post which makes me think!
    Well, as I can relate to most of the statements (and the comments too- especially Kristen Nomura), I think that the debate is about numbers in general rather than just the historical aspect.

    John Marshall tought me how to differentiate between statistics and analytics- -I think this is mostly the point here.

    If we simply look at the numbers over the years, we are looking at the statistics, we can find trends – how we grown? have we improved vs. last quarter? (quite similar to financial and SEC reports as stated above) – well these are the flat statistics that our end user usually requests.

    I think our job, as web analysts and leaders of this field (well, at least striving to be leaders :-) ) is to teach the user the right questions to ask.

    I too meet managers of all levels asking for various numbers and figuers. I think our job is to show them the numbers which really matter. To show them before and after numbers for important events, to show what we CAN do with analytics data.

    With regards to changes of tags, systems etc. – well the main problem when we see a change is to know WHY it happened. With so many changes around – design, technology, season, day part, price, customer preferences – it's sometimes hard to tell.

    But at this point I'd like to offer a "word" which always helps me to find the good juicy stuff. The word is: "SIMPLIFY".

    When we simplify the way we think, when we simplify the numbers and KPI's we will usually get insights. Yes, sometimes we wil not be right. Yes, most of the time we will "miss" very "important" details on the way – but when we simplify we CAN get insights.

    So, while I can accept the somewhat frustrating feeling of "too much information" and "too many changes to understand what happened" – But I too think that in our new cyber world one very important rule is to follow what's working in the "oldfashion" offline world – look back, compare – deduct…

    (Sorry for this long comment:-) )

  20. 20
    Alex Gardner says:

    It might be dangerous to go against the crowd here, but I have to wonder if web analytics is so different from everything else that the following quote no longer applies:

    "Those who ignore history are doomed to repeat it."

    By throwing away historical data, I wonder if it increases the risk that people will continue to make the same mistakes again and again on their sites. Quite often there are important lessons that have been learned from this data and while they may lose some relevance, surely they do not become completely irrelevant?

    Decay is a fact, but aren't the lessons that are learned from historical data still potentially important to help shape decisions now?

  21. 21

    I have your book and it is wonderful. I have a quick question. Is there an Site Abandonment Rate for a Search Engine and not a site with a shopping cart (Content Drive Site)? And if so, how would I get it?

    kind regards,

    Brian Tucker, MBA
    Competitive Intelligence Analyst

  22. 22

    Hello Avinash,

    I had the pleasure of visiting Google last night presented in Chicago by Kristen Rahn Nomura and her co-part [cannot recall name] on Google Analytics She offered a brief but insightful detail to the way the program functions. As the industry continues to evolve the question that has risen is the ability to integrate additional web data-reporting capabilities to ensure [in a nut shell] your site is working properly and you're generating revenue. Often data presented by web analytics lacks Voc [Voice of the Customer] such as Opinion Lab, which complements not only G.A. but also many other Google programs such as Dynamic Comment Cards on Google Ads, etc. If Web Analytics answer the questions Where? and Who? Opinion Lab answers the question: Why?

    What are your thoughts?

    P.S. I had ordered your book a couple of days ago, and I am very intrigued to learn more.

    P.S.S. Your Chicago Google location is awesome. From look and feel, to the people our team acquainted with, it was a real pleasure.

    Respectfully,
    Mario Bilotas, esm

  23. 23

    Mario : You are right!

    I am a huge proponent of understand the Why (you'll notice that in the book as well). Here are some of the posts where I get on my soapbox…

    I could keep going on, but I don't want to create a irritating list of links! : )

    I am glad you enjoyed your visit to the Chicago Office, I can't think of anyone better to meet there than the absolutely wonderful Ms. Rahn Nomura.

    [The other person I think you met must have been the charming Elliott Rader.]

    -Avinash.

Trackbacks

  1. [...]
    Nuestro público cambia contínuamente, aunque tengamos los mismos lectores, todos cambiamos contínuamente, y por lo tanto es necesario ir ajustando el aspecto formal de nuestra web de manera acorde.

    Os recomiendo el siguiente artículo de Avinash Kaushik sobre la relativa importancia de los datos históricos de clickstream.

    2) Factores contextuales

    Por otro lado, están los factores contextuales. Cuando realicé mi Master en Sistemas de Información en la London School of Economics, una de los mantras que más me repitieron fue “lo importante es situar el sistema de información en su contexto: nada es absoluto, todo es contextual”.
    [...]

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