July 2007

31 Jul 2007 12:53 am

crown I am a NPR junkie (and support my local station KALW). Driving to the airport last Monday I was surprised to hear a segment about "behavior marketing". NPR covers everything of course but for some reason it was surprising to hear of the current technology cool thing on NPR! Now that NPR has covered this I feel I am late to the party!!

Behavior targeting has been the news a lot recently. The old story for BT was about targeting ads, though that does keep getting better (unwarranted plug: get adblock!). The new story, and news, is all about using different methodologies to target the right content, promotion, message etc to you on the site you are visiting.

Omniture's purchase of TouchClarity has brought automated targeting to our side of the pond. Kefta does some very interesting things in terms of your ability to input business rules to drive targeting, they were just acquired by Acxiom.

On the multivariate testing side, Offermatica has announced "affinity targeting" (though I have to ask Matt for a non-marketing blurb of what it actually does, meanwhile here is our friend Jonathan's excellent write up).

In a world that is as "action challenged" as our world of web analytics, we should welcome behavior targeting with open arms .

Not the "lets figure out how to serve you the best ads from your browsing habits" but rather "we have all this data about you in our web analytics tools, why don't we show you relevant content regardless of if you are there to buy or get tech support".


The challenge with web analytics always has been: scale, data and diversity. They all plot actively against our ability to take fast action.

Here is how they do it….

~ Scale: There are thousands of people ("visitors") on our websites at all times. And thousands more are coming. That creates a unique problem of huge numbers that impacts analysis (not reporting, analysis).

~ Data: These visitors and pages going back and forth generate tremendous amounts of data, at a level where it is really hard for a human, or even our tools, to parse it all fast enough for us to take action quickly (see my point of view on real time data needs ). Or even take action every few days.

~ Diversity: Most website owners are pretty bad at understanding all the reasons why people come to their websites (see my recommendation on how to measure primary purpose ). Trust me people are using sites for purposes that you did not intend, or created your site for and that complicates data analysis and action.

target Behavior targeting done with the right tool for you means that you can overcome the Scale, Data and Diversity problem by, simple put, automatically understanding your visitors as they interact with your web presence and showing them the most relevant content.

Sometimes the content targeting is based on business rules you input into the "automated system" (like say in Kefta). At other times the content targeting is 100% automated, based on complex "machine language algorithms" (as in the case of Touch Clarity).

It is the ultimate holy objective: Right thing to the Right person at the Right time.

BT has been around for a while. What has changed is how much more accessible these systems are, and in the grand scheme they are getting cheaper (yes "cheaper" is relative to your own context!). :) The promise is tempting and you can do a lot if you do it right.


While the promise is tempting, and results often very positive for clients, I find that people seem to treat BT as a panacea. God's answer to all problems you could have on your websites.

It is important to remember that the principle of Garbage In, Garbage Out applies here, more than it does any other situation.

For one simple reason: At the end of the day what makes a behavior targeting platform tick, and produce results, is not its own native intelligence and smarts even though that is critical. It is your ability to actually feed it the right content which it can then target.

garbage in garbage out

You feed your BT system crap and it will quickly and efficiently target crap to your customers. Faster then you could ever have yourself.

So you should absolutely positively investigate behavior targeting platforms and how you can use 'em. In fact if you are a Fortune 1,000 company it might be a crime against your customers for you not to look into BT.

But before you implement make sure that you cover these two pre requisites (no matter which vendor you want to go with):

#1 Invest in rock solid customer "listening posts":

    Think about it for a second. What are you actually trying to do with BT? Right content to the right person at the right time. Holy grail.

    Now take a step back and answer this question: Beyond having a web analytics tool and using it do you actually have ways to hear from your customers and understand who they are, what they need and what kinds of problems they have (with your website, with your products and services, with working / dealing with you)?


    I suspect that most of us don't have active listening posts. Surveys, follow me homes / site visits, usability studies, remote testing, market research or phone surveys or listening to call center conversations. Qualitative data [for more details read chapter three in the book ].

    All of the above help you get a solid picture of who your customers. Understanding them means that you can actually come up with all the content that your BT platform needs to intelligently target to the right person at the right time.

    No amount of technological coolness from your BT platform can make up for your lack of good content. In the end end you still have to get off your quantitative high horse and get out and talk to customers.

    Do this for atleast three months before you implement your BT platform.

#2 Do A/B or MultiVariate testing first:

Readers of this blog are aware of my love for testing (of any type). Shifting the power away from you or your HiPPO's deciding the website experience to your customers dictating it.

Before you jump into BT it is a great idea to have implemented and done multivariate testing for a while. With multivariate testing you are not actually doing BT but you are trying to come up with various bits or pieces, or, I'll betray my loyalty, "mboxes" of content and throw them on a page to see what works. That process is very powerful in what it can teach you.

strands of dna 1 You will learn how unbelievably hard it is to come up with good content that will improve your customer experience (or conversion rate for you).

You will learn how painful it is for your organization to actually come up with creative and content (the marketing run around, the displeasure from sales in actually approving promotions, the road blocks from legal about what you can say and put out there, the displeasure from your "cool" creative designers to anything simple, the run around from your IT teams, and so on and so forth).

Then there is the learning around process.

Testing of any sort is not ad-hoc. For it to work in a systematic way you will have to create a structure and repeatable process with defined steps and roles and responsibilities and organizational clarity. As you struggle and fail and succeed with MVT you will figure all this out and if you are committed you will come out stronger at the other end.

This will be a priceless experience. You are now ready to move from you creating a few relevant customer experiences to automated system doing it for you at massive scale.

Do this for atleast three to six months before you implement your BT platform (and the initial three months can be in parallel to doing #1 above, establishing solid customer listening posts).

In summary:

You can't expect to get massive results from your Behavior Targeting efforts by simply moving from having a web analytics tool to implementing a complex targeting solution. You need to evolve first and ensure you actually have a process for understanding your customer needs / wants and ensure that your organization has the process of feeding good stuff into your targeting platform nailed down.

Ok I have had my humble (or not so humble) say, its your turn now. Do you agree? Is any of the above making any sense? What am I missing? If you have successfully done behavior targeting did you have the above two prerequisites? What else has worked for you?

Please share your feedback and critique 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.]

27 Jul 2007 12:07 am

sproutTime to celebrate the one year anniversary of the top ranked web analytics blog list, and do so in high fashion with a new and improved ranking computation!!

Doing this list has been a lot of fun, mostly because I am always trying to think of new and interesting ways in which it can be made better. This one is perhaps the most lovely update thanks to my peer blogger Kevin Hillstorm.

Kevin writes a wonderful blog, MineThatData, and during one of my recent visits I noticed that he had updated his "Friends Of MineThatData" list with a "Kevin's Proprietary Ranking System". You can imagine that would pique one's curiosity. :)

Kevin's system is a non-linear regression model where the two independent variables are alexa and technorati ranks and the dependent variable was 98, 94, 90 etc, values he assigned to the highest, the next highest and the next highest blogs in his list when ranked by the average of alexa and technorati ranks.

I particularly liked this part:

It is designed to give a value no greater than 100, no less than zero. It is designed to rapidly move somebody up from 0 to 50 as a blogger builds and audience. In your case, you could double your audience, and your ranking won't move much.

I like it because it encourages young bloggers to move up the rank very quickly, even with small numbers. At the same time it gets harder and harder for those on the top to truly move their score (so no short term tricks will work!).

Kevin has generously shared his proprietary ranking system with me and I have adapted it a little bit.

For my best web analytics blogs ranking I am using Technorati and Feed Subscribers. I have come to appreciate the number of feed subscribers as the best success metric for a blog (see: Tips For Measuring Success Of Your Blog).

I emailed the 35 web analytics blogs I track for my rankings and they all kindly sent me their feedburner subscriber numbers. Thanks folks (!!).

Only one blogger declined to share his feed subscribers, the list is poorer for that.

Without further ado here is the list of top ranked blogs on web analytics:

Rank July '07 Rank July '06 Top Web Analytics Blogs Score
1 3 Occam's Razor
by Avinash Kaushik
2 New Google Analytics Blog
by Jeff Gills
3 1 Web Metrics Guru
by Marshall Sponder
4 New Web Analytics World
by Manoj Jasra
5 New WebAnalytics.be Blog
by Aurélie Pols
6 New Analytics Talk
by Justin Cutroni
7 4 Unofficial Google Analytics Blog
by Shawn Purtell
8 New Lies, Damned Lies…
by Ian Thomas
9 5 Increasing Your Website's Conversion Rate
by Robbin Steif
10 New The Commerce360 Blog
by Craig Danuloff

If you want to play along at home here is the formula:

top ranking blogs formula

Where E4 are your feedburner subscribers and F4 is your technorati ranking. You can also try this excel spreadsheet and play along: blog_rank_calculation.xls.

My thoughts / observations :

  • Success can no longer be achieved by simply getting a high technorati ranking (for example by just getting links from other blogs). You need to also write content that will compel visitors to be converted into readers by signing up for your RSS feed.
  • The highest technorati rank on the list is 2,128 and the lowest is 72,921.
  • The highest feed subscriber number is 3,954 and the lowest 328. In both cases a huge difference!
  • There are atleast three blogs on the list above that would not have been on it under the old purely technorati driven system!
  • The system is also does not "punish" someone simply because of technorati "issues" (case in point would be Robbin whose technorati ranking dropped simply because of a blog move, but her loyal feed readers ensured that she is still on the list).
  • Just for fun I compared the rankings to the one's last year, notice how many new blogs are now on the top ten!! The web analytics blogosphere is richer from participation.

    If you have a unique voice it is never too late to express it, you'll find a audience quickly.

My Personal Best Blogs Rankings :

With each top blog listing I also present my own personal ranking of the best blogs in the last few months (using the criteria that they “Eat like a bird, and poop like an elephant�).

Here are my personal favorites…..

# 1 Coremark Analytics – Wendi Malley
(Wendi is a math goddess. She shares practical applications of mathematics and statistics that you can actually use, and in many cases will blow your mind. I get smarter every time I read a new post from her. Thanks Wendi.)

# 2 Web Analytics Demystified – Judah Phillips
(Judah provides a unique and intelligent perspective on all things web analytics, especially technical. He has a quirky sense of humor to boot! Thanks Judah.)

# 3 Visual Revenue – Dennis Mortensen
(No one has made this list twice, except Dennis! For being a COO he is frighteningly good at both the big picture of web analytics and the deep and dirty details. Thanks Dennis.)

Please sign up for RSS feeds for the above three blogs and link to them from your websites / blogs so that we can get them on the top ten list above!! :)

Once again I want to thank Kevin for all his generous help and ideas.

Ok now it's your turn…….

What do you all think of the updated scoring mechanism? Better? Worse? Would you suggest a different metric? Is there a blog on web analytics that you love that is not on the above list? Please share your feedback via comments.

[Like this post? For more posts like this please click here.]

PS: If you want to a copy of the list here is a handy list:

Overall Top Ranked:

# 1: Occam's Razor by Avinash Kaushik
# 2: Google Analytics Blog by Jeff Gills
# 3: Web Metrics Guru by Marshall Sponder
# 4: Web Analytics World by Manoj Jasra
# 5: WebAnalytics.be Blog by Aurélie Pols
# 6: Analytics Talk by Justin Cutroni
# 7: Unofficial Google Analytics Blog by Shawn Purtell
# 8: Lies, Damned Lies… by Ian Thomas
# 9: Increasing Your Website's Conversion Rate by Robbin Steif
# 10: The Commerce360 Blog by Craig Danuloff

My Personal Recommendations:

# 1: Coremark Analytics by Wendi Malley
# 2: Web Analytics Demystified by Judah Phillips
# 3: Visual Revenue by Dennis Mortensen

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