July 2008

24 Jul 2008 12:27 am

off center 1 If you are using a modern web analytics tool (tag based or log based) it is quite likely that it is using cookies for tracking purposes.

In my conversations it is embarrassingly common to find a lot of FUD and confusion and lack of understanding (or appreciation of!) cookies and the role that they play in any analytics done on the web.

Hence my attempt at this simple easy to understand primer. If you are an Analyst or a Marketer or a Website Owner or a Website User it is critical that you read this short blog post – your data will make so much more sense after are done.

Why are cookies important?

Cookies, usually anonymously, allow the website owner to measure the number of Visits and the Unique Visitors to the website and hence understand the Customer's website experience and segment visitors that are New to the site from those that are Returning.

That's it.

No more and no less.

Lots of other tracking is possible without the use of cookies, they are not the be all and end all of visitor behavior tracking. Wipe that sweat off your forehead. Go get a cold glass of water to drink.

Let's attack the rest of this complex issue in a few bite sized understandable chunks.

Transient vs. Persistent.

There are two types of cookies that the web analytics software will set when you visit a website. They are commonly called "Transient" and "Persistent" cookies. Some folks refer to them as "session" and "user" cookies respectively.

jam cookiesThe job of the transient cookies is to help "sessionize" your experience on a website. Put simply, you are going to make a series of clicks and leave. That's a session. The transient cookie helps group those clicks efficiently.

The transient cookie is "set" when you visit the site, it disappears when you leave.

The persistent cookie is set the first time when you visit the website, and it will remain there for the duration that the website determines. For example, Analytics cookies are typically 18 months but many other tools will use anything from 18 months to 18 years. Persistent cookies are there to help identify a unique browser to your website, in as much they are the closest thing to tracking a "person" / "unique visitor".

The persistent cookie is on your browser until you either delete it, reinstall your browser or do other such things.

It is important to note that almost always persistent cookies don't contain any PII - Personally Identifiable Information - data. They just have a random string of numbers or alphabets that only the company who set the cookie can read. For example here is a cookie that Webtrends.com just set on my browser as I visited www.webtrends.com: C8ctADY1LjU3LjI0NS4xMS00MTU3MTQwMTc2LjI5OTQ0NzE5AAAAAAAAAAACAAAAo
I should see if I can mess with them by changing that cookie to COREMETRICSWILLALWAYSBEWATCHINGYOURSITE4LOVE+OMNITUREISGREATAND

First Party vs. Third Party.

A "third party" cookie is set by, well, a third party when someone visits your site. So if www.omniture.com is using WebTrends as the web analytics tool of choice then when I visit omniture.com a cookie will be set on my machine under the www.Webtrends.com domain. On omniture.com a Webtrends.com cookie is considered a third party cookie.

[Omniture.com is actually setting cookies using .2o7.net which would make them third party cookies on that domain.]

In the good old days it was easier for the web analytics vendors to use third party cookies and they were rampant. But it was discovered that there were other players using these cookies in sub optimal ways. This lead to default internet browser settings that would reject third party cookies, and many other anti spyware and malware programs auto deleting them etc etc. Suffice it to say they have fallen out of favor, and are considered quite sub optimal for tracking "unique visitors".

first party or third party

A "first party" cookie, hence, is set by the web analytics tool using the domain of the website itself. As an example when you visit www.coremetrics.com you'll notice (if you have WASP!) that they are setting cookies using the domain data.coremetrics.com – which makes those cookies first party.

First party cookies are the preferred tool of choice for tracking "unique visitors" because they are deleted / rejected a lot less by any objective measure. This means, for example, they are a far superior at tracking repeat visits or new and returning visitor segments etc.

Another reason first party cookies are rejected a lot less is that much of the internet does not work if you don't accept first party cookies. Email providers like hotmail (! :) or gmail.com, ecommerce websites like amazon.com or crutchfield.com, banks, even blogging platforms! They all require you to accept first party cookies.

Almost every single decent web analytics vendor now provides an easy ability for you to use first party cookies. Some like Google Analytics only offer the option of having first party cookies.

If you notice some initial push back from your vendor to use the easier-for-them third party option, do a little push back of your own. Insist on first party. Its good for your health.

Exception for Third Party Cookies.

web analytics ad There are some relevant uses of third party cookies. One of the most common is by ad serving platforms because that is the only way they can track a "unique visitor" across multiple websites. So even if that third party cookie gets blown away and rejected a lot more, they (you) really don't have much of a choice. That's just how the internet protocols work.

Here's a example of how that works.

We saw that omniture.com is using .2o7.net third party cookies. After going to omniture.com I could go to ebay.com and then to nytimes.com. .2o7.net knows that I was at the Omniture site a little while back and then I went to eBay and then NYTimes.

Now as I am reading the latest Maureen Dowd column .2o7.net (if it was a ad serving platform) could serve me a ad for Omniture next to the Maureen Dowd column. Knowing I also went to eBay they could even give me a deal on Omniture in that ad! : )

This is of course just one example to illustrate the use of a third party cookie and why Atlas and DoubleClick and Yahoo and all the others use them (and provide value to their customers).

First party cookies can't be "read" and "carried over" like the above scenario.

Does my choice (1st or 3rd) influence where my data is stored?


The type of web analytics software you use determines that.

If you are using a ASP based solution (say NetInsight or Microsoft AdCenter Analytics or VisiStat) then both your first party or third party cookie data is stored in the data center of your application service provider (vendor).

If you are using a in-house solution (like ClickTracks or Urchin) then your data is stored in your own data center (regardless of what kind of cookie you use).

Cookie Deletion Rates.

It is important to remember cookie rejection is not the same as deletion. With rejection you don't even accept (worsens tracking). With deletion you collect data for the session (visit) but tracking after that visit worsens.

Everyone wants to know cookie deletion rates ("help my web analytics data is crap!"). There is no "global standard". Sadly I have never seen a study that was objective and not pushing the vested interests of the publisher (be it a company or a "analyst").

broken cookies It is also extremely extremely difficult for a "third party" to have the kind of access required to actual data that would help them develop anything close to a objective "standard".

The biggest determining factors are your customers and their browser settings and software on their computer. And that can vary greatly from site to site.

My own personal experience across a number of ecommerce, support, and other corporate sites (excepting extremely "tech heavy audience" sites) has helped me come up with a "benchmark" of cookie deletion rates of 3% to 5% for first party cookies and 20% to 25% for third party cookies. They all tend to fall in that range.


If you want to know what the number is for you, I recommend putting in the sweat, blood and tears to measure it on your actual site. If it is important to you, it is important that you don't just take someone's word for it and proceed to evaluate your own web analytics data and get your own benchmark. I assure you that you are unique.

Do I have to use cookies?

The current generation of web analytics tools all use cookies to perform the core function of "accurately" compute Visits and Unique Visitors.

i have questionsIf you use cookies those numbers will be better (not perfect, see this post: Data Quality Sucks, Let’s Just Get Over It).

You will get a better understanding of metrics like Visits to Purchase or New and Returning Visitors or even Conversion Rates.

But if your company executives or, more likely, website customers have a preference for you not to use cookies then you don't have to.

You won't be able to measure some of the above Key Performance Indicators, but you can still get good value from the cookie-less data that you do collect. Top Visited Pages, Revenue, Referring Websites (URL's), Search Engine Keywords and on and on and on.

Don't let the fact that you don't use cookies get in the way of being able to use the web analytics data in meaningful ways.

The data won't be perfect but then again perfection is greatly overrated! (Chapter 13, Page 341 of my book.)

There are analytics tools that allow you to use alternatives to cookies to compute Visits and Visitors. You can use user_agent_id's, combination of browser_id and operating system etc. See if your Management or Customers are ok with that. If yes, use those. If not, to stress again, the data you collect, anonymously, can still reveal insights of value.

Is privacy important?

I know that sounds like the most obvious question in the world, with the most obvious answer in the world.

Yes. It is.

The primary function of your website is to be responsive to your customers. It is important to have a clear privacy policy, it is important to be transparent about what you are collecting (especially if you are collecting PII – personally identifiable information), and to educate your users.

Here's my humble privacy policy (you'll always find it in the footer).

Be transparent, there are few things more important than the trust of your customers.

Besides as I have stressed several times, even with what data you can collect (say you just have your raw server web logs and nothing else) it is possible to find insights. Nothing's impossible for a Analysis Ninja!

That's it.

You are now a graduate of Cookies 301. May the force be with you!

I would love to hear your feedback on this delightful and often beguiling topic. What do you think of cookies? What has worked for you? What did not? How have you overcome obstacles? Any tips for the rest of us?

I am sure you have stories you can't wait to share. Please do.


Couple other related posts you might find interesting:

16 Jul 2008 01:20 am

complex beautifulWhat would make you cry of happiness in a Web Analytics report?

What would make you cry of happiness in any report / presentation that you got from a analytics practitioner or consultant or your mom?

This post attempts to sort through the good, the bad and the ugly and answer that question (except that Mom bit, that will require therapy!).

It will also help you win contracts, prizes, company bonuses, and generally give you Superwoman (/man) like powers to impress people with your awesomeness in presenting complex insights that simply drive actionability.

Some context first.

grand prizeI had the honor of helping judge the winners for the WAA Championship (and the SEM Scholarship Contest ). That made me think a lot about what makes great analysis.

When looking at so many wonderful entries, how does one decide the winner? Are there specific traits? How do you know who deserves to sit at the kids dinner table and not the adult one?

Thus this post was born. It was my attempt, before I judged the contest, to create a framework that would help me identify real analysis and separate the Squirrels from the Ninjas.

Daniel said it would make a great post, and so this one's for him.

[UPDATE: Thanks to permission from the WAA I was able to add the top four winners to this post. Please see links at the bottom of the post, the contain great learnings.]

My hope is that it will help you identify what makes for magnificent analysis and in your day to day job (as Marketers, Usability Professionals, Consultants, Analysis Ninjas, Reporting Squirrels, …) present your thoughts on a set of data and have the maximum impact in terms of insights and action.

Seven Filters That Help Identify Great Analysis:

After you are done with any analysis, and before you present it to your client / peers, apply these 7 filters to ensure that what you are sending out is real gold. . . . .

1) No data pukes.

A summary of the data from the tool is not enough (no matter how pretty). Period.

Often "analysis" that is submitted is essentially a small table of data, which is essentially a "mini me" of the large table from which it came. This is not analysis, it is just a smaller report.

no data pukes pleaseHere is another thing that people consider analysis: x,xxx visits to the championship page almost a xx% of the x,xxx visits for the period of 06/01 to 06/14.

That's the "table" in English. It has the additional disadvantage of forcing me to do math two or three times and try to even graph it in my head. Too much work for anyone to do from a "analysis".

This might be a bit harsh but as I read any "analysis" here is what's going on in my mind:

A] "What's your point?" Give me value, not data.
B] Based on your point, "what do you want me to do?"
C] If relevant, and usually only if asked, give me the data (and please please please don't make me think or have to compute 19% of 8,296 Visits!).

Remember its the first one that I want the most. A drives action.

Bonus points: If you did a good job with the graph, you should not have to repeat in English underneath the graph what it is showing.

If there are any data rivers in your data, please consider redoing your analysis.

2) Hard tie to business outcomes. Always.

If you have learned anything on this blog then it is probably my insane obsession with Outcomes (see Trinity, Web Analytics 2.0).

At the end of the day every analysis needs to solve for the business outcomes. So you have to have some understanding of the goals going in (this is much harder than you imagine).

Many people just jump into the data, find interesting trends and patterns, convert those into "insights" and off it goes. The problem? You are sending things out you think matter, rather than what the business actually cares about.

business outcomes

Revenue, leads, increased customer satisfaction, brand value, friend invites, loyalty, bounces, website engagement :^), job applications, ads clicked, task completion rate and …. and … and .. You get the idea.

Awesome analyses always have hard tie to outcomes.

It was interesting that for the WAA championship there were no real business outcomes provided to the Analysts. Just loose guidance: give us stuff. :)

Yet that did not stop the superstars of web analytics. They simply assumed what the site's desired business outcomes were.

Many of them opened with their interpretation of the three goals of the website (or the bold ones even said something like: "you should actually be driving xxx outcomes but you the WAA are focused on silly things" – now that is chutzpah I can admire!).

Is your analysis focused on clearly established business outcomes? If not by your boss / client, then by you?

3) Usage of other tools (True Analytics 2.0).

Another little thing I obsess about, trying to always advocate the use of more than one source of data to ensure people understand more than just the "what". . . .

web analytics 20 demystified1

I think it was the fifth analysis that I saw that used something other that he web analytics tool the Analysts were provided (Google Analytics).

Such a shame.

Google Analytics (or for that matter Omniture or WebTrends etc) are great tools. You need the What. But it is so limiting. You need the Why and the What Else and more. True Web Analytics 2.0 to get robust answers that have deeper customer and competitive insights.

Sure the WAA does not do surveys to understand how to serve their customers better, and does not have "direct" competitors and WA 2.0 is hard work.

But the enterprising Analysts (and the winning team) went out of the box. They multiple hatsused the AdWords Keyword Tool, they did searches on other search engines, investigated Web Analyst's search behavior on Google Trends, checked how many corporate WAA members members link to the WAA site (a measly one!), one of them did their own survey directly to members (!!), checked the DMOZ, compared the site to IAB etc.

Now that is almost orgasmic. They did not take the lame excuse that the client did not give them data sources. They used all the tools at their disposal and executed a 2.0 analysis.

Do you?

Bonus: Remember you don't have despair about what the client as. Use free services like Compete and mine Google for press releases by various organizations (like shop.org) that contain relevant info and more.

4) Not boring. Please.

Ok this blog is a exception (!), but let's admit it: Web analytics is boring.

Analytics of all sorts is boring. To lots of people (not you and I of course!).

Most web analyst report outs, glossing consultant analysis, make you want dramatically hurt yourself, rather than read them. They are all the same, data pukes, pretty graphs that tell you nothing, no tie to outcomes and descriptions and summaries that would make the IRS proud.

Let that not be you.

Look that these nice folks, they read all the analysis (check out the thick stack!), and just look at how excited they are!

excited old couple

I have to read lots of reports and summaries and briefs. I am always looking for people who made it interesting to read their submissions. Do they have a interesting way of framing the analysis?

In the real world this quality stands over all other "consultant" / "analyst" reports.

The analysis of the winner of the WAA Championship was essentially a series of email exchanges between people, with each email they revealed their ideas, insights and methodologies. No graphs. No tables. Just a quirky sense of humor (and deeply delightful analysis).

They stood out from the polished nicely templated graphics rich submissions of everyone else.

They made web analytics unboring. They made it fun.

Sexy wins. :)

5) Connect insights with actual data.

This might sound absolutely surprising but in many of the analysis it is really hard to see what the connection is between data and the insights derived from that data.

It seems along the way we have all developed "best practices" and preferences and "what works" and what does not and so on and so forth. Hence as we look at websites and data we sometimes simply jump to making recommendations based on what we know and think and feel rather than staying grounded in data.

connectOften I read something like: "Redesign the navigation", and my first thought is why? based on what?. Or "Internal search should be every where" – why? surely a best practice, but why for this site?

Lots and lots of people did this in the WAA Championship, especially those that were from decent sized agencies or consulting companies. They have the curse of knowing lots.

Me? I always put that secondary. My recommendation: Tie your recommendations to the data on hand. Include your feelings in a appendix, but in the main body, tie to data.

6) Meet the "expectations of scale".

This is perhaps a personal bias (especially in competitions). I am not going to, sorry, have the same set of expectations from Michelle Chin as I do from Jaume Clotet as I do from Zaaz.

tall and shortEach of those comes with massively different set of experiences and resources. The bigger you are the more I expect (and please remember not more data pukes, more analysis!).

More in terms of insights, more in terms of rigor, more in terms of everything.

If you are "big" or you have written a book (!!) then you are playing the game at a different level when it comes to expectations. Michelle has to be just so good to beat the bejesus out of you (and I know Michelle, she can!).

Look at your size. Do your analysis reflect the depth that your size should? It better.

7) Have something unique. Enough said.

Remember that if you are going for a RFP or a contract that 99% of what you will have access will be the same, 70% of the analysis that you will end up doing will be the same as your competitors, you might have read the same books and attended the same conferences.

Do something that makes you stand out.

And I'll let you into a secret, it is not the formatting of the text you deliver or 3d charts. That has been done to death.

And its not that hard.

Here's a example, everyone will report that a metric (say conversion) was 53% for keyword z and it was 56% for keyword q. Why don't you compute statistical significance between the two? Rather than reporting those two numbers out you can show how much confidence there can be in those numbers.

See how easy it was to stand out?

unique 2

Or here's another one. One of the Analyst started by stating that they were leaving out a time period that could distort the data. Interesting that they thought of that.

You could likewise eliminate from your analysis sources that reflect "one time only not repeatable events". Why bother?

Or try this, measure offline impact of the online activity! It is hard to do and you'll stand out!!

Business life can be a contact sport (competitors certainly are) and if you want to win then you have to have a UVP – a unique value proposition.

Never let a analysis leave your computer without making sure that there is something unique in it that will stand out.

See that was not hard?

Here is a summary of the "Avinash Filters for Awesome Analysis Presentations":

1) No data pukes.

2) Hard tie to business outcomes. Always.

3) Usage of other tools (True Analytics 2.0).

4) Not boring. Please.

5) Connect insights with actual data.

6) Meet the "expectations of scale".

7) Have something unique. Enough said.

Do you agree with the list? Have something to add? Would you like to "puke" :) on something, or simply disagree? Please share.

From your experience are there techniques in presenting analysis (or conducting them) that have worked particularly well for you? It would be awesome to have your insights and lessons. Thank you.

Happy Birthday to Nelson Mandela! An icon, an amazing human being, a true leader. Here is a great four minute audio and photo tribute, please check it out: Nelson Mandela at 90.

PPS (Bonus!):
Due to popular demand, and thanks to the WAA's permission, here are the top four winning entries from the WAA championships. This is a great way for you to learn more about how to present great analysis (and they each took a different tact).

[It is quite gratifying to me at some level that three of the top four are international entries. Validates for me the superior analytical sophistication that is outside the US.]

Next Page »