The reason is simple: The ecosystem within which you function on the web contains mind blowing data you can use to become better.

Your traffic grew by 6% last year, what was your competitor's growth rate? 15%. Feel better? : ) When should you start doing paid search advertising for tours to Italy for 2011? In May 2010 (!). What is your "share of search" in the netbook segment compared to your biggest competitor? 9 points higher, now you deserve a bonus! How many visitors to your site go visit your competitor's site right after coming to yours? 39%, good god! Where to do display advertisements to ensure you get in front of men considering proposing to their girlfriends (or boyfriends)? Go beyond targeting men between the age of 28 and 34, use search behavior and be really smart.

I am just scratching the surface of what's possible.

It is simply magnificent what you can do with freely available data on the web about your direct competitors, your industry segment and indeed how people behave on search engines and other websites.

The secret to making optimal use of CI data lies in one single realization: You must ensure you understand how the data you are analyzing is collected.

Not all sources of CI data are created equal. It is key that before you use the data that comScore or Nielsen or Google or HitWise or Compete or your brother-in-law shove into your face that you understand where the data comes from.

Once you understand that you choose: 1. The best source possible that is 2. The right answer for the question you are asking (which implies you have to be flexible!).

Here are the sources of competitive intelligence data.

#1: Toolbar Data.

Toolbars are add-on's that provide additional functionality to web browsers, such as easier access to news, search features, and security protections. They are available from all the major search engines such as Google, MSN, Yahoo! as well as from thousands of other sources.

These toolbars also collect limited information about the browsing behavior of the customers who use them, including the pages visited, the search terms used, perhaps even time spent on each page, and so forth. Typically, data collected is anonymous and not personally identifiable information (PII).

After the toolbars collect the data, your CI tool then scrubs and massages the data before presenting it to you for analysis. For example, with Alexa, you can report on traffic statistics (such as rank and page views), upstream (where your traffic comes from) and downstream (where people go after visiting your site) statistics, and key-words driving traffic to a site.

Millions of people use widely deployed toolbars, mostly from the search engines, which makes these toolbars one of the largest sources of CI data available. That very large sample size makes toolbar data a very effective source of CI data, especially for macro website traffic analysis such as number of visits, average duration, and referrers.

Search engine toolbars are a ton more popular which is the key reason that other toolbars data sources, such as Alexa, are not useful any more (the data is simply not good enough).

Toolbar Data Bottom-line: Toolbar data is typically not available by itself. It is usually a key component in tools that use a mix of sources to provide insights.

#2: Panel Data.

Panel data is another well-established method of collecting data. to gather panel data, a company may recruit participants to be in a panel, and each panel member installs a piece of monitoring software. The software collects all the panel’s browsing behavior and reports it to the company running the panel. Additionally the person is also required to self report demographic, salary, household members, hobbies, education level and other such detailed information.

Varying degrees of data are collected from a panel. At one end of the spectrum, the data collected is simply the websites visited, and at the other end, the monitoring software records the credit cards, names, addresses, and any other personal information typed into the browser.

Panel data is also collected when people unknowingly opt into sending their
data. Common examples are a small utility you install on your computer to get the weather or an add-on for your browser to help you auto complete forms. in the unreadable terms of service you accept, you agree to allow your browsing behavior to be recorded and reported.

Panels can have a few thousand members or several hundred thousand.

You need to be cautious about three areas when you use data or analysis based on panel data:

The panel methodology is based on the traditional television data capture model. In a world that is massively fragmented, panels face a huge challenge in collecting accurate and complete (or even representative) data. A rule of thumb I have developed is if a site gets more than 5 million unique visitors a month, then there is a sufficient signal from panel-based data.

Panel Data Bottom-line: For companies such as comScore and Nielsen panel data has been a primary source for competitive reporting they provide their clients. But because of the methodology's inherent limitations, recently panel data is augmented by other sources of data before it is provided for analysis (including for a subset of data you'll get from comScore and Nielsen – please check & clarify before you use the data).

#3: ISP (Network) Data.

We all get our internet access from Internet Service Providers (ISP's), and as we surf the Web, our requests go through the servers of these ISP's to be stored in server log files.

The data collected by the ISP consists of elements that get passed around in URLs, such as sites, page names, keywords searched, and so on. The ISP servers can also capture information such as browser types and operating systems.

The size of these isps translates into a huge sample size.

For example, Hitwise which chiefly relies on isp data, has a sample size of 10 million people in the United states and 25 million worldwide. Such a large sample size reduces sample bias (surprise!). There are also geographically focused competitive intelligence solutions, like Netsuus in Spain, that provide analysis from excellent locally sourced data.

The other benefit of ISP data is that the sampling bias is also reduced; since you and I don’t have to agree to be monitored, our ISP simply collects this anonymous data and then sells it to third-party sources for analysis.

ISP's typically don’t publicize that they sell the data, and companies that purchase that data don’t share this information either. So, there is a chance of some bias. Ask for the sample size when you choose your ISP-based CI tool, and go for the biggest you can find.

ISP Data Bottom-line: The largest samples of CI data currently available comes from ISP data (in tools like Hitwise and Compete). Though both tools (and other smart ones like them) increasingly use a small sample of panel data and even some small amount of purchased toolbar data.

#4: Search Engine Data.

Our queries to search engines, such as Bing, Google, Yahoo!, and Baidu, are logged by those search engines, along with basic connectivity information such as IP address and browser version. In the past, analysts had to rely on external companies to provide search behavior data, but increasingly search engines are providing tools to directly mine their data.

You can use search engine data with a greater degree of confidence, because it comes directly from the search engine (doh!). Remember, though, that the data is specific to that search engine—and because each search engine has distinct user base, it is not wise to apply lessons from one to another.

With that mild warning here are some amazing tools. . . .

In Google AdWords, you can use Keyword Tool, the Search-based Keyword Tool and Insights for Search.

Similar tools are available from Microsoft: Entity Association, Keyword Group Detection, Keyword Forecast, and Search Funnels (all at Microsoft adCenter Labs).

Search Engine Data Bottom-line:Search engine data tends to be the primary, and typically the best source you can find, for search data analysis. If you are analyzing data for your SEO or PPC campaigns and you find the search engine providing the data then you should instantly embrace it and immediately propose marriage!

#5: Benchmarks from Web Analytics Vendors.

Web analytics vendors have lots of customers, which means they have lots of data. Many vendors now aggregate this real customer data and present it in the form of benchmarks that you can use to index your own performance.

Benchmarking data is currently available from Fireclick, Coremetrics, and Google Analytics. Often, as is the case with Google Analytics, customers have to explicitly opt in their data into this benchmarking service. But this is not always true for all vendors, please check with yours.

Both Fireclick and Coremetrics provide benchmarks related to conversion rates, cart abandonment, time on site, and so forth. Google Analytics provides benchmarks for visits, bounce rates, page views, time on site, and % new visits.

In all three cases, you can compare your performance to specific vertical markets (for example, retail, apparel, software, and so on), which is much more meaningful.

The cool benefit of this method is that websites directly report very accurate data, even if the web analytics vendor makes that data anonymous. The downside is that your competitors might not all use the same tool as you; therefore, you are comparing your actual performance against the actual performance of a subset of your competitors.

With data from vendors, you must be careful about sample size, that is, how
many customers the web analytics vendor has. If your web analytics vendor has just 1,000 customers and it is producing benchmarks in 15 industry categories, it might be a hit or a miss in terms of how valuable / representative the benchmarks are.

WA Vendor Data Bottom-line: Data from web analytics vendors comes from their clients, so it is real data. The client data is anonymous, so you can’t do a direct comparison between you and your arch enemy; rather, you’ll compare yourself to your industry segment (which is perfectly ok).

#6: Self-reported Data.

It is common knowledge that some methods of data collection, such as panel-
based, do not collect data with the necessary degree of accuracy. A site’s own analytics tool may report 10 million visits, and the panel data may report 6 million. To overcome this issue, some vendors, such as Quantcast and Google’s Ad Planner, allow websites to report their own data through their tools.

For sites that rely on advertising, the data used by advertisers must be as
accurate as possible; hence, the sites have an incentive to share data directly. If your competitors publish their own data through vendors such as Google’s Ad planner or Quantcast, then that is probably the cleanest and best source of data for you.

One thing to be cautious about when you work with self-reported data. Check the definitions of various metrics. For example, if you see a metric called Cookies, find out exactly what that metric means before you use the data.

Self-Reported Data Bottom-line: Because of its inherent nature, self-reported data tends to augment other sources of data provided by tools such as Ad Planner or Quantcast. It also tends to be the cleanest source of data available.

#7: Hybrid Data (/All Your Base Are Belong To Us).

Competitive intelligence vendors are observing you from the outside. Any single source, toolbar or panels or isp or tags or spyware etc, will have its own bias / limitation.

Some, smart, vendors now use multiple sources of data to augment the data set they started their life with.

The first method is to append the data. This is what's happening in the case of Quantcast and Google Ad Planner, in both cases they have their own source of data to which your self reported data is added. The resulting reports are "awesomely good".

The second method is to put many different sources (say toolbar, panel, isp) into a blender, churn at high speed, throw in a pinch of math and a dash or correction algorithms, and – boom! – you have one "awesomely good" number. A good example of this is Compete or the DoubleClick Ad Planner.

Google's Trends for Websites is another example of a tool that uses hybrid data for its reporting (see answer #2 here).

The benefit of using hybrid methodology is that the vendor can plug in any gaps
that might exist between different sources.

The challenge is that it is much harder to peel back the onion and understand some of the nuances and biases in the data (sometimes mildly frustrating to analysis ninja's such as myself!).

Hence, the best-practice recommendation is to forget about the absolute numbers and focus on comparing trends; the longer the time period, the better.

Hybrid Data Bottom-line: As the name implies, hybrid data contains data from many different sources and is increasingly the most commonly used methodology. It will probably be the category that will grow the most because frankly in context others look rather sub optimal.

[Update:] #8: External Voice of Customer Data.

This is one often overlooked source of competitive intelligence (/benchmarking) data. There are several ways to use Voice of Customer data.

For starters various companies such as iPerceptions (like CoreMetrics, Google Analytics, Fireclick above for clickstream) publish Customer Satisfaction & Task Completion Rate (my most beloved metric!) numbers for various industries.

If I am in the internet retail game I can use these benchmarks to compare my performance:

Or I can dig deeper and compare my performance by the Primary Purpose segments:

Both of the above are from the iPerceptions Q4 2009 Ecommerce Benchmark report. You'll find other reports in the Resource Center.

With other sources like the ACSI (American Customer Satisfaction Index) you can get a big deeper. Just choose an industry from their site, I choose Internet Travel, and bam!

If I am Travelocity I am wondering what in the name of Jebus did the other guys do last year to have all those gains in Satisfaction where I got a zero.

And really what are those guys at Priceline eating! 5.6 points improvement just last year, 15 points over the last 9! Sure they started with a smaller number but still.

What can I, sad Travelocity, learn from them? From others?

In both cases above the intelligence came from a third party doing the research and giving you data for free.

But you can also commission studies, from the two companies above or one of thousands like them on the web.

Or you can do it yourself.

I just met a Top Web Company the other day and I spent $300 on 20 remote usability participants to go to the website of Top Web Company and their main competitor. I gave the usability participants the exact same tasks to do on both sites.

The scores were most illuminating (and embarrassing for Top Web Company). It allowed me to (without working at either company) collect competitive intelligence about how each were delivering against: 1. Task Completion Rate and 2. Customer Satisfaction.

You could collect your own competitive intel using UserTesting.Com, User Zoom, Loop11, or one of many other tools.

Or for even "cheaper" (and a bit less impactful) insights you can use something delightful like www.fivesecondtest.com. Upload how your pages, upload those of your competitors (or complete strangers not in your industry) and learn from real users which designs they prefer and what works best.

Hybrid Data Bottom-line: I love customers, I love Task Completion Rate as a powerful metric, I love VOC. Above are three simple ways in which you can collect competitive intelligence using Voice of Customer data and drive action perhaps even faster than the first seven methods!

[PS: Nothing, absolutely nothing, works better to win against HiPPOs than using competitors and customers!]

The Optimal Competitive Analysis Process.

A lot of data is available about your industry or your competitors that you can
use to your benefit.

Here is the process I recommend for CI data analysis:

Don’t skip the steps, and glory will surely be yours.

See the additional posts linked to below for types of analysis you can do once you choose the right tool [OR you can also start on page 221 of my new book Web Analytics 2.0!].

Ok now your turn.

Do you use a source of competitive intelligence data not covered in this blog post? Which of the above 7 is your favorite? Was there something surprising you learned in this post? What is one thing you would add to the critical analysis above?

Please share your tips, best practices, critique in comments.

Thanks.

PS:
Couple other related posts you might find interesting:

• Competitive Intelligence Analysis: Metrics, Tips & Best Practices
• Paris Hilton, Kim Kardashian & Telling Stories With Data
• Competitive Intelligence Analysis: Google / DoubleClick Ad Planner
• Competitive Intelligence Analysis: Google Insights for Search
• Competitive Intelligence Analysis: Google Trends for Websites
• Google’s Search Based Keyword Tool

The Definitive Guide To (8) Competitive Intelligence Data Sources! is a post from: Occam's Razor by Avinash Kaushik

There is nothing particularly wrong with the standard 19,000 reports in your web analytics tool. But they do represent the Vendor's best guess about what you should look at. Sometimes they even get it right.

Most of the time though your business is absolutely unique (even as it exists amongst hundreds of competitors) and it is absolutely important that you take your web analytics tool and mold it around you. The power that is given to you even in free tools like Yahoo! Web Analytics and Google Analytics can create a view of data that will help you find faster insights.

This post is inspired by a suggestion from Horia Neagu in reply to my tweet asking for blog post ideas. My thanks to Horia.

Horia's question was: How about a post entitled "10 Google Analytics Custom the Reports You Absolutely Must Set Up"?

I am not going to write about that, simply because the very idea that a report is custom means that there are probably no "ten standard custom reports" to set up.

I am going to share one recommendation and two ideas for making your own custom reports better.

This is a "teach a person to fish" type post. Sorry. :)

No Goals, No Glory.

Here's a cliché: If you don't know where you are going, any road will take you there.

Nowhere is this more applicable than when it comes to trying to find insights from your data you can action.

You report your poor heart away, no one seems to be able to take anything you give and take action.

Often it is the case that you and I have not bothered to sit down with he HiPPO / the boss's boss and tried to understand what in the name of all that is holy and pure is our website trying to do!

What are the goals?

No custom report (or advanced segment, the life giving oxygen) was ever created without an answer to that question.

So ask that question. Get an answer before you go about your customization ways.

If your leaders / clients truly want wisdom from you they will answer the question. But it does happen sometimes that begging or throwing yourself at her/him does not elicit anything of value.

In those rarest of rare cases (after you have already submitted your resume to other companies that will cherish you for the golden child you are) try to figure these one or more of these three things out:

1. Where it the company currently spending money? Email marketing? Affiliate? Paid Search? Online PR?

And what's the biggest bucket?

Now go create your custom reports because if you can help the HiPPO's figure out how to reduce cost of acquisition they will love you more than you can imagine.

2. If possible, without violating HR policies, figure out what your boss's salary bonus is tied to.

Start doing analysis that will help your boss get a raise. A great goal to have, love and promotions likely.

3. Go visit your website, yes yes the one you have not used for a while. : ) Find out the single worst thing about it (should take you less than half hour of clicking around).

Now go look for data that will help you prove that the worst thing is the worst thing. Not a bad goal to have to fix what's completely broken, and people will listen.

Three good proxies if you have no goals to start with. Ideally you'll know what your Macro Conversion is so you'll start your analysis with a bang. Super ideal would be that you know both your macro and micro conversions!

Remember: No goals, no glory. Not for you. Not for your boss. Not for your company.

Custom Reporting Tip #1: Always, Always, Always Focus On The End To End.

One problem with standard web analytics reports is that the data you need is scattered all over the place, making it harder for you to find insights.

For example I am trying to figure out which pages stink and need fixing. In Yahoo! Web Analytics the standard report only shows Page Views and Average Time on Page. How much good will that do?

Or I want to figure out which sources of traffic I should make love to or divorce? The standard Google Analytics report spreads the data I need over four tabs.

Custom reports are good at solving this problem. Drag the dimension you need (traffic sources, landing pages etc) and analyze the data by choosing the metrics that tell the end to end story.

End to end has three pieces: Input. Onsite Activity. Outcome.

Here is my favorite, custom, traffic sources report:

By inputs I mean metrics that help you understand (in line with your goals) how well the "top of the funnel" (usually acquisition) is working.

In my case that is measuring Visits (to know who is sending how much) and % of New Visits (to know who is sending how much that is of value to me – new visitors are very valuable in this case).

At a glance I have the information to start making some preliminary superficial judgments about performance.

By onsite activity I mean choosing metrics that help you understand the behavior of your visitors on your website (thus absolving your Acquisition team of any blame, perhaps!).

In my case that is measuring Bounce Rate (not so fast Acquisition team, don't get me bad traffic! :)) and Average Time on Site (as a proxy of measuring if the landing pages are engaging visitors and as a proxy of how much each traffic bucket engages with the site).

Depending on my goals I would choose different onsite activity metrics for my custom report.

By outcomes I mean, well you don't need to know do you? You read this blog! I am all about outcomes, every day!!

In my case the outcome metrics are Goal 1 (my macro conversion) and Average Value created for my website.

I could also have used $ Index or Per Visit Goal Value metrics if I were analyzing a non-ecommerce / content only website.

Remember Without a crisp articulation of outcomes every battle you fight will be lost, every day and you will live a very very unhappy life.

With these end to end metrics my custom report tells me stories that would otherwise take too long to piece together (or stories I might have missed completely).

One of the stunning realizations was just valuing Twitter traffic for example. (Click on the above report for a higher resolution report).

My twitter (social media) campaigns were doing exceptionally well. Lots of traffic (#3) overall, the second highest conversion rate (0.78%) and a Average Value that was not the best but rather sweet ($136 – which looks ever better when you compare the cost which is negligible).

Yet non focused traffic from twitter is not doing that well. 0.33% conversion and $39 average value. Pathetic.

I can now jump, like a na'vi, from row to row understand performance quickly and efficiently.

The power for a custom report that shows the end to end story.

It is so easy too.

For example here is the exact same custom report created in Yahoo! Web Analytics, just 30 seconds of drag and drop:

[Click on the image for a higher resolution version.]

Always go e2e. If you don't, you better have a good excuse!

Custom Reporting Tip #2: Create "Micro Eco-Systems".

I think I can honestly say that I have get to meet a single decision maker or a department or a company that has yet to tell me: "You know what the problem is Avinash? I don't get enough reports."

:)

We love spewing out data and pretty soon your company has 200 reports and I'll bet you $50 that not a single decision is actually based on data.

So fix it.

Create micro eco-systems.

What I mean are custom reports that do three things:

Let's understand how to do this by looking at a real life example.

My goal is to create a "search ecosystem" report that collects different important pieces of data, for three different stakeholders, all into one place.

I do that by first understanding who all the stake holders are who'll need to use the data (let's hope!) and doing a simple stake holder interview to understand what their business goals are.

Now rather than spamming everyone with a report (that no one will find, I'll have a hard time version controlling, and other such pain), I'll just put it all together in one place (at least in Google Analytics due to a simple yet exceptional feature – tabs!!).

Here is a pictorial view of the process that I'll go through:

[Click on the image for a higher resolution version.]

My first "client" is the Acquisition team, they are responsible for spending the company's money wisely. They are measured on bringing new Visitors (potential customers) to the site.

I create a tab for them that shows Visits, New Visits, Bounce Rate and Average Time on Page (not site). I add the latter two because I want them to see their end to end view and I want them to realize they hold some level of responsibility for people not just coming, but also staying.

We have just one report, each day (God willing) they'll log in and see their own personalized sweet view of the data:

[Click on the image for a higher resolution version.]

But I am not done yet.

Next up is my HiPPO. Let's call him Paul.

Paul only cares about Revenue and all things connected to revenue. He does not care about any other metric. Nothing wrong with that.

Rather than creating another report for Paul I click on *Add Tab* simply do this:

Create a person view for Paul. I throw in Visits (I have to give him some context and some Input metric) and Goal Conversion Rate (so he knows efficiency), Goal Value, Revenue, Shipping (because Paul is having us charge lots for shipping because he thinks of it as a profit center (!!), not great but remember I am personalizing).

Here's the resulting output:

[Click on the image for a higher resolution version.]

You know what the result is?

Paul actually looks at the data every other day (and a bit deeper each Friday) because it does not contain crap. It contains just what he needs to do his job (find people to reward and find people to fire).

That is what you are going for. Taking people from data apathy to data usage.

[Oh yes, yes, I noticed Revenue and Shipping are zero in the above screenshot. I wish I could show you someone's real data! Not today. But you get what the report is trying to do.]

Finally there's Amy. Another key stake holder, but a tougher nut to crack. You see her bonus is tied only to Visits, a low bar if there ever was one.

So what do you do?

You can't over smart Amy, she is too smart for you (and probably a level or two higher).

You are going to lose her if you give her too much data.

You need to entice her to start using data, and restrain your smarts – you know you want to create a impressive 8 column report!

In this case I simply add a tab. It says Amy Chang (so she knows it is her very own personal report). It has Visits and Average Time on Site. I added Time on Site as a Outcome metric, just to keep up with my outcomes obsession.

[Click on the image for a higher resolution version.]

It is simple. It is effective.

It will get her to see just the data she wants (plus one more thing :)).

And here's the sweet part…. since this his an eco-system report perhaps she (and Paul as well) might see other pieces of data in other tabs and might be intrigued enough to ask you to add more metrics.

Then and only then and only at that time and only when you are asked (am I repeating myself?) add those metrics. It is vastly more likely that she (and Paul) will use the data.

There you go… one report that has all the data each stake holder needs personalized and customized.

No one is going to come to you and say: "hey want folder is my search report" or "I don't understand what all this data is saying" – it is personalized. And when you have to make changes, it is all in one place.

Well…

Such simple little things: tabs (or cup cakes :)).

Makes it so much easier for you to create a data democracy. And a bit sad that you can't do this with most paid web analytics tools today – yes you can create a custom report but the above report would be a one huge 13 column report that:

I hope all paid web analytics vendors will incorporate this feature, for the sake for our data democracy!

That's the story. Goals. End to End. Micro self contained eco-systems.

I was hoping to teach you how to fish, rather than just tell you which 10 reports to create. Regardless of the specifics of the reports and metrics above I hope you have learned a bit more as to how to think about approaching the issue and the important things to focus on.

Custom reports are a powerful way to take what looks overwhelming in web analtyics – REPORTS and DATA – and make it didapper. It is also a wonderful way to start the journey of your company, big or small, to start using data.

Ok now your turn.

Do you have custom reporting tips to share with us? What small or big thing you have done that really really worked for you? Have you tried end to end reports? How about micro eco-systems? What strategy completely failed? Got a custom report you think everyone in the world should be using?

Please share your stories / tips / bruises / successes.

I'll send the best one a copy of my new book Web Analytics 2.0.

UPDATE: It was hard to pick just one winner so a copy of the book goes out to SteveK (for advocating common sense!) and to Ali Shah (for emphasizing sharing of context). A bonus prize also goes to MGSeeley (for bringing a smile with his adorable analytics haiku!).

Analysis Ninjas: Leverage Custom Reports For Better Insights! is a post from: Occam's Razor by Avinash Kaushik

As we spring to action full of passion I wanted to share with you all a short list of things that will expand your little world of online marketing & web analytics.

We all have a tendency of getting caught in a rut, using the same tool to do the same things and spew forth the same data. Change is hard, even if we know that we should be executing a multiplicity strategy to win in the web analytics 2.0 world.

Before all the excitement of the new year wears out, here are five simple things I would love for you to try so that your company will have a glorious truly data driven 2010!

#1: Don't suck.

Seems obvious. And yet in our quest for ever more hard problems to solve we forget that the number one goal of every website is not to suck. Especially at the really simple and basic things.

At a recent conference there were three keynotes.

One was extolling the wonderfulness of their multi channel campaign tracking. When I went to their website it was a 100% flash website with a constrained small size where it took too much looking to click on anything and then too much scrolling to read anything and unclear calls to actions (if any). That's sucking. No amount of great multi channel tracking will save this company, they suck at the basics.

The second was about predictive analytics and how using massive integrations between online and offline databases they had accomplished some really cool reporting of data (and make no doubt the IT work done over 18 months to accomplish this was cool). Their home page is a mess. 24% of the content covers what any visitor might want, rest is the company shouting at you (in many annoying ways). That's sucking.

The third was about how to create data driven cultures and how this person had created a impressively big cross functional team across multiple countries and standardized on Omniture after a lot of work over two and half years. I did a search on some of their products and they did not have page one search listings (on Google or Bing) for what should be their head terms. (That's sucking.) They did have PPC ads, which I click on the ad for specific product they land me on generic nonsense pages. That's sucking.

I share these stories to illustrate vividly how we in the web analytics world get lost in our data and Omniture and Google Analytics and reporting and lose sight of the the basics and the customer experience.

It is important to realize that if you suck nothing else matters. Not your api driven integrated massively multi channel attribution analyzed campaign lifetime databases. That is not going to save you or your company.

Before you attempt the hard make sure that you do all the standard stuff to ensure your company has a fighting chance to win.

Here are some tips to inspire you:

• I LOVE looking at the bounce rates for the top 20 landing / entry pages to the site. Find the losers, fix 'em. These guys are so bad they could not even get one click from the visitors.

• Sit down with the owner of the top ten pages to the site and look at them. I mean really look at them and ask this question: "What the heck are we trying to do with each page?" Make sure there is a clear answer (and a match between Customer Intent and Webpage Purpose).

• Check the load time of your important pages. Use something simple like: www.WebSiteOptimization.com Or whatever complicated tool you have.

• Sign up for your websites campaigns using your personal email address. See how the emails look. Relevant? Personal? Click on the links, what to you see on the landing pages? Fix!

• Create a funnel for your cart / checkout / lead submission process. Find the biggest abandonment page. Fix it.

• Ask your Finance department where most money is being spent on the web. PPC? Affiliate? Display? What? Take a week to segment that data and find out how to save 10% of the cost.

• Count the number of links on your main pages. I mean count them. There are 98 links on a travel site I am looking at right now, on the page for a hotel in Chicago. 98! This is a top site.

  What are the analytics people doing if they are not helping the product page owner figure out how to kill atleast 50% of those links on a product specific page. There should be one link: Search for Hotel or Make Reservation! Do this for your site.

• Fix the 25 things Dr. Pete lists in this delightful checklist: 25-point Website Usability Checklist.

There are so many ideas. I hope that before you go for massive web analytics glory that your use your wonderful powers first to make sure your site and customer acquisition strategy does not suck.

PS: Bonus tip: Make sure you visit your website once a week, atleast.

#2 Learn basic statistics.

The days of tools and reports simply puking data out are rapidly reducing. No longer can tools or "analysts" just puke 15 metrics on a report and hope to survive.

Web Analytics tools are starting to become smart (see: Analytics Becomes Intelligent). Data is starting to truly get numerous.

For all of the above reasons it is becoming ever more important that you are know atleast Statistics 101. You don't have to be armed with the knowledge of how to create various models or be able to jump into SAS and get naked with it. But you are going to have to know what a mean and a median and r squared and standard deviations and Z scores and confidence intervals and all that lovely stuff is.

If you have not been exposed to statistics perhaps you can take a class at a local community college or university. Many employers will pay for ongoing job relevant education.

Alternatively get one of the simpler books on the topic and immerse yourself in self education. Regardless of if you are a novice or an expert I think one of the best books to start with is The Cartoon Guide To Statistics ($13). A cartoon book? Yes. It is quite good.

Once you know statistics 101 you'll find that you'll think of data analysis differently and you'll get better at finding that proverbial needle of insight in the haystack of data. Knowledge of statistics is a key arrow to add to your analytical skills quiver.

Hello statistical significance!

#3 Try one (or two) new usability / VOC tool/'s.

My passion for the customer is, as they say, legendary!

Part of it is the humility I have developed at the powerlessness of clickstream data to answer all the needed questions. Part of it is that there are just so many darn good options out there to listen to our customers.

So this year why not try one of the newer more powerful and yet cheap usability analysis tools?

Here are some tools that are pretty cool and unique:

• Five Second Test. I absolutely love the idea of collecting "first impressions" from current customers, employees or just randomly selected people. Within thirty seconds you can take a screenshot of your lovely home page or landing page, upload it and for free get feedback from real people.

• 4Q / Kampyle / UserVoice. Each of these tools does something completely different, and yet each allows people to type things that you can read and be wow'ed or saddened by. Why not try one of these tools this year and truly get in touch with your customers and a real and meaningful way?

• UserTesting.com. You are not a small enough company, or a big enough one for that matter, to do usability testing. This is usability testing for ultra cheap, $29 per person. Set out the tasks, identify your audience, test happens, you watch the video and read comments, you cry, you fix things, you become rich.

  Also checkout Feedback Army.

• WebSort / OptimalSort. The information architecture on most website is terrible and the reason is that company employees create it for themselves. A great option to hear from the customers was to do card sorting studies. Problem? Expense! Not any more baby. Both these tools are quite affordable, all online and in a fraction of the time it would take to do a offline card sorting study you can get the key data you need. Sweet.

You don't have to do all of the above. But you do have to listen to your customers.

In 2010 Consider trying just two tools listed above that you have not used so far. I promise you that you'll want to give me a big hug the next time you see me.

#4 Try one new competitive intelligence tool.

I practically have a illicit love affair with competitive intelligence. And I am not embarrassed!

If I ever come to see your company, or you see me presenting publicly, then you have seen me present data about your company / industry and then proceed to say nice / not nice things. There is just so much gold out there to be discovered.

Here are some tools for you to try, ideas for analysis you could do:

• Compete.com / Trends for Websites. I love the depth of data now available in both tools for free (even if you use just the free part of Compete). Index your overall performance against your competitors.

  Where do people go after they leave your site? What are the top five referrers for your competitor? What are the top sites that get traffic for the word love? All free from Compete.

  People who visit my site, what other sites do they visit? What are the things they search for? What's the difference between US traffic and India? All free from Trends for Websites.

• Google's Search-based Keyword Tool. If you have never explored the long tail for your website (if you are a medium to large site) using SbKT you might be committing a crime. If you have never taken a list of keywords AND the landing pages recommended by SbKT where you have zero impression share and given it to your SEO team then you should feel bad. There is so much here.

  [Learn how to use SbKT here: Monetize The Long Tail of Search.]

• Google Ad Planner. Some display / banner ads stink because they are just terribly produced and blink and annoy you with sound and do insane things when you move your mouse over them inadvertently. Most display ads stink because they are not relevant / well targeted. Make sure that is not your ads. Use the Ad Planner to hone into the exact sites where you can find your audiences.

  What sites are visited by: Men who are in the market for engagement rings. Women who are interested in the NFL. Young adults who are looking to buy net books. Affluent 100k+ folks or comic book buffs or brides to be.

  Now go buy advertising on those sites (from any ad network) and earn a higher ROI on your campaigns.

  [Learn more about Ad Planner: Competitive Intelligence Analysis: Google Ad Planner]

These four tools should keep you busy for a long time. Don't go at it all at once. Ask your boss's boss what his next 90 day priorities are, find the tool above that might have the insights, go on a honeymoon with it.

#5 Identify two new micro-conversions and goal values for each.

The road to web analytics glory (and a promotion for you) runs through the Micro Conversions path.

I am absolutely convinced that we don't get the love that we deserve from our company leaders because (even if we get beyond data puking) we rarely quantify the impact of all of work that the website is doing.

During Q1 make it your personal quest to identify two new micro conversions for your website (many ideas in the preceding blog post).

Now make sure, and this is absolutely key, you take one more step and quantify the economic value of each micro conversion (instructions and ideas: pages 159 to 162 in my new book Web Analytics 2.0).

That economic value will help you arrive at the number on the right, $83,848. That number will finally help you understand the complete value your website is adding to your business (only $21,454 is from the Macro Conversion). That number will allow you to measure your campaigns with a level of accountability that will be supremely awesome.

If you do nothing else on this list (I hope it does not come to that), please make sure you do this item. It is that important (especially if you are a non-ecommerce b2b government peaceful protest photo sharing website).

For the true Analysis Ninjas let me share one bonus item, one thing that will put even them above the top. . . .

Bonus: #6 Measure one thing that is "intangible".

The hardest thing to do in online analytics is to measure the intangible. How did people feel about the website experience? What was the positive brand lift? Did the unaided brand recall improve 60 days after the campaign (online or offline)? And more such questions.

Each is really hard to answer, one must think differently.

Here is a post with seven different strategies: Brand Measurement: Analytics & Metrics for Branding Campaigns.

As an Analysis Ninja go all out on three of them this year and take your business to the next level of measurement and insights.

Good luck ya'll!

Ok now your turn.

Care to share examples of sucking that you have killed on your websites? Got a creative use of statistics in your web metrics practice? Which is your favorite online customer listening strategy? Have you had success with quantifying goal values for your micro conversions?

What is your company's online, or online analytics, new year resolution?

Please share your thoughts via comments, thanks much!

Five Sweet Web Analytics Resolutions To Kick It Up A Notch is a post from: Occam's Razor by Avinash Kaushik

It is almost never tools. Not any more.

Surprisingly it is often not their will to use data, that is there in many cases.

Sometimes it is that they don't follow the 10/90 rule.

It is always the organization structure.

Specifically: Who owns web analytics / who it reports to from a org structure perspective.

[Let me hasten to add that this, web analytics ownership, does not exist in a vacuum. If your overall web business is misaligned from an org perspective then honestly there is no hope for you, regardless of where analytics sits.]

This is a topic I cover in my new book, Web Analytics 2.0. Chapter 14: HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture.

In this blog post I'll share a unique "case study", more like one person's problem, and my advice to them about how to think about the organization problem.

Here's the question / challenge:

I’m facing an issue I’m sure many large organizations struggle with: where should an organization place its web analysts? Currently, I lead a small team of analysts at a medium-sized bank. We are part of the Web Sales division, along with an e-commerce (online media) team and the content crew.

Web Sales is considered a channel in the same way our call-centre, local branches and customer account managers are. As such, we are not a part of the central Marketing (and Marketing Intelligence) teams at corporate. I see a few different options but would be happy to hear your opinion.

You will all agree that it is really hard to answer a question like the one above without spending time with the company and understanding its strengths and meeting the political players involved.

In this post let me share with you a common sense framework I use in my consulting engagements to figure out a home for web analysts.

Each facet of the framework also contains a peek into what I am thinking, best practices I have developed from all the bruises I have (as a Practitioner and a Consultant) and how I end up making the choices I do. I hope it is of value to you all (and now you don't have to pay me large sums of money to do this for you!).

The four pronged real world tested probing and loaded with politics framework to find a home for Web Analytics:

1. How long has the company been doing web analytics, what is the landscape of tools?

2. What's the state of analytical maturity of the organization (either the center or the division/silos)?

3. Who owns the power to make changes to the site (not who owns updating pages or hosting the site)?

4. Which physical organizational model will work best for you? Centralized? Decentralized? Something else?

Oh and one more thing…

Before you get upset (if you are in IT) please please know that the tweet above comes from someone has spent three years in IT, lived the life and paid the dues. It sadly simply does not work. A mismatch of skills, motivations and what the core existence is supposed to deliver. I'll reluctantly agree with you that there are perhaps exceptions to the rule, I'll believe it if you show them to me. :)

Which division / department offers the best possible home for Web Analytics?

After a lot of experimentation and failures I have come to realize that often (if above conditions are met) Marketing is the best organization for Web Analytics to be in. It is optimal because Marketing is in the business of raising awareness, connecting with customers, presenting the company's value proposition etc etc.

Unlike say Sales that is there to make a quota at any cost each quarter. Or PR that is there to pimp the company and it's greatness to the world (not that there's anything wrong with that). Or Corp Comm whose job it is to share information and where folks are not hired for their business savvy. Or…. other divisions. In my humble experience Marketing tends to have the right set of skills, motivations and their core existence is around current and future customers.

If they have the power in the company, Analytics will be happy there.

Caveat: Remember Marketing ownership is not a panacea. You'll have to go through the questions in the framework above and ensure that there is a strong business leader who owns driving changes on the site and that the company is on the right evolutionary path and…. all the things you read above. And even if Marketing owns web analytics the ideal you are shooting for is Centralized Decentralization.

[Update: Please see Jim Novo's thought on value of Finance as an option for owning Web Analytics.]

Now you know.

I hope you've found the four pronged real world tested probing and loaded with politics framework to be of value and that it helps you make better decisions about how to organize web analytics in your company. It is one of the hardest things to pull off right, and with all my heart I wish you all the very best in your journey.

Ok… your turn now.

What is the organization structure like in your company? Where does web analytics fit? Does it work? If not why not? What would you do differently? What do you think I am missing in my four pronged framework? From your experience how would you make it better? What is one thing I got completely wrong?

Please share your feedback via comments. Thank you.

PS:
Couple other related posts you might find interesting:

• Six Rules For Creating A Data Driven Boss!
• How Should Web Analysts Spend Their Day?
• Convert Data Skeptics: Document, Educate & Pick Your Poison
• Analytics Career Advice:”I am an Analytics God, I want more $$. How?”

Who Owns Web Analytics? A Framework For Critical Thinking. is a post from: Occam's Razor by Avinash Kaushik

Even with a broken right hand I can't help but write this post!

The waterfall of positive feeling stems from the fact that this book was very hard to write.

I only had one job, at Intuit, when I wrote my first web analytics book. I now have several full time jobs, plus this blog, plus speaking around the world, plus a family, plus… so much more.

It took weekends of writing and nights of editing and days of research combined with practicing the preaching by doing oodles of analysis and, more importantly, the support of the most understanding wife in the world.

At the end of it all it is rather gratifying to see one's book at a bookstore, helps grasp the magnitude of the process. And there's absolutely nothing quite like hearing your five year old yell in a busy Borders bookstore: "I FOUND DADDY'S BOOK!"

This blog post is in three parts: The pitch. Request for help. A lovely contest [Contest closed now, thanks for the entries!].

You don't have to read the whole thing & skip ahead, but that would hurt my feelings. :)

Here we go. . .

The Pitch:

I invite you to consider buying my second web analytics book. It is not only the most current book on everything important and bleeding edge in Web Analytics, it is a labor of love that will help you transform your personal thinking and assist in revolutionizing your organization (big or small).

It is not a technical book, though it will make you technically dangerous. It is not just a business book, though every dna strand in this book is more about online marketing than online analytics. It is not a hard book to read, though it is brain food.

Here's why I think you'll love it:

Chapter 1 The Bold New World of Web Analytics 2.0

No dragging of the feet, the book starts with a bang by laying out the framework that will be the center of every company that will leverage data (qualitative, quantitative, competitive) on the web. It ends with a challenge to embrace Multiplicity – without this it's goodbye greatness.

Chapter 2 The Optimal Strategy for Choosing Your Web Analytics Soul Mate

It will be hard for you to find a more compelling four step process to choose the right web analytics tool for your company. Soul searching, questions to torture vendors with, comparing vendors, running a pilot and negotiating a contract, it's all in there. You be off to the races right.

Chapter 3 The Awesome World of Clickstream Analysis: Metrics

The thing I enjoyed about this chapter (I know I wrote it, but still. . .) was that the first half works really hard to evolve your critical thinking skills. I love that because we take too much for granted, now you'll be skeptical. A good thing. The second half shows exactly how to pick the best metrics for your org and, my absolute favorite (Page 64), how to diagnose the root cause of a metrics performance.

Chapter 4 The Awesome World of Clickstream Analysis: Practical Solutions

When people think of web analytics everything they think about is chapter 4, and yet you'll find so many yummy treats here. The best WA report, segmentation, site search, SEO & PPC analysis, email, rich media, cookies, data sampling. . . . I am out of breath!

Chapter 5 The Key to Glory: Measuring Success

If I have one jihad it is to massively convert every person who touches the web to focus on measuring Outcomes! It is the one reason we can't achieve the greatness we so richly deserve. No more! Glory will be yours!! B2B. B2C. Small Biz. Large Biz. Non-Ecommerce. We make love to 'em all! One thing you'll read here that you'll read no where else? Computing Economic Value, a concept that will liberate you.

Chapter 6 Solving the “Why” Puzzle: Leveraging Qualitative Data

Oh, oh, oh qualitative analysis!! I am a Mechanical Engineer with a MBA, a late covert to the power of understanding the super sexy "why" by leveraging lab usability studies, surveys, card sorts, online remote testing and more. You get a jump start. The thing you'll adore: Pages 190 – 192.

Chapter 7 Failing Faster: Unleashing the Power of Testing and Experimentation

Sure you've heard of A/B and multivariate testing. But do you know how to truly win the game? There is no technical mumbo-jumbo here, just the real deal and how to get testing right. The thing you might not know / realize the power of: Controlled Experiments. I am convinced this is God's gift to online humanity, you'll agree with me by the time you reach Page 208.

Chapter 8 Competitive Intelligence Analysis

The most magnificent advantage the web possesses: everyone's data is available for everyone else to use. If Hilton Hotels has the data for Choice Hotels why not use it to "crush" them (sorry Sarah!). This chapter shows you how. I think the thing you'll be surprised by is at the start of the chapter (Data Sources, Types and Secrets).

Chapter 9 Emerging Analytics: Social, Mobile, and Video

The chapter I had the second most fun writing. Mobile, twitter, blogs, videos etc are just so darned hard to measure and so much changes every few hours that I had to really really work hard to find the essence of each and then make specific practical measurement recommendations that will stand the test of time. It was hard.

Chapter 10 Optimal Solutions for Hidden Web Analytics Traps

This is a collection of major reasons I think people fail at web analytics, and of course I boldly try to share how to avoid that fate. Behavior targeting, dashboards, accuracy, data mining, predictive analytics, and, the thing you'll appreciate the most IMHO, five steps for intelligent analytics evolution!

Chapter 11 Guiding Principles for Becoming an Analysis Ninja

All my life learnings laid bare. . . this is where you, yes you, start to evolve from a Reporting Squirrel to an Analysis Ninja! No metrics, data pukes, guidance on creating every more reports. No, none of that. Rather… analytical techniques, tips and tricks to apply to your job, how to evolve your thinking to a higher level.

Chapter 12 Advanced Principles for Becoming an Analysis Ninja

The chapter I had most fun writing (and rewrote the most number of times). It deals with two of the hardest practical challenges we face in the field of measurement: multi-touch campaign attribution analysis and multi channel analytics. Both are very hard to get right, both have a ton of fud out there, it was fun to share my recommendations.

Chapter 13 The Web Analytics Career

The chapter I should have had in the first book. How to plan a career in web analytics (paths, salary, longevity), and how to then cultivate the right set of skills. If you are a leader then how to spot great talent, how to interview them and make the right choice.

Chapter 14 HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture

Some might argue, rightly so, that the most elusive thing to accomplish is to truly bring data democracy to your organization. This chapter bravely hopes to help you do exactly that: excite people about data, remove organizational barriers, use data to change behavior, dealing with data quality, and creating data driven HiPPO's.

Convinced?

Nothing, absolutely nothing, in life is easy. But if you have the will and access to knowledge then that just might help you choose an optimal path, a path where your hard work will yield above normal results. That's my hope, and promise, with Web Analytics 2.0.

Jennie and I have decided to donate 100% of our proceeds from this book, just like for the first one, to two charities. This book benefits The Smile Train and Ekal Vidyalaya. We are very excited about that.

Request For Help:

As you all know my philosophy for this blog is eat like a bird, poop like an elephant. But if you are up for it I would love to ask you for a bit of help.

Recommend the book.
If you know someone who needs to turbocharge their online existence, please recommend Web Analytics 2.0 to them. Even in our hyper connected world, nothing works like a personal recommendation.

If you use a link please consider using: http://bit.ly/akwa20 That link has an affiliate code, all proceeds of which go to the above mentioned charities.

Review the book.
If you have a blog, website, twitter account, any kind of platform, it would be great if you could write a review of the book and help spread the word.

If you purchased the book online then please, pretty please, review the book on the store's website. Amazon. Borders. Target. Powells. Whatever you used.

Connect me.
I am very very bad at pimping. So if you know someone who is someone (or knows someone who knows someone) then please consider connecting us. Especially people outside our analytics / search circle. Authors. CEO's. Journalists. Influencers. TV anchors (or weather man/woman). Oprah (I can dream, can't I?).

Our world is separated by six degrees of separation, I am sure you know someone who just might consider helping me with my cause.

Share a picture.
I love getting to know my audience, and while your emails and tweets are pretty fun there is nothing like a picture.

I had a "Web Analytics: An Hour A Day Fan Mail" flickr group that has some incredible pictures from around the world, bringing my audience closer to me.

I would love to do the same again for my "Web Analytics 2.0: Fan Mail". Be as creative as you want to be. Babies. Cats. Posters. Cars. Places. Or the best, you. All would be welcome.

I will only post the pictures with your permission. Please send them to blog at kaushik dot net. Thanks!

A Lovely Contest:

[The contest is closed now. Winning entry details.]

Steve Cunningham invited me to be a part of a little "contest" he is running. The prize is a delight, you get to win a pack of seven books on online marketing & social media: Six Pixels of Separation, The New Community Rules, The Whuffie Factor, Trust Agents, Crush It!, Duct Tape Marketing, and Web Analytics 2.0.

How to win you ask? Two ways.

1. Answer this question in comments below: If you were to measure the success of a company's social media efforts how would you do it?

Pick any social media channel, or all. Only a short answer is required. The most innovative / interesting answer wins. No answer is too small or too simple.

[If you have my book already then my answers in the book to this question will win you major brownie points, but perhaps not the contest! :)]

2. You can get four more chances to win, if you want. Simply visit these blogs and answer a different question on each: Steve Cunningham, Beth Kanter, Tara Hunt, and John Jantsch.

Good luck!

A Word of Thanks:

This is from my book's acknowledgment page…

I would like to express my deep appreciation to the readers of my blog, Occam’s Razor. In approximately three and a half years I have written 411,725 words in my 204 blog posts, and the readers of my blog have written 615,192 words in comments! Their engagement means the world to me and motivates me to make each blog post better than the last. It is impossible to thank each person, so on their behalf let me thank three: Ned Kumar, Rick Curtis, and Joe Teixeira.

A very solid case can be made for the fact that neither one of my books would exist without you and your engagement and encouragement.

Gracias. Arigato. Ngiyabonga. Xie xie. Obrigado. Shukriya.

Web Analytics 2.0 Book: In Stores Now!! is a post from: Occam's Razor by Avinash Kaushik

"You know what… web analytics tools like Site Catalyst, Yahoo! Web Analytics, WebTrends, and yes even Google Analytics, are mostly glorified data pukers. Each tries to outdo the other in trying to collect ever more data and regurgitating it. For all the math they do, it is astonishing how little intelligence they have, how little actual smarts are applied."

Silence for a a few mins.

Awkward glances.

Then this: "What do you mean, and what can we do?"

Me: "I wish the tools would use an algorithmic approach to highlight the things an Analyst needs to know, give 'em some starting points. Why make people dig for hours?"

You have to hand it to the team at Google, you "provoke" them and they respond. Google Engineers truly rock!!

Today Last week the Google Analytics team announced a raft of sweet features that take the current functionality in GA, wrap a liquid hydrogen fuel tank on it and shot it into a higher value orbit. Take some time to learn more about how you put more power behind your analysis punch: Google Analytics Now More Powerful, Flexible And Intelligent.

In this post I'll want to share rest of the story, what came of the above provocation.

The first thing you'll notice in Google Analytics is a new cool ability to better identify the "known unknowns", i.e. we know what we want to know, but we don't know if and when it is happening.

The feature is, rather cutely, known as Custom Alerts.

Here's an example. Everyone tells me that Twitter is nothing but hype. But

[sidebar]
i started to write this post in preparation of the GA new features launch, unfortunately the next day i broke my right hand. that meant going to emetrics to do the announcement in a temporary cast, and of course no blog post. i had surgery this past thu. metal plate and some screws in, things will be normal in a few weeks.

i unfortunately still can't type the thoughtful teachable post i had in mind, rather here are two videos that tell you about two features i am really proud of. hope you'll love 'em as well.
[/sidebar]

custom alerts: identifying the known unknowns

video: 8 mins:

intelligence: identifying the unknown unknowns!

video: 16 mins:

love it?

i hope you had fun learning a bit more about these two cool features. promise me you are going to set up two segmented custom alerts today!

let me answer one question that might be top of mind: the features are rolling out to all accounts starting last thu, it'll get to yours any day.

it would be great to hear from you, please share your feedback, suggestions and critique via comments. thanks.

Analytics Becomes Intelligent. Hello Insights! is a post from: Occam's Razor by Avinash Kaushik