Kingish.com It's a way of life

We lovingly craft reports every day. We try to make sense of what they are saying. When we hear nothing we try to bludgeon them, hoping for the best.

My hope in this post is to share some simple tips with you that might make your reports and analysis speak to you a bit more. Suggestions that might increase the probability that you'll bump into things that might be insightful, and communicate data more effectively.

None of them are very hard to do, but I think they make a world of difference.

Excited? Here we go. . .

#1: Go as deep as you can. Then, a little bit more.

Far too often in our daily lives we let our job titles limit how deep we go in our analysis.

For example let's say I work at a delightful car / health / spaceship insurance company. Naturally all of my analysis is focused on the efficiency of the website in moving the Visitors quickly from the landing page to click on that delightful Submit Quote button.

I am focused on what the site does because that is what my job title says: Web Analyst

I am analyzing campaigns (which ones convert better and which worse), I am looking a little bit at the bounce rates, and of course I am totally obsessing about my seven step quote submission funnel (and how to reduce abandonment).

Bottom-line: Quote, quotes, quotes.

And that is fine.

The data is easily available in the web analytics tool so why not.

Here's my advice: You should kick things up a notch. Don't focus just on the quote (the part the site does), include the final conversion to a paying customer (even if that data is offline).

The picture you get from stopping at Quotes might be very different from stopping at Policies Purchased.

Here's what you are focusing on (and it is good):

All my experience in these things suggests that it is dangerous to think that the Conversions column is representative of the final outcome.

Here is what it probably looks like (and this is going from good to great):

See how the ranking changed?

You would make different recommendations right? Would it save your company money? Would it make you refocus your efforts on where improvements are needed?

You betcha!

For straight ecommerce websites the first picture is what you use every day. But for most other types of businesses the final success does not exist in web analytics tool. So what? Get the data out of the crm / erp / "backend" system. . . dump it into excel. . . write a simple formula!

Usually you don't need a complicated multi year data warehousing effort with expensive business intelligence tools to buy. At least for this scenario you just need a column and a short movie data with your online IT person and a longish coffee break with your "backend" IT person to get the right primary keys set up. Then you can bring your sexy back!

Go deep.

You are paid to find real bottom-line impacting insights (remember line of sight to net income?). Do that.

If you are a purely ecommerce business then you can go a bit deeper too. Consider doing quarterly analysis that focuses on calculating customer lifetime value. Up a notch.

If today you are a content site that is only focused on measuring content consumed try to go deeper to understanding CPA of the ads or Visitor Loyalty. Once again going one step deeper, up a notch.

And so on and so forth.

Make it a point to pause every Friday at 0900 hrs. Look at your most important work / report / dashboard. Then ask yourself this: "How can I take my view of the data one step deeper?"

Now figure out how to do that. That'll impress me, your boss and your mom.

#2: Join the PALM club. [PALM: People Against Lonely Metrics]

This rule actually comes from my second book, Web Analytics 2.0. [Page 318, Principles for Becoming an Analysis Ninja, if you have the book already.]

The rationale for this rule, joining the PALM club, is quite simple.

You need a someone in your life. I need someone. Everyone needs someone else. A boy friend. A girl friend. A cat. A "you complete me" person.

So why not your metrics?

We do reports / dashboards like this one all the time:

Ok great.

I know the top referrers sending traffic to my site in a month. Maybe I can appreciate more the power of Twitter or google.co.in or whatever.

You might even impress me next month with a updated version of this where some of these might have shifted a bit up or a bit down.

I might not do anything with the data… but you surely hypnotized me for a few seconds.

This is the problem with lonely metrics.

They don't have any context. They fail to communicate if 841 visits from Twitter were any good. In fact is any of the above good or bad? How do you know?

Why not find a BFF for your lonely metric and present something like this. . . .

Much better right?

I found a "you complete me" for my Visits metric, Bounce Rate.

Now in an instant I can not only see which referrers are big or small, I can see which ones are "good" or "bad".

I could have picked conversion rate as the bff. I could have picked % new visits. I could have picked connection speed or mobile platform or underwear size.

Whatever makes most sense for my business. But putting two minutes of thought into my metric would help make my report a little bit more useful.

Kick it up a notch. Right?

Never ever never never never ever present any metric all by itself.

If you want a cop out then at least trend it over time. If you actually want love then join PALM and don't let your metric be lonely.

Let me close with one of my favorite examples of this rule, this one's to inspire you if you have a pure content (non-ecommerce) website. . . .

Good to know what content's being consumed. Column: Pageviews.

Much much much better to know what the $ index value is for each.

See that crazy blue line that's literally off the chart? You would want to know that about the 1,414 pageviews right?

Now go find your dashboards, your reports, your data pukes (sorry!) and make sure that for every dimension you are not reporting success or failure using just one metric. Join PALM!

[Tip: Not that you are trying to but if you want to impress me but if you are then make sure the second metric you pick is as close to an outcome metric as possible. Or an actual outcome metric. I. Love. Outcomes.]

#3: Measure complete site success. Measure everyone's success.

One of my greatest passions when doing analysis is to look at the complete view of things. Rather than just the obvious.

An application of that passion is to look at all the jobs the website is doing, representing all the work that is being done by people in your company who touch the site.

Ecommerce is too easy an example of this so let me use a non profit example.

San Francisco Aids Foundation is a charity I support. It does incredible work to prevent new HIV infections.

The only way SFAF stays in business is if you and I make donations. As an Analyst I would focus all my energies on trying to figure out how many donations we are getting and where those people come from and what they are doing on the site etc.

But donations is just one measure of success ("macro conversion"). There are other jobs that the site is trying to do, and people who work on those jobs. So why not measure those?

For example. . . .

* SFAF helps prevention through information sharing and providing services. One key way of doing this is providing forms and information as downloads. Example see all the downloads on the Science & Public Policy page. Or the Bulletin of Experimental Treatment for AIDS.

I can track downloads easily (using event tracking or "fake" pageviews) and help quantify those micro conversions.

* There are a ton of micro conversions on the Advocacy Action Center page. Sign ups. Successful searches for elected officials. Tell-a-friend's.

* On the How You Can page, and other places on the site, there are links to other websites. Why not track these through outbound link tracking to see if we are sending people to the right place.

* Oh and of course the important micro conversion of signing up Volunteers!

Measure the above four micro conversions, in addition to the macro conversion of donation, helps give a complete view of success. And what to do better.

Maybe Google is really good at Volunteers and not optimal for attracting people who donate. If you focus only on donations you'll devalue Google. Or maybe facebook is the best source for sharing information (downloads). And more such things.

Not only are you measuring all that matters. . . . you are validating the jobs of people who put together all that content.

Most of the time we don't do this. We, web analysts, just focus on one thing and then we wonder why we don't have the impact we want to, or why everyone does not pay attention to us.

Broaden your view!

If I were analyzing Amazon I would measure sales AND affiliate signups, signups for amazon prime, credit cards, wish lists, "like's" on reviews, self publish inquiries, free downloads….

If I were analyzing L'Oreal Paris it would be sales AND reviews, coupons downloaded, successful completion of "Profile My Skin", videos watched, sign ups for mobile alerts….

In both cases a complete view of the website and success of every person who works on the site.

Ninjas do that. You should too.

#4: Be smart about using time. Move beyond MoM.

This is one of the most common view of data presented in web analysis…

The picture illustrates the performance of a metric over two consecutive months.

This is of course better than just showing data for June.

The problem occurs when you proceed to look at six such graphs on your dashboard and then proceed to use the trends to deliver insights. You are reading too much into the ups and downs, you are inferring things that might not even exist.

Two months do not a trend make. Important lesson.

Not even for the world's most flat line no seasonality business.

So here is a best practice. . . . at least add three months. . . . if the data looks like below you'll think one thing (and every different from above pic)…

But if the data looks like the image below. . . . you'll think something else. . . .

Worry in one case. Jubilation for the temporary awesomeness for May in the other.

The more time you put into this graph (and if you have as much space as above you can easily add at least six months and it will still look pretty) the better.

But if I can only have three I love using current, prior, same month last year.

Better context right? Will take you off on a completely different line of inquiry, all from adding June 2009 to look at June 2010.

If June is the last month of your quarter and you have a cyclical business then maybe you want to compare Apr, May, June 2010 and have the first column be March 2010 because you want to see how the last month of this quarter did vs last month of the last quarter (because Apr and May don't really show if the trend ended as high or low as it should have ended).

So on and so forth.

Remember:

1. Don't look at just one month or just two consecutive months.

2. Understand your business and its cycles of up and down. Use that understanding to pick the right comparative time period / time horizon.

3. If you do present your data as a trend it does not hurt to include some "tribal knowledge" and throw in some annotations! Like this…

Sweet momma that is awesome!

Kick it up a notch, ok?

#5: Present data better, make insights obvious.

There are so many ways to present data that a small section of a blog post is insufficient. And of course there are so many people who are better at this than I am.

Let me just say that the way you present data matters, a lot. I'm not saying you should make it pretty. I could not care less if it is pretty or not. I'm saying present it in a way that the insights you think exist in the data become more obvious.

Here is a "data element", from an actual dashboard, that I really like. It might not be sexy but it is extremely functional and it is super awesome at communicating the smarts of the Analyst.

Three month trend for one very important business metric…

First note that rather than just showing one column for the performance of this metric it shows four. One for each key segment of the customer that the company has.

This would require you to know the business (good thing), know its customers (great thing) and track the segmented data. IE have your act together.

Second note that the data is for three months. You could show more but in this case you don't want to overwhelm the Executive. If you go more months, shrink the segments.

Third, really important, note that the overall goal is clearly indicated in the picture. 80. And to get that number you would have to talk to Finance and Marketing and HiPPO's and get an agreement up front. This is absolutely magnificent, key to you building relationships and finding insights.

The nice thing about our picture above is that the overall metric would get averaged out and show a trend similar those we showed in tip #4 above.

But would it be insightful enough? A single metric trend would hide insights.

In this case it is pretty clear that Blue, Red, Green segments are doing fine. In fact the one that is absolutely most important, Green, we are doing ok.

The stink bomb in the pile is Purple. It has been dragging the overall metric down (and you know that even if the overall metric is not even shown!).

And you know how much gap you need to overcome for each segment, and were to prioritize your work (PURPLE!!).

This is just one tiny, I call it "functional", way of presenting data.

The presentation is ok, could be made more pretty.

What's precious is the process that went into creating the element – talking to leaders, meeting with Finance and Marketing, identifying the key metrics, finalizing customer segments, and establishing goals.

We often don't do all the above work (the things that are mandatory for data driven organizations) and even if we do it we don't show it because we show lame single line graphs.

Don't do that.

Kick it up a notch. You are working very hard at your job, make sure your work shows up and helps identify better insights.

Those were the five simple things you can do every day to make the most of your daily data analysis. They are not very hard to do, and they'll pay outsized dividends.

I am not someone who leaves the good enough alone. No sirree bob!

With love and affection here are 4 more bonus tips on improving your analysis and truly kicking things up a few notches:

#6: Leverage segmentation, daily.

All said and done the number one way to move from being a Reporting Squirrel to an Analysis Ninja is to leverage segmentation. Every tool has on the fly current and historical segmentation feature set. Use it.

I'll honestly not respect anyone is not applying at least some primitive segmentation to their data.

Learn how to:

~ Web Analytics Segmentation: Do Or Die, There Is No Try!
~ Google Analytics Releases Advanced Segmentation: Now Be A Ninja!

#7: Move beyond the top ten rows of data, seriously.

The "head" of your data will sustain finding insights for a month or two. You might even action something. The real gold lies in your ability to analyze tens of thousands of rows of data at one time. It is harder to do, and hence the rewards are bigger and your competitors will eat your dust more.

Learn how to:

~ Analysis Ninjas: Move Beyond The Top Ten. Find Love (/Insights)
~ Make Web Analytics Actionable: Focus On "What's Changed"

#8: Perform "pan-session" analysis, and win big.

One of the absolute criminal behaviors in web analytics (and indeed online marketing) is that we are so obsessed about Visits, and visits based analysis.

Few people sleep with you on the first date. So why is that your mental model?

Every true Analysis Ninja focuses on measuring customer behavior of one person (or in our case Unique Visitor) over the entire span of that person's interaction one our website.

Hence my devotion to measuring Days and Visits to Purchase. Truly analyzing how people buy. Or analyzing Visitor Recency and Visitor Loyalty to understand now just the first Visit (and conversion) but rather subsequent Visits (and conversions).

I tell you this is honestly kicking your web analysis up five notches, not just one.

Learn how to:

~ Measure Latent Conversions & Visitor Behavior
~ Measure Days & Visits to Purchase

#9: Evolve to multichannel analytics, achieve analytics nirvana.

There is perhaps nothing harder and nothing more impactful than getting good at multi-channel analytics.

Measuring the offline impact of your online activities gives your business a view of success that is truly orgasmic. If you get good at measuring the impact on your website of your offline activities (television, catalogs, billboards etc) then you have truly accomplished the rarest of the rate.

Learn how to: Multichannel Analytics:

~ Tracking Online Impact Of Offline Campaigns
~ Tracking Offline Conversions. 7 Best Practices.

Feeling like an Analysis Ninja already?

Of course not, you have to go do all these things! :)

Remember that tips 1 through 5 you should be able to do quite easily, just need to remember them and remember to use them. Tips 6 through 9 take time, they take a lifetime. Remember them, practice when you have time and slowly evolve over time.

Ok?

Good luck.

As usual it's your turn now.

What are your favorite tips for data analysis? When you present data what is the "trick" that you use most often to be awesome? Have you used any of the tips above? Got any favorites? What do you think it takes to morph from a Reporting Squirrel into an Analysis Ninja?

Please share your feedback / critique / tips and all via comments.

Thanks.

5 + 4 Actionable Tips To Kick Web Data Analysis Up A Notch, Or Two is a post from: Occam's Razor by Avinash Kaushik

Stale.

One thing that I never want to be.

We all have a tendency to learn up to a point, we get comfortable and keep chugging along rarely investing in our ongoing education.

I call it the slow but sure path to irrelevancy.

Let me share my prescription for avoiding irrelevancy: Try new things.

Simple right?

At any given time I have six or seven interesting tools running on this website. That's not including others I actively seek out around the web. Most of them are not even related to my current job or problems I know of. And that's on purpose.

I want to constantly be in the know of new and more clever ways of working with data, tools that are often solutions to problems we don't know we have yet or tools that are sometimes seeking problems to solve!!

Irrelevancy is not fun. Stale people are not appealing (just like, as your mom taught you, a week old bread). If there is one thing you take away from it post I hope it is the importance in investing in yourself / your education / your ongoing awesomeness.

In this blog post I want to share four analytics tools that I have been playing with for a while… tools that solve an interesting problem… tools that point to what might be in terms of our near term analytical future… and in almost all cases they don't even know!

I love doing this, I hope you'll have as much fun as I do.

First Some Context.

Remember I am the creator of the 10/90 rule of investment in web analytics.

I had created the rule many years ago, early into my job at Intuit, and quite simply it states:

If you have a budget of $100 to make smarter decisions on the web…. invest $10 in tools + vendor contracts and invest $90 in people (big human brains inside or outside the company to do analysis and the process of producing insights).

When I had created the rule Google Analytics did not even exist!

The rule was borne out from my own experience having inherited a world class tool we were paying $250k a year for and produced crap. Well not crap… lots of data that no one cared about or actioned. I threw out the world class tool, purchased ClickTracks for a fraction of the cost and put money into Analysts and boom!

Ok not boom overnight… but over the course of a few months the org started to be more data driven, because analysts we hired produced analysis. That fed a virtuous cycle. More analysts. More insights. More desire to be data driven.

So as you look at the tools below remember the 10/90 rule.

In the end it does not matter who has the coolest or the biggest tool. Or for that matter how many tools.

People matter.

You matter.

Remember that, at least for the rest of this post. Ok?

Let's go look at some tools…

Measuring "Invisible Virality": Tynt.

Tynt's promise is simple. Add a piece of javascript to your web page (do a View Source on this page to see it), and it will tell you how often your content is being copied.

Copied! Say it ain't so! :)

[Please click on the above image for a higher resolution version, including all the other metrics.]

In the last month data was copied off one of my posts 5,616 times, with most of it being content and some of it images.

But that's not all.

If you look at the higher resolution version (click above) you'll see it also reports other data like Visits Generated etc.

The way it works is that when someone copies a piece of content Tynt adds a little bit of additional text and a trackable code with a hash (#) at the end of the url from where content was copied.

Like so… the text that was copied from my blog is the first two lines… the Read More and link was added automatically by Tynt…

When people click on that link Tynt can report visits generated, page views, where the links were posted (in case there is a referrer) etc.

There is additional data like how many of your copies created links that were posted and then clicked on…

Gold are places were the copied text was pasted with the additional "Read more: http://…" text+link were also posted AND someone clicked on it.

You'll note that Tynt's selling point is connected to SEO. The idea that your copied text creates links back to you which in turn creates visits back to you, and per Tynt, better SEO goodness. You know links and page rank and what not!

I *personally* do not see much value in all that data. Two reasons:

1. Most likely the additional text+link will be posted as is only by someone who is quite careless and perhaps only on the least desirable sites. I mean if someone smart's going to copy they'll be clever enough to get rid of the link+text. :)

2. Search engines are complicated little beings. The days of just inbound links counting towards SEO goodness are long behind us.

So I am less enamored by Tynt data that focuses on all that.

I love the data you saw in the very first screenshot, and I absolutely love this…

[Please click on the above image for a higher resolution version, including all the other metrics.]

The first screenshot shows how often content is being copied and the above indicates the blog post / web page where the content is being copied from.

Why is this cool?

If you are a regular reader you'll notice that at the end of every blog post (before the start of the comments section) is a Topsy widget.

It measures how often a blog post is tweeted/retweeted. Goes viral. Higher the number the better, makes sense?

I also measure the # of Comments Per Post as a measure of how "engaging" / "valuable" people found the content to be. Looking at how often it was tweeted/retweeted is one more layer of information in understanding what subject / ideas in a post / things I write are well received by people and which are not.

But.

Both the above attempts measure two minorities.

1. The rarest of the rare who post a comment.

Context: I write twice a month. This blog has around 70k Visits a month, 39k Feed Subscribers and the average number of comments on each blog post is just 35. Minority perspective right?

2. The rarest of the rarest of the rare who are on social media. Who tweets after all. :)

The cool thing about Tynt is that it allows me to get some sense of "engagement" / "perceived value" / "Like" with the v a s t majority of people who will neither submit a comment nor write a tweet.

People who still use email. People who like something I wrote so much (or hate it so much) that they copy the text and paste it and forward it to others. Or copy the text and post it on their blogs (without attribution of course :)).

I like that a lot.

This entire interaction that was completely invisible to me is now a bit more visible. I can measure the "invisible virality" / "spread" by this big huge non-commenting, non-tweeting audience.

In the time period above I had written 4 posts (5,616 times copies). Check this out… It turns out the post with the fewest comments, just 25, and the fewest tweets, just 100…

…was copied an astonishing 506 times, when all other posts were copied in small double digits.

See what I mean… something I would have perhaps considered to be only a small success turns out was a huge hit with the blog's audience. I just would not have known that so far.

Here's another interesting application. . . Lots of people are measuring "influence" of a blogger (/ piece of content) using data from the "minority activity" (comments, retweets etc) and selling it as the complete truth. But how can you do that without some insight from the majority?

Tynt shares one very interesting piece to the puzzle that perhaps in the future fit some place where we can use it with all other context we have.

Invisible Virality. Cool right?

Applying Smarter Ideas to Measuring "Sentiment": Analyze Words.

Raise you hand if you are in the "If I am any more excited about doing sentiment analysis then I'll pee in my pants".

So many raised hands!

Here's the problem: Most solutions stink. Not just stink… dinosaur's breath after a meal stink.

We are algorithmically trying something that as yet does not lend itself to algorithmic measurement… "emotion". It is darn near impossible to cleanly buckets feelings and nuance into clean Positive, Negative, Neutral buckets.

We, computer programs, are simply not there yet. [Though I am absolutely confident that we will get there at some point.]

For now you are most likely wasting time (and money). Sorry.

Here's the other problem…

Even if it works… I don't think it works. [What!]

Let's say you have a 100% perfect human read and 100% human categorized analysis on hundreds of thousands of rows of text. Clean into the three desired categories. Like in the image above.

Now pause for a second and think… what could you do with this?

You have aggregated data into three pieces and we all know aggregated data stinks at delivering insights!

That does not mean wanting to identify insights from lots and lots of text is not prudent. It is.

I like a much more nuanced approach.

Analyze Words applies one such nuanced approach to text analysis.

It uses the well established and long use LIWC (Linguistic Inquiry and Word Count) methodology to categorize all your delightful text (in this case your tweets).

Why the LIWC? Here's the idea behind the LIWC:

"The ways that individuals talk and write provide windows into their emotional and cognitive worlds."

Cool right?

You go to Analyze Words and you punch in your twitter id and bam (!) your "psychological" profile, or in this case mine…

Nice eh?

No simplified over promise under deliver aggregates!

The three categories and 11 sub categories provide much much much more nuanced understanding of what your text is saying, in this case for your twitter profile.

Why is this cool?

In this case measuring "Personable": Engaged in other people's well-being and at peace with expressing your own uncertainty about the world. High Scores in personable use positive emotion words, ask questions, express their own ambivalence and reference others frequently.

Better than positive, negative, neutral right?

Or "Analytic": "If law school exams were a persona, they would rank real high in this category. Ample large words and phrases that include complex thinking styles (e.g. "if – but not …")."

Love it!

Two magnificent things about this approach (remember it's not the tool, its what you do with it :))…

1. It is very sophisticated in the approach it is applying. Nuance and segmentation rule the day. There is nothing, nothing, more sexy in the world of web analytics.

2. It is immensely actionable. You can quickly see areas where you are scoring well, where you are not and you can start to take action to fix things!

Of course you can do even more.

You know how you are doing… now compare it to your "competition" and find their strengths and weaknesses…

When you do competitive analysis, like above, find contrasts with your own profile, what your brand stands for in the world and their brand stands for.

Highlight differences where you brand strength is strong. Hopefully they'll discover where they stink and for the sake of humanity fix that.

Nice eh?

Analyze Words provides a glimpse of an approach that I hope others follow.

Rather than trying to find short cuts, where none exist, and provide aggregate data, where it just gets crapified, follow a well established methodology while leveraging segmentation and nuance.

We've applied it just for Twitter in the above case but you can easily see how you could apply it to call center data, tech support websites, forums, online survey open text voc answers and so much more.

Applying Simpler Ideas to Measuring "Sentiment": StatsIt.

StatsIt started off as a differentiated web analytics tool, but has morphed into a delightful social media monitoring tool.

It's approach is to index blogs and tweets and delicious and twitter and youtube and on and on and analyze that data to find yummy actionable insights about your social media presence / activity.

Like all tools it gives you pretty charts…

Sweet, now you know how much "activity" is happening. Give it to your boss, she'll be impressed. You on the other hand realize "activity" rarely has insights.

I want to focus on just one part of StatsIt that I adore because of how simple it is in its brilliance when it comes to finding insights from lots of text.

StatsIt has indexed a ton of content from all the social web activity. When you tell it your brand terms (or just your brand name, in my case "avinash kaushik") and it churns through that social web data to provide you with something awesome…. a tag cloud!

[Click on the image for a higher resolution version, along with a peek at other metrics.]

Why is this cool?

Mikko and his team have taken 1,000 words from the English language that are connected to emotion. Good emotion, bad emotion, ugly emotion.

They look at their social web data and in that they look at the words around your brand mention and finally identify the emotional words people are using in context of… you!

The tag cloud above shows the emotional words use around mentions of me for a month's worth of time.

Without having to read all the text I can at a glance now get a really good understanding of the tone and texture of activity around my presence. More importantly it does not take all that long to figure out what emotions should be there but aren't.

A very simple, effective and elegant solution to a complicated problem.

Oh and guess that happens when you click on one of the words in the tag cloud?

You are right… it takes you directly to the text from all the data that StatsIt has collected!

By clicking on the words you are essentially segmenting your data and drilling down to the text (tweets, blog posts) where you can learn more about what the person was saying when they express, say, "great" as an emotion. :)

Effective "sentiment analysis" baby!

Why can't we be this simple in other places?

We are always seeking complexity. Here are two ideas that popped into my head from the StatsIt's approach that might apply in other places.

We collect lots of open text from our online surveys right?

Rather than finding the perfect answer to what's expressed in the text, and of course getting it wrong, why don't the vendors show us a emotional tag cloud?

Can there be a better / easier / faster way to allow us to make sense of all that text, leverage as a segmentation tool and find insights every day?

Vendors! Come on!!

Another idea.

Reviews are important. Most ecommerce sites have them.

But why is it that we only see "quantitative" analysis of the reviews? 5 stars. 3.2 moons. 61% rotten tomatoes. Etc etc.

The richness of the review is only partly in the quantitative analysis of the rating. The real sweet nectar is in the words people write in reviews.

I recently gave a talk at eBay. So let's use that as an example.

You get quick quant rating on eBay that you typically use. But perhaps the real gold is here….

This seller, me, is 100% positively rated.

Not let's say that you want to buy a Sony digital camera that is listed by both me and Emer. We both have 100% positive ratings for our 60 or so prior eBay auctions.

How can you best decide if you should buy from me or Emer? You can't possibly read 120 reviews, or even scan them quickly.

Now would your life be much much easier if eBay choose to provide an "emotional tag cloud" for both Emer and Avinash?

Very quickly you could see that while we both have same quant ratings it turns out that my emotional cloud shows a neutral to positive feelings expressed while Emer's is outrageously positive.

Now is it easier to decide who to buy from?

As our dear friend Sarah Palin would say: You betcha!

So why does eBay not provide this simple emotional tag cloud?

Or for that matter Trip Advisor or Amazon or any site that hosts reviews and ratings?

Simplicity rocks. Especially when it's actionable.

Quick, Efficient, Effective Mobile Analytics: Percent Mobile.

It is always a really good idea in web analytics to understand how data is captured (case in point the delightful blog post on Competitive Intelligence data capture).

No where is this more true than when it comes to mobile analytics.

There are many methods of collecting data depending on the platform you are on, and if Steve Jobs gets upset he can totally shut you down with a mere update of his TOS! :)

I am not going to cover all that here today. For those of you who already have my second book Web Analytics 2.0 please jump to Page 250 to learn all about data collection options, platform limitations, challenges with campaign analysis and finally reports and KPI's you should measure for mobile.

In this blog post I want to share a lightweight wonderful mobile analytics platform called Percent Mobile.

Now most web analytics tools, like Google Analytics and WebTrends and others, will capture and report data for javascript enabled smart phones (like the iPhone, Android and some Nokia phones). Honestly that is all the traffic that is of commercial value, so even if you miss the rest it is not the hugest of deals.

But all these "big boys" have simply "added on" mobile analytics to their tools. The result is that they suffer from both a lack of imagination and, this is important, truly great databases when it comes to devices and carriers and other unique mobile information.

Not Percent Mobile.

They have two incredible benefits:

1. A really expansive and accurate database and detection mechanism when it comes to mobile platforms.

2. A really simple UI and reporting layer, even your mom will understand the data.

They also have four different methods of enabling data collection, I am using their standard javascript tag on this blog (do a View Source).

Here is what the resulting data looks like…

[Please click on the above image for a higher resolution version.]

No hunting and pecking to find the data, like you would in Google Analytics or Site Catalyst or CoreMetrics. A quick at a glance view of how much traffic is mobile, key stats about the devices, the devices themselves (go iPad!!), vendors and operating systems.

If you compare this to your web analytics tool you'll notice almost immediately how much better this data is compared to what the "big boys" are reporting.

Click on the image above and you'll see a bit more clearly other really sweet metrics. % of mobile devices accessing your site via WiFi. Phones with touch screens and full keyboards etc.

[Can you imagine how cool it would be to segment your mobile traffic for full keyboard phone vs none and see which convert better. Or does access via WiFi mean more content consumption than via 3G? Etc. So cool.]

That is not all… if you scroll a bit more you can get a country map view, the networks used to access your site (AT&T still #1 for me!) and countries etc.

Of course it would be hard for me to like any tool that does not allow segmentation. :) You simply drag and drop on to the box on top..

And what would an analytics tool be without the normal search, referrer and all that data we have so come to love (and hate!).

I particularly like the "Activity Types" box at the bottom left, I don't know why web analytics tools don't categorize referrers by default.

I am also surprised at the long tail of referrers. Yes Google is big but there are 91 other referrers for this segment. More mobile SEO!

Why is this cool?

It might seem odd that I would like a tool that would give me similar data that I can get out of WebTrends or Omniture or Xiti or whatever.

The first reason is that, as mentioned above, the data is actually much better because of the databases that power Percent Mobile.

The other thing is that getting this data causes less pain than pulling my two front teeth.

Finally I so do like supporting pretty tools, especially if they have good data!

The one thing Percent Mobile lacks is some way of measuring any outcomes. I can certainly dig to my "conversion pages" but it would be great if they just let me just input them into the tool and then they could measure outcomes for me (even if it is like the Goals process in GA).

But if you want a light weight easy to use free mobile analytics tool just throw Percent Mobile on your site and have fun. Go to www.percentmobile.com , click Sign Up (top right) and use the Invitation Code "Avinash" (no quotes).

Mobile rocks no?

Summary Of Our Lovely "Let's Keep Learning" Cruise.

It is important to point out that I am not affiliated in any way with any of these tools / companies. I am also not recommending overtly or covertly that you buy / use them. That is totally your call.

Of course I would not personally use them or write about them if I did not thing they had value. :)

My sincere hope is that you'll internalize:

1. How important your ongoing education is. DBS: Don't be stale!

2. What it is that each tool does that is so unique, what unique problem each solves.

3. Why it is important that you can separate the wheat from the chaff, notice how I quickly put aside most data from Tynt to focus on just what was important to me.

4. Where are new places in your business you can apply things you learn from analytics, like in my example of emotional tag clouds for Ebay or Amazon.

5. Why simple and effective is better than expensive and complicated (even if "perfect").

I hope you got that, more than names of interesting tools.

I cannot tell you how much fun it is to step outside the world of Omniture and Google Analytics and other traditional web analytics tools. It stretches your mind and sometimes you look at these new techniques and data and you notice you are smiling and feel so happy.

Try it, and have fun.

[In case you were curious at the moment I am playing with these incredibly cool tools: PostRank, Next Stage Sentiment Analysis, SEO Effect, and Colligent. Each in its own way does something magical and quite unlike anyone else.]

Ok your turn now.

What do you think of the work that Tynt, Analyze Words, StatsIt & Percent Mobile do? Have you tried any of 'em? What obvious flaws did I overlook? Are there other tools you are using in the Viral, Social, Sentiment, Mobile space that you really love? If so would you please post them in comments?

Please share your feedback / critique / ideas.

Thanks.

PS:
Couple other related posts you might find interesting:

• Multiplicity: Succeed Awesomely At Web Analytics 2.0!
• Actively Avoid Insights: 4 Useful KPI Measurement Techniques
• Web Analytics Tool Selection: Three Questions to ask Yourself
• Kill Useless Web Metrics: Apply The "Three Layers Of So What" Test
• Web Analytics Career Advice: Play In The Real World!

Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions is a post from: Occam's Razor by Avinash Kaushik

We have more web metrics and data than there are stars in the universe (slight exaggeration!).

Yet we stink at informing decisions. Our reports are ignored. Sites & online marketing continue to suck.

A large part of the reason is that a large part of our job seems to consist of glorified data puking, hoping someone will be impressed. After all there is so much data in those reports!! #fail

This blog post encourages you see the forest, the much hyped big picture, and shares a framework that will help you ensure that every single moment of your day is spent on activity that will be:

1. of value to your organization, hence appreciated and acted upon

2. has a clear line of sight to the one thing that matters: profit

If you don't want your professional life to be frittered away then please come along this short journey.

First some context…

If you have seen one of my keynotes recently then you have heard my near evangelical fervor when it comes to trying to convince you to compute Economic Value.

If you have Web Analytics 2.0 then you already know who much attention is paid to this concept in the book (jump to page 159 for how to compute it for your website).

The reason for this emphasis is to help fix our miserable failure at at creating data driven organizations.

To steal your energy away from being just in the report / data production business.

To encourage you to do better than spend a lifetime implementing analytics tools, building data warehouses, chasing the next shiny object.

My recommendation has been:

1. Identify your Macro Conversion (focus on this a lot!).

2. Report revenue. Report like crazy on the 2% conversion rate.

3. Identify your Micro Conversions.

4. Compute the Economic Value (see page 159). Show your bosses and HiPPO's the complete value of your website.

That last one will get any organization to sit up and pay attention.

Why?

Because for the first time in their young and passionate life they'll see the complete value your website is adding to the business. And because my dear it will be a huge number that no one can ignore! You are going to tie your work to the bottom line!

Revenue = Good. Economic Value = God! [Also slight exaggeration :)]

Professor Ken Wong's Magic Potion

Prof. Wong is the award winning Commerce '77 Teaching Fellow in Marketing at Queen's School of Business (and an awesome speaker, you should hire him for your next event!).

He took the stage after my talk and said, I am paraphrasing here, "Avinash did not go far enough in his keynote. Economic value is important but the only thing that matters is Profit!"

That was awesome!

One of Prof. Wong's key points was how the success of our work, as Marketers, is measured based on a lot of things but not often enough based on perhaps the most important metric of them all: Net Income.

Prof. Wong covered a lot of key points (as a MBA with a minor in Marketing I wanted to take off my clothes and jump for joy when he said the 4P's of Marketing are killing Marketing!).

I wanted to share two of his slides that left a lasting impression on me.

They are particularly applicable in the web analytics context. In sharing my interpretation of them my hope is it will change a little bit how you think about your work and success.

The very first slide, "Profit: The Ultimate Client Need", shares the key elements that need to function for the outcome (ROI) that causes companies to remain in business.

My interpretative points.

Net Income is driven by two important variables:

Unit Margins (how much you make on each X you sell or Y service you provide)

Unit Volumes (how many of X or Y you sell)

Margin times Volume gives you the golden metric Net Income!

[Keep this formula in mind, your life should be revolving around it else you are wasting everyone's time.]

Peel the onion back one more.

Unit Margins is in turn driven by two more variables:

Price (how much you charge for X product or Y service)

Cost (how much it costs you to make X or provide Y)

Price minus Cost equals Unit Margins.

Get it?

So if you want to have very high Margins you have two variables you can control. You can charge lots for your product or service (think of a Vertu phone).

You can also make it at the cheapest possible cost (no phone costs $100k, you make it for $300 and sell it for $100k).

You can of course also charge lots and lots and it costs you a lot to produce (think of a Tesla car). But give some thought to how you'll stay in business.

Continuing the onion peeling…

Unit Volumes, our other variable to have high Net Income, is driven by two variables:

Market Share (is your share 90% or 5%?)

Market Size (is that share of a market the size of Maldives or China?)

Both share and size are important.

You'll sell lots of X or Y if you have a high market share and the limit you'll hit is the size of the market (you can then play in the current size or grow the pie).

Line of Sight.

Having a clear line of sight means that you are able to map every metric you report on (or better still torture with segmented analysis to find insights) every single day directly to the strategic objective of the company.

Prof. Wong is suggesting, rightly so, that that strategic objective is Net Income.

And you have only one of four things that you'll move through actions your company takes: Price. Cost. Market Share. Market Size.

Here's my crystallizing question for you. . . .

When you report the metric Page Views Per Visit which of the four are you solving for?

How about with Bounce Rate? Or Time on Site? Or % of New Visits? Or Visitor Loyalty? Or…..

Is there a direct line of sight between what you as a Marketer are being incented on, or you as an Analyst are spending time analyzing?

If not, are you surprised that no one loves you? Sorry… I mean… no one loves your work?

Here is a simple exercise you could go through: Pick out all the metrics you are reporting today (on your dashboards and top reports). Try to put them into one of the four important buckets from Prof. Wong's slide.

The clear line of sight exercise. . . .

Were you able to cleanly bucket all metrics you currently report? Time on Site and Conversion Rate and Task Completion Rate and % Internal Site Search Exits and Cart Abandonment Rate and % of the Page Scrolled and % of Visitors Refreshing Pages and all the other sweet things.

Some of the metrics in the above paragraph are complete crap, you are wasting your time and everyone else's time with them. And you'll now discover that very quickly because you won't have a place where you can bucket them.

Other metrics will make you think harder. Where do you bucket Conversion Rate? Are you impacting Price or Cost?

What about Customer Satisfaction? Or Page Rank!

Not every metric will map cleanly, and that is ok. I had to think really really hard to bucket each of my metric in the above picture. Some of the metrics were controversial. But bucket I did.

If it turns out your web metric has no line of site then it might be time to kill.

If the work you do can't be mapped into Price, Cost, Market Share or Market Size then why are you doing it?

Before you dip your hands into Omniture or WebTrends or Surfaid, :), answer that question.

I know it seems like a lot of work for a "lowly" Analyst to do. It is. But without it there is little hope for your personal success (promotions / bonuses) or your company's success (higher Net Income).

"What Matters Most" Fishbone Analysis

As you look at the picture above it is amply clear that the metrics I have chosen in each of the four buckets are perhaps unique to me/my business.

The reason is simple… they are a reflection of the strategy my company is currently executing, i.e. our "world domination via an effective data driven online marketing plan".

This simple truth, that metrics should reflect current business strategy, is the reason I loved another slide from Prof. Wong's presentation.

It leveraged the same framework, but added "what matters most". . .

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

The focus is still on Net Income driven by, hopefully, improved Margins and Volume which in turn are driven by much beloved 4 levers of Price, Cost, Share and Size.

What is awesome about the "fish bone" above is that it drills down to the 14 specific strategies that most businesses will use to become great (or simply survive).

You Ms. Web Analyst now have a framework you can take to your Marketing Directors and CMO's to discuss which of the 14 strategies they are currently executing to drive the 4 beloved levers.

Ask any Web Analytics "Guru" or "Professional Speaker" or "I am so important you are paying me $5,000 an hour to give you generic advice Consultant" and they will always tell you that all good journeys in web analytics start with asking your bosses this question: What are the goals of the organization?

The advice is sound (and well worth $5k/hr). The problem is that we never get an answer from the customers of our data / our management. You are $5k x 8 hrs short and still none the wiser.

Get off the slow train to nowhere…. You now have a new BFF: Prof. Wong's "What Matters Most" slide!

Don't ask the generic "What are the goals" question. Ask "Of these 14 specific strategies which are we currently executing".

Once they tell you which ones (be patient, it might shock them that you are giving them something tough and specific to think about), you'll be in business.

The 5 strategies they pick from the right-most column will help guide you in terms of picking the right Key Performance Indicators / Web Success Metrics for your business.

And you know why a win now is guaranteed?

Because each metric you identify starts with a specific business strategy which has a direct line of sight to the 4 beloved levers which will have a impact on Net Income!!!

Minorly orgasmic right? [Trust me, you do this and you'll agree. :)]

Summary:

Recommendation #1: The Web Analytics Maturity Mandate!

For far too long we have been like toddlers… bumping into things, having a limited vision, working just what we know (which is little).

What I love about this approach is that it forces us to grow up. It forces us to understand what we are solving for: Net Income. It forces us to have a line of sight between our work and the ultimate goal: Net Income. It forces us to not live in our dungeon but rather take a well defined framework to enable the discussion that will yield wins all around.

No lip service to how important process is. This blog post shares what you specifically must do to succeed!

Recommendation #2: Win With Web Metrics: Steps

Here are the specific steps I recommend you follow for optimal execution of the recommendations.

Step 1: Learn Finance 101 and the terms outlined in the slide titled "Profit The Ultimate Client Need".

Step 2: Don't pick any metrics, don't run reports, resist the charms of Google Analytics, Omniture Discover2 etc.

Step 3: Meet with your Management team (or the senior most Marketing person) and identify which strategies outlined in "What Matter's Most" the company is executing (/wants to execute).

Step 4: For each strategy identified in step 3 identify the Web Metrics / KPI's with a clear line of sight to the 4 beloved levers.

Step 5: Use the Web Analytics Measurement Framework as the foundation of all your reporting.

Step 6: Spend you work day on focused segmented analysis to identify actionable insights you can report using the Web Analytics Measurement Framework that will help drive data driven actions on "What Matters Most" so that your company will improve in the one thing that matters: Net Income.

Step 7: The happiness you'll get from leading a meaningful professional life will make you irresistible to the opposite sex which in turn will lead to happiness in your personal life! Enjoy it.

A simple but effective 7 step process.

:)

Good luck.

Ok now it's your turn.

Do you agree that a focus on Net Income and a focus on "what matters most" is key to success in web analytics? Can Web Analytics tie the work they do, the metrics they report, into Price, Volume, Market Share & Market Size? Or is our work simply not that important? In your job today how do you ensure line of site? Will you change anything based on the recommendations from Prof. Wong?

Please share your feedback / critique / ideas.

Thanks.

[UPDATE]

Zach Olsen, who blogs at By Data Be Driven, has taken the Clear Line of Sight framework outlined in this post and applied it to a medium sized eCommerce website. It is so wonderful, take a look:

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

Zach's effort is awesome for these key reasons:

• Really clear line of sight from Business Objective to Net Income.

• Clean flow from What Matters Most to 4 beloved levers (Price, Cost, Share, Size).

• (This one I love the most…) Identifying of Targets for each metric! You can't be serious about Web Analytics without doing this!

I hope you are as impressed by Zach's effort as I was.

He has also done something sweet for all of us… he has created a excel spreadsheet that you can download and customize for yourself, and hence get a jumpstart! You can download it at this blog, bottom of this post: Web Analytics Framework Example. Please download it!

My thanks to Zach for his effort and for his permission to share it here.

[/UPDATE]

PS:
Couple other related posts you might find interesting:

• Six Web Metrics / Key Performance Indicators To Die For
• Analyze This: 5 Rules For Awesome Impromptu Web Analysis
• Actively Avoid Insights: 4 Useful KPI Measurement Techniques
• The "Action Dashboard" (An Alternative To Crappy Dashboards)
• Barriers To An Effective Web Measurement Strategy [+ Solutions!]

Win With Web Metrics: Ensure A Clear Line Of Sight To Net Income! is a post from: Occam's Razor by Avinash Kaushik

Most of the time spent by Marketers & Analysts tends to be spend looking for "known knowns".

Things we know and expect to see in the data, we look to see if they are there. "Oh look Google is still our Number 1 referrer and we are selling lots of product x as we always do. Yea!"

Some of our time is spent reacting to the "known unknowns". Looking for things we know might be happening but don't know when they happen. "I would like to know when conversion rate dips below q%, let me go see if that happened last week."

None of it is spent looking for the "unknown unknowns"…. mostly because it is a hard problem to solve. But one that is important for Omniture and WebTrends and Coremetrics and other tools to solve. "I did not even know 20% of our customers were from Australia and that 9 days ago they all stopped coming to our site."

[For one approach to solving the unknown unknowns problem, and source of this framework, please see the second video in this blog post: Analytics Becomes Intelligent. Hello Insights!]

I believe that actions taken based on web analytics data dramatically increase when we shift from our obsession with the known knows to the known unknowns.

From: "Oh my God I did not know that metric had crashed for that segment!! If only I had known that I would have taken action sooner."

To: "Thank goodness I had an alert in my inbox about that big drop yesterday, I'm off to fix landing pages for that segment. No I can't talk to you about Desperate Housewives, I have to go take action!"

And you know what? That is easier to accomplish than you might think.

All you have to do is use the built in Custom Alerts feature in your web analytics tool (and every single tool worth its salt now has one, so you have no excuse not to use it!).

How does it work?

You want to know when something of value happened. But you don't want to hunt and peck at data. You want to be poked with a stick that it happened. You need. . . .

Being told when to look at important things you can take action on, sounds magical and revolutionary? It is. :)

In this blog post I want to share some alerts with you with the hope that it'll spark your creativity.

I also want to hear from those of you who have already use this feature. What is your favorite alert in Omniture? What is the one alert that you created in WebTrends that saved your job? What is the first alert you create for a client, and why?

But before we go jump into the alerts pool naked and all excited…

A Prerequisite:

There is one important reason custom alerts are not used more, or when used they provide little value: A lack of focus on the important.

Many of us toiling away in the field on the front line are just tasked with producing "numbers", or fulfilling certain contractual reports production expectation.

So the alerts we end up creating might be on random things, guesses, what we feel might be important or, again, random things. If you triggers alerts based on that you shouldn't be surprised no action gets taken.

Worse to impress our bosses we might spam everyone with alerts and it takes only a few days for people to configure their email filters to send all your alerts directly into spam.

Please do not underestimate how horrible this problem is.

So what's the fix?

You want the known unknowns right? Ask people around you what they want to know that is important to the business, but currently unknown.

You are asking what the business objectives are, you are asking for the goals, you are asking about targets.

In short you need to leverage the Web Analytics Measurement Framework. . .

See how important alerts to identify the known unknows just pop out at you right away?

If you don't put in the effort, as a in-house employee or as a outside Consultant, to go through the process of working out the Web Analytics Measurement Framework you will fail at this.

Spend time with your HiPPO's and Clients. Spend time with the Marketers. Spend time with people who have the power to take action. Ask all these people what's important but they don't know.

That'll give your effort the focus that will guarantee action.

You skip the above process and all you are doing is self foreplay that will yield nothing (except frustration).

A Helpful Tip For Increased Success:

In championing a rethink of how we all approach our segmentation strategy in our web analytics tools I had recommended a Web Analytics Segmentation Selector Framework.

It advocated identifying actionable insights by focusing on three key activities:

1. Acquisition 2. Behavior 3. Outcomes!

Do the same thing with your custom alerts.

Rather than creating all kinds of alerts, they are easy to create, go through the exercise recommended in the segmentation post and focus your energy on the 1. the top priorities and 2. things decision makers might action.

In web analytics it is never ok to not focus on the most important. It is especially criminal behavior if that waste of time and life is cause by you firing off "alerts".

Remember the tale about the boy who cried wolf? Don't be that.

Creating Custom Alerts:

You have your objectives, goals and targets squared away. You are not going to boil the ocean, you are going to focus on identifying the known unknowns in 3 key buckets, for things people care about.

Now, finally (!), it's time to get down to business!

It is not very difficult to create custom alerts. If you use Google Analytics in the left navigation click on Intelligence, then click on the link that says Create new alert. If you are using Site Catalyst or Yahoo! Web Analytics etc please check your user manual.

Let me walk you through a simple one.

You've convinced the HiPPO's that Twitter is where it is. Their response: "Meh!" But you have permission to tweet a storm away, but not during work hours. So you set out to do this as a hobby, but you know you are right, and while you don't want to spend looking at every twitter visit, you want to be alerted when twitter revenue shoots up!

Step one is to choose your primary alert settings. . . .

Give your alert a name. In this case High Twitter Revenue (since you are already adding campaign tracking parameters) to your tweet urls.

With Google Analytics you can apply this to one of your websites or all of 'em or just to a selected few. Quite convenient.

Choose the period for which the data will be analyzed. In this case you want to know the moment glory is achieved. You can also choose Week or Month.

Finally choose (with the check box) if you want to be emailed or for the alert to just be noted in analytics.

So far easy right?

Step two is choosing the sweet settings. . . .

You choose the dimension you are interested in. There are a bunch to choose from. New vs. returning visitors, countries, campaigns, a particular page someone came from or a page someone landed on your site etc. Depending on the tool you use you might have fewer or more options.

I choose Source and the Value I use is twitter.com.

Note the Condition in the middle. Quite important. You can choose Matches exactly or does not contain or ends with or whatever. This one box can be your shining moment or the start of your embarrassment, choose carefully.

Now for the last step. . . .

Choose the metric you want to focus on.

If this is your first alert, or the first few, try as hard as you can to focus on activity #3, Outcomes. That is what people care about the most. Try to resist, for now, the temptation to alert based on visits or time on site or % of new visits. They are nice and all but really…. no. :)

I choose the metric I like as an outcome on my blog (remember a non-ecommerce website!): Per Visit Goal Value.

Now the KEY PART!

For my value I choose 2. There is a lot of thinking behind that.

Not only do I want to prove Twitter brings in revenue, that would be easy. I want to prove that my efforts with Twitter are so magnificent that they will knock your pants off.

So I don't just have a alert set up, it is set up to cross a high bar. My average Per Visit Goal Value is $1.14. My alert is set to be triggered at $2.

You don't win people over by just meeting some averages, you win them by being big and brave. Keep that in mind when you create alerts.

Ok lecture over and as it turns out I am done with my first alert!

Click Save Alert, do a little jiggy, wait for glory.

When it comes, when you've cleared the high bar, it will look like this:

If you did not opt for your email to be sent in then it will look something like this in your web analytics reports:

Now you know when an unknown that you might not specifically be looking for has occurred and you can, as the email says above, partake in "happy analyzing"!

[Note: If you use Google Analytics make sure you use Annotations to add a quick note with your victories directly on the graph. These Annotations can be shared with others and now when they login they'll also say: "Ohhh that Jennifer is so smart, getting us so many wins, we need to promote her!" Video: Analytics Annotations.]

Ideas For Cool Custom Alerts:

The important word in "custom alerts" is the word custom. As in what you will end up creating will be custom to your business, based on what's important to you.

But I want to close this post with some ideas for alerts I have created recently. My hope is simply to spark your creativity as you use this cool feature.

#1: "Head" Keyword by Bounce Rate.

The "head" of your search terms consists of a few keywords that bring in very large amounts of traffic. A very prudent alert is one that keeps an eye on any ups or downs of these ten or so keywords.

I have set the bounce rate around 10% higher than what it actually is because every little increase in this bounce rate is bad for me, and I want to know that.

If you are running very specific search campaigns whose goal is to attract lots of new visits, then set up a alert for that.

If you, God forbid, are trying to get more page views for people who come from Bing, then set up an alert for that. [Note: The god forbid is for the metric not for Bing!]

Focus: Acquisition. Success: Initial goal met or not.

#2: Campaign by "Things of Real Value".

These are my favorite kinds of alerts.

Far too often we are obsessed with conversion rates in an eCommerce context. Why on focus on things that actually matter, things that might indicate real success or failure?

Like Average Order Value. Or Quantity (of items)?

Here's an alert I create, all the time, to set a higher bar of accountability for my campaigns (especially when I have a lot of people / resources dedicated to them):

Tell me when some email campaigns I am running cause an unusual spike in the number of items ordered. I want to know what I am doing right there.

In this case I am focusing on one specific campaign, you could focus on all your email campaigns to allow you to identify the diamond in the rough quickly.

#3: New Visitor by Revenue (Increase).

Making money from our existing customers is important, but getting better at convincing new customers to do business with us is important as well (especially in the context of the fact that we shamefully ignore all our existing customers and focus all the time on getting new ones!).

I like an alert like this one:

Tell me when I have an amazing increase in my daily revenue (not conversion!) from New Visitors when compared to same day in the previous week.

I have set a high enough bar for revenue, a 20% increase, before I am distracted by an email. Note also I have been careful to compare like week days, I don't really want to compare Sundays to Saturdays (for obvious reasons).

As soon as I get the alert I go look at an advanced segment I have already created for New Visitors to dive deeper into the sources (campaigns, direct, search) that might have seen this revenue spurt, the pages or products on my site that are doing well. All to learn what I should do more of.

Of if you apply the condition "% decreases by more than" then things you should stop doing!

#4: Source by Time on Site (Customer Behavior).

I am a movie studio. I have trailers for my movie. I have a blogging strategy. I would like to know when parts of that strategy are causing buzz and word of mouth and viral and …. pick your fav phrase. :)

Here is one small alert:

Thanks to your clever use of event tracking you are able to capture time spent watching the movie trailer optimally. The above alert will show you if there are any sites with the word blog in their name that sent visitors that watched your entire movie trailer (a rare occurrence! :)).

NOTE: Now I know that referral path contains blog will not capture all the blogs (like this one!). Remember this is just to spark your creativity.

#5: Country by Huge Visits.

I don't syndicate the content of my blog. But I did give Sidney permission a little while back to translate some of them into Chinese (like this one). He does a wonderful job.

Almost all of the success of my posts at China Web Analytics will be measured by Sidney, his increased readership or comments or rss subscribers or (sadly) number of times it is copied (pirated?) and posted without his permission on many many other blogs.

But there is a small amount of success for this effort that I can measure.

Do I get any traffic from these posts?

I don't know when it happens (a known unknown!) but I have set up an alert to let me know if there is a big improvement in Visits in context of my current 1,200 averagevisits from China…

When this alert is fired off, perhaps in sync with Sidney's publication of my posts, I'll know syndication was a good idea (on this small measure of success).

You can do the same if you have goals / priorities that are geographically focused.

Flip the coin…. and let's say you are the awesome South American giant Mercado Livre and you depend on the US for a good chunk of business.. you can set up custom alerts to know when traffic from the US or Florida or Miami takes a nose dive.

Consider that alert as insurance that if something broke in your online marketing strategy that you will find it quickly.

In Conclusion:

Custom alerts enhance your ability to find surprises in your data, things you might not be expecting.

If you start by using the Web Analytics Measurement Framework it will help bring a focus on what's important to your execution. If you use the Segmentation Selection Framework you'll find that it brings a discipline to your approach.

I hope the above five examples inspire you to go give the feature a whirl, regardless of the web analytics tool you use because all of 'em have it.

Your Turn!

I have barely scratched the surface of what is possible. How do you use custom alerts? Has an alert you had set up saved your bacon? Does your tool provide a particularly clever option? Do you have a best practice you want to recommend?

Share your ideas for custom alerts (for any type of website, using any tool)!

Thanks.

PS:
Couple other related posts you might find interesting:

• Move Beyond The Top Ten. Find Love!
• Leverage Custom Reports For Better Insights!
• Consultants: Present Impactful Analysis, Insightful Reports
• Excellent Analytics Tip#4: Make Your Analysis/Reports "Connectable"

Identify The Known Unknowns: Leverage Analytics Custom Alerts is a post from: Occam's Razor by Avinash Kaushik

The world of the intertubes should be a lot more data driven and awe-sexy than it really is.

Yet for all our collective efforts at writing and tweeting and kvetching online marketing is still based mostly on faith. Not data.

Surprising at so many levels right?

Last week I had the privilege of being invited to deliver the keynote at the annual CMA President's Dinner. John Gustavson, President & CEO of the Canadian Marketing Association, invites a hand selected audience consisting of the crème de la crème of Canadian executives from a vast array of industries. This year they were joined by senior Canadian government officials.

It is difficult to choose something for an address to such a diverse, accomplished and senior audience. My choice was the above thought, faith & data.

My plan was to challenge the status quo, deliver tough love, and inspire transformation.

There were no slides, no notes, just me up on the stage talking. Ok there were around 10 or so bullet items, the talking points. On the flight to Toronto in order to prepare I also wrote down the speech (though I don't read my speeches, so it stayed on the computer).

I wanted to share the speech with you in the hope that it helps you accept the challenging reality we face. I hope it also provides you with a practical set of recommendations to kick your work up a notch or two so we can all win at this web thing.

TV. Internet Marketing. Faith. Data. Problems. Solutions. . . .

__________________________________________________

CMA President's Dinner Keynote.

Good evening.

It is a pleasure to be here tonight and address such a beautiful audience. I want to thank John for inviting me.

My plan tonight is to present some thoughts on how to transform people and companies in the age of the Web, for about 15 minutes, and then address your questions. You are welcome to ask me questions about my talk or anything else connected to the web, companies – marketing – opportunities.

I must admit up front that I am as hard core as any evangelical born again Christian in my passion when it comes to the web. The raw innovation and empowerment that a connected digital world has unleashed is the reason I lovingly refer to it as "God's gift to humanity".

To truly appreciate some of this let us consider the world where marketing is done on faith. Television. Or for that matter magazines or newspapers or radio. All wonderful channels, that are needed and will be around for a long time! But when it comes to measuring success of our marketing efforts all of these channels are largely faith based initiatives.

Consider how we measure success of our TV campaigns.

At a time when there is massive fragmentation of channels and content consumption, where the head is becoming ever smaller with each passing day and the tail becoming really really loooooong, it is amazing that we rely on a measurement system of sampling a handful of viewers who help determine success of tens of millions of dollars of content and millions of dollars of advertising spend. It is outright mind blowing that we use a system whose own legal disclaimers essentially boils down to: "Our data is massively suspect".

Now think of how thin the ice is when it comes to measuring the impact of our precious marketing dollars in magazines and newspapers and other offline channels.

Yet we accept it.

We continue to use faith rather than data to make decisions on $120 Billion (!!) of advertising spend because we don't have much of a choice. We chalk it up to: "It is just the way things have always been." Or: "TV is really hard to measure, those boxes just don't connect or share." [It is rare that we blame the fact that we have not carried out our duty to demand more from both the channel and offline measurement systems.]

All that should explain why I have minor mental orgasms when I think of the online marketing channels and measuring actual business value delivered by our ever more precious marketing dollars.

Just thinking of all the data you can get is enough to put give you a temporary high. With 90+% accuracy you can measure the number of impressions of your ads. You can measure interactions with the ads. You can measure how many people end up on your websites. You can understand how many of them puke and leave! You can measure every facet of success (micro and macro conversions!!). You can measure revenue and economic value! For every dollar you spend! Oh my!!

And to think I have not yet started to talk about how finely you can tune your marketing by leveraging geographic and demographic and psychographic targeting. Leverage powerful metrics like Loyalty, Recency, Brand Perception, Task Completion Rate, Size of Second Level Network, Competitive Share of Voice and more. These are not "loser" metrics like visits and pageviews!

Oh, oh, and you can run experiments! You can fail faster! You can involve your customer in helping you choose the look and feel of your site or the prices you should charge for maximizing profit. You can run controlled experiments to measure incremental online/offline impact and balance the portfolio of media channels you are exposed to, rather than getting distracted by sideshows like "attribution analysis".

So much promise. So exciting. And these are all things you can do today. Don't get me started on the future and what lays ahead, the excitement of it all might cause me to faint.

Yet.

Yet if you look around you on the web you'll see that we swim in a sea of mediocrity. We still see irrelevant blinking banner ads. You'll see astonishingly sucky websites, belonging to come of the best companies in the world. You'll bump into advertising that is remarkable in how irrelevant it is to customer intent. You'll see horrid landing pages. You'll experience missing calls to action, rambling text, and waterboarding through Adobe Flash.

All of it largely driven by faith.

It breaks my heart.

If for no other reason than because your employees are frustrated (they want to be, and can be, so much better) and your customers are being tortured each and every day.

So in a channel that is so full of promise, so full of data, so empowering when it comes to relevance and creativity… why is it that we suck so much?

Based on my humble experience I have boiled it down to three important things:

1. The web has been around forever and yet it is not in the blood of the executives who staff the top echelons of companies.

Make no mistake, they are smart, they are successful and they want to do better. But the web is such a paradigm shift that if it is not in your blood it is very difficult to imagine its power and how to use it for good.

How do you demand innovation & creativity & radical rethink if you can't imagine it?

2. We still believe in and live in the world of "shout marketing", the thing we have practiced on tv and radio and magazines all our lives.

It is not that we don't mean well. But our mental models are jaded.

We still believe in getting lots of impressions. We want to interrupt. We don't despise irrelevance enough. We care about "eyeballs". Because that is all we know. Unfortunately the web (/interactive /digital /social) mandates new mental models, and we are the old dog that won't learn new tricks.

3. Our lousy standards for accountability.

Pause and think of how we measure success today. We measure "reach", we measure "exposure" and other such lame metrics. Partly because that is all we have been trained to expect.

We never say: "Here is a 100,000 for my search campaigns, please come back and report on task completion rates across the top three primary purposes and the economic value added." We never say: "Don't try to fool me with page views generated, did we impact page depth on our content site?" We rarely push hard by saying: "I don't care how frequently our content was updated, what was the impact on visitor loyalty." Or say: "Fine we improved online conversion rate by two percent, but what was the impact on the sales in our retail stores?".

Our bar for accountability is less than low. It is almost non existent.

So…. It turns out the problem is not the web, the problem is not the opportunity, the problem is not measurement.

The problem is you.

The problem is every person in this room.

Our raw understanding, mental models and expectations.

I am sorry. It is kind of a bummer to hear that.

But if you are the problem then the nice thing is that you hold in your hands the power to change your companies and bring about the promised revolution of data driven customer centric online marketing.

Problem identified, how do we fix it?

At the risk of being booed out of this impressive ballroom let me say that the solution is to Embarrass Management!

People who report to you and ask people who report to you to embarrass you.

Why is it awesome?

Turns out no one likes to have their egos bruised. Leverage this powerful force to start to address the three problems I had just outlined.

There are two specific strategies I recommend.

1. Leverage Your Customers.

They want to help. You just have to politely ask.

Not being polite is popping up a 35 question survey on your site. Being polite is inviting them to answer just a couple of questions about their experience when they leave the site. Being polite is uploading your latest "oh my god they are so going to love this (!)" design into fivesecondtest or usertesting and letting your customers share feedback at the cost of a few Tim Hortons coffees. Being polite is running a/b tests on your site so your customers tell you which call to action, piece of content, navigation structure or even product price will yield highest customer satisfaction AND revenue!

Leveraging customers means that when the HiPPO / Boss (perhaps you) opens her mouth to say: "I don't think that will work" or "I like that other way better" or "No one will buy a toothbrush priced $299" or "Twitter is dumb"…. you can say: "Why don't we mock up a quick experiment / online survey / media mix model to validate your hypothesis?"

Allow your customers to help you evolve your mental model. Allow you customers to teach you new and effective marketing strategies. Allow your customers to complement your existing intelligence and savvy.

And if it is hard to get to the above point…. leverage embarrassment!

I recently spoke at a major conference about how one of the top camera companies was disappointing its customers by stinking at the long tail of search. I searched for a digital camera, wireless printer and digital camcorder as a normal undecided customer would. None of my 18 or so searches threw up a single link for this company (not organic, not paid). And yet I was ready to spend $500.

Then I copied exact text from their website for multiple products and searched for them another 20 times. Result? They still would not show up.

Trust me nothing hurts like that raw view of massive failure of your online marketing on the single best acquisition channel on the web today.

Caused embarrassment. Forced a rethink at what is a glaring football field size hole in their marketing strategy.

Who wins? Customers. And the company, they will reduce acquisition cost and make more money.

When there was an argument at a top financial services company about what the home page, the holiest of holy properties per this company, should look like what do you think the company was going to do? Go with the version the President & CEO of the company liked. One smart person interjected to say: "Why don't we take your instinct and convert it into a HiPPOthesis?".

The CEO smiled. They tried three versions. The CEO's performed worst, on goals he had chosen. He still smiled after the test because 1. They made more money. 2. Avoided a big mistake. 3. Created happy customers. 4. He learned something new.

By involving customers companies have figured out that garish zebra print bed sheets are a perfect fit for being sold in their offline stores, identified the perfect song for their tv commercial, designed the best selling dvd covers, discovered pricing / discounts / product bundles that they would never have thought would have worked.

All faster and at a lower cost, with a higher impact on the business. Mental models evolved. Accountability increased.

2. Leverage Competitors.

I have rarely found a strategy that works better at elevating the game of any company than contrasting their efforts with those of their competitors.

It is astonishing that in a medium where your competitor is just a click away, the experience is absolutely frictionless, that we still live as if the burden and hurdles of the offline world exist online.

It is in comparing to competitors, known and unknown, that you can truly get the management to pay attention. Something about the size of the hit to the ego.

Here's an example.

Recently I visited the Sr. Executives of premier technology company and showed two sets of numbers. The ACSI has been measuring customer satisfaction for more than a decade. During that decade Apple's customer satisfaction went from 77 to 84. During that exact time period this tech company's numbers went from 78 (one point higher than Apple!) to 74.

Ouch. That hurts. Especially because they have poured many millions into "improving" the site (and a few million on analytics!).

Sure they don't have the "fanboyism" of Apple, yet Apple had that 10 years ago too. It is painful to realize that Apple started behind them and moved so far ahead, during a time where they not only did not defend their lead…. they actually regressed.

What do you think the management is doing now? Yep, questioning key things like who makes decisions, what the org structure looks like, how can they replace current hyper matrixed accountable structure with something that forces the right behavior at all levels.

Here's another example.

Rather than showing a CPG company how one of their sites was doing I took the liberty of comparing their tea website with their detergent website with their shampoo (personal grooming) website. It was astonishing how each was doing. For example the much smaller tea business was doing better than their key personal grooming business.

But I did not stop there. I compared them to an external benchmark.

What do you think I used? Their direct competition? No. I compared them to my blog's traffic.

It turns out I get two times the traffic when compared to all three of them combined!

Now my blog has nothing to do with a large multichannel CPG company. Yet I write a blog on an esoteric topic (I know that no one really cares about web analytics) and I write twice a month.

Yet I can get more traffic! Part time. With no marketing.

And they spent a couple of million dollars building their websites. To deliver what outcome?

Can you guess the result of this effort?

If you guessed a massive evaluation of their online strategy, ordered from the very top, then you would have guessed right.

Competitors provide a great contrast to your lameness or awesomeness. Be it leveraging the full power of online marketing channels. Be it creating optimal customer experiences. Be it bringing a new layer of imagination and accountability to your existence.

Embarrassment works.

Of course you have to do it right and be absolutely transparent that comes from a place of deep love and from a desire to to be better.

Because you see the goal is not to embarrass. The goal is not to be rude.

The goal is simply to provide context, fast. The goal is to get you, and your companies, to move beyond faith. The goal is to see the obvious potential in front of us. The goal is to throw away the shackles that have for far too long weighed us down.

That is what I mean by, now in quotes, "embarrass".

I hope you take away the passion I feel for making sure that advertising on the internet has to be magnificent and accountable. I hope you'll go empower your organization to "embarrass" you and that you'll do the same to them. I hope tomorrow will be the first day of a revolutionary transformation for your business.

Good luck!

__________________________________________________

The speech was received better then I expected (never easy to tell your audience they are the problem, or lay out tough to swallow solutions). I was profoundly grateful for that. The Q&A session following the speech was a of fun as well (always nice to get a chance to give my "It's not a OR world we live in, that's for super lame folks, it's a AND world!" mini sermon).

It's your turn now.

I would love to get feedback. What are your thoughts on the promise, the three problems and the two possible solutions to jump start a magical revolution?

Online Marketing Still A Faith Based Initiative. Why? What's The Fix? is a post from: Occam's Razor by Avinash Kaushik

My love for segmentation as the primary (only?) way of identify actionable insights is on display in pretty much every single blog post I write.

I have said: All data in aggregate is "crap".

Because it is.

One of my earliest blog posts extolled the glorious virtues of segmentation:
Excellent Analytics Tip#2: Segment Absolutely Everything.

Many paid web analytics clickstream analytics tools, even today (!), don't allow you to do on the fly segmentation of all your data (not without asking you to change javascript script tags every time you need to segment something, or not without paying extra or paying for additional "data warehouse" solutions).

So it was with absolute delight that I wrote a detailed post about the release of Advanced Segmentation feature in Google Analytics in Oct 2008:
Google Analytics Releases Advanced Segmentation: Now Be A Ninja!

Of course Yahoo! Web Analytics, the other wonderful free WA tool, had advanced segmentation from day one.

And as recently as two weeks ago I stressed the importance of effective segmentation as the cornerstone of the Web Analytics Measurement Framework.

The Problem.

You can imagine then how absolutely heartbreaking it is for me to note that nearly all reporting that I see is data in aggregate.

All visits. Total revenue. Avg page views per visitors. Time on site. Overall customer satisfaction. And more. Tons of data "puking", all just aggregates.

The achingly tiny percent of time that the Analyst does segmentation it seems to stop at New vs. Returning Visitors! I have to admit I see that and I feel like throwing a tomato against the wall.

Yes new visitors and returning visitors are segments. But they are so lame that I dare you to find any insight worth, well, a tomato based on those two. You can't. Because new and returning are still two big indefinable globs!

Even if your business actually is tied to understanding the first and then subsequent visits by a person then you are far better off segmenting using Visitor Loyalty (in GA count of visits).

But I am getting off track (this whole non-segmentation business drives me bananas!).

Deep breath.

The Unbearable Lightness of Being.

Segmenting your data is key to your success and that of your company.

It is not very difficult to segment your data. Many tools include some default segments you can apply to any report you are looking at.

For example when you look at your revenue or goal performance it takes a trivial amount of effort to look at All Visits but add to that report the Paid Search Traffic and Non-Paid Search Traffic and get deeper insights.

You can tell your boss: We made 900k, and while you are obsessed with Paid Search please note that 850k of the revenue came from Organic and only $25k from Paid.

PS: Our business is in trouble because we are over-reliant on Search!

See what I mean, a bit better insights.

Among things in the above image I love analyzing Direct (to understand value of the free traffic), Visits with Conversions (to understand my BFF sources and pages and behavior), and Non-bounce Visits (to understand people who give me a chance to do business with them).

But true glory will only come from going beyond the default segments.

Because default segments are created to appeal to everyone / the lowest common denominator, and we all know that there is no such thing as "everyone".

You are unique. The top three things your business is working on are unique. The multi-channel strategy you are executing is unique. Your investment in tools vs people in your company is unique (you are 90/10 instead of 10/90!). You are struggling with your own unique challenges.

You have to have a segmentation strategy that is unique to you. And if you don't then your employment with the company needs to be re-evaluated. (Sorry.)

So how do you go about identifying unique segments for your business or non-profit?

Ask a lot of questions. Tap into the tribal knowledge. Force your leaders (ok HiPPO's) to help you define Business Objectives, Goals and Targets. [Key elements of the Web Analytics Measurement Framework.]

Let me tell you that without the above there is no hope. The first two will tell you what is important and currently prioritized. The third will tell you where to focus you analytical horsepower (based on actuals vs targets).

If you have O, G & T then it is time to select the segments to focus on, the micro-groups of data you'll focus on.

The Segmentation Selector Framework.

My humble recommendation is that as a best practice you should pick at least a couple of segments in each of these three categories:

1. Acquisition. 2. Behavior. 3. Outcomes.

You'll choose to focus on the micro group that is of value to you, and just to you, in each category. You'll apply those segments to web analytics reports where you hope to find insights (and if you choose the right segments you will!).

Let us look at each category I am recommending.

Segment Category #1: Acquisition.

Acquisition refers to the activity you undertake to attract people (or robots!) to your website.

This would include campaigns you run, like pay per click marketing (PPC), email, affiliate deals, display / banner ads, facebook marketing campaigns.

Acquisition also includes search engine optimization (SEO), because it is an activity on which you spend time and money.

Ask yourself this question: "Where is my company currently spending most amount of time and money acquiring traffic?"

Bam! There's the most important segment you will focus on.

Why? If you do your analysis right you can lower cost (by identifying and eliminating the losers!) and you can increase revenue (by identifying and investing where things are going well).

See the process I followed there?

• Ask the question to identify what's important / high priority for the business.

• Create a segment (and then micro segments) for that one thing.

• Apply on the relevant reports to measure performance using key performance indicators.

• Take action. It will have an impact!

Don't just log into Site Catalyst or WebTrends and go on a fishing expedition, or treat every single thing with equal importance.

Paid search. A specific group of keywords. Television campaigns. Email campaigns to prospective customers in Florida, New Mexico, Arizona and Utah. Coupon affiliates. "Social media campaigns" (context). Billboard ads on side on highways. Business cards handed out at trade shows.

All of the above are examples of acquisition strategies.

When you look at your web analytics data look at All Visits AND at least one of the above.

Two acquisition segments is normal.

If you make it three then choose one acquisition strategy that your company is experimenting with.

Say you have 1/10th of one person doing some tweeting or facebooking, :), then add that one segment to your top two. This will allow your management to look at what they are focused on and also one thing that sounds cool but they have no idea if it is actually worth it.

(Short term focus) Win – Win (Long term focus)

How To Apply Segments / Analyze Data.

The reports you'll apply your acquisition segment to will depend on the Key Performance Indicators you have chosen. But a typical set of metrics you'll evaluate will hopefully represent a spectrum of success, like for example. . .

The effort will be to try and understand if for our acquisition segment (say all my brand keywords or for email campaigns to increase sales of the most expensive products). . . .

• How many visits did we get (to get context)

• Of those how many were new visits (if that is a focus)

• How many could we get to give us one pathetic click (bounce rate!)

• What was the cost of acquisition (if you can get total cost give yourself a gold star)

• What value could we extract at a per visit level

• How many people could we get to convert (replace total goal completions with conversion rate if you want)

• What was the total value added to our business or non-profit

As you look at your acquisition segments in context of all visits you can quickly see how you can start to find insights faster. Don't focus specifically on the metrics I have used above but rather the thought process behind their selection.

This is not the end of your journey but it is a darn good start!

[If you have Web Analytics 2.0 pop the CD at the back into your computer. In dashboard examples look for Stratigent_Sample_Dashboard.xls, via my friend Bill Bruno at Stratigent. It has an excellent example of segmented acquisition display, you can immediately steal it for your company!]

Segment Category #2: Behavior.

Behavior refers to the activity people are undertaking on your website.

When people show up, what is it that they are doing? Is there anything discernable / important in their behavior that is adding value to your online existence? Or, the flip side, what do we want people do to on our site, and is anyone exhibiting that behavior?

Even people who sometimes have segment their web analytics data often forget to segment by online behavior.

Many, but not all, behavior segments fall into these two buckets: People who see x pages. People who do y things.

Here are some specific examples (all of which you can create in Yahoo! Web Analytics or Google Analytics in a few seconds without having to pay anything extra for vars and slots or having to update your javascript tag or having to buy an add-on, you can also apply them to all your data including all your historical data).

Visits with more than three page views. . .

This can be so valuable on content only websites (more page views more impressions of irrelevant display ads!) or even on ecommerce websites (more pages views the deeper you sink your hook into the visitor, engagement baby!).

Where do these people come from? Do they buy a lot? A little? Do they write reviews? Did we acquire them or did they just show up? If they see so many pages what type of content are they interested in (politics? naked pictures? sports?)?

So on and so forth. Segmenting one behavior, understanding its value.

Similarly another could be focusing on people how add to cart and then abandon the site.

Or people who enter the site on the home page and their behavior. . .

Or all those who did not enter the site via the home page!

Or people who use the site's product comparison chart or car configurator or, my fav, internal site search. Vs. those that don't.

Or people whose Days to Purchase (/Transaction) are 5 vs for those for whom the Days to Purchase is 1. . .

Or, cuter, those whose last visit to our website was 100, or whatever, days ago. Why? And what do they want?

Or people who visited the site more than 9 times (!) during the current time period. . .

Where are these sweet delicious people coming from? (Note: To a blog updated only twice a month!) What do they read? What do they buy? What can we learn from them and do more of?

Those are the types of questions you'll answer from your behavioral segments.

The more you understand what people are doing on your site, the more likely it is that you'll stop the silliness on your site (kill content, redo navigation, make cross sells better, eliminate 80% of the ads, learn to live with 19 days to conversion, don't sell too hard, and so much more).

It is also likely (I want to say guaranteed) that you'll find the delta between what you want to have happen and what your customers want. You'll choose to make happier customers, who in turn, in the naughtiest way possible, will make you happy.

And it all stars with being able to identify and focus on the right behavior segments.

Pick at least two.

But I have to admit in this segment category I truly "play" with the data a lot because it is so hard to know what the right segments are, because visitor behavior is such a complicated thing (they are constantly trying to mess with us Analysts!).

It is only after experimentation (a lot) that I end up with something sweet.

Segment Category #3: Outcomes.

Outcomes are site activities that add value to you (business/non-profit).

I find that here the problem is less that the Analysis Ninjas don't segment, rather it is that they are incredibly unimaginative.

But first what is it?

Segments with outcomes are people or visits where you get a order (at an ecommerce website) or you get a lead (at Organizing for America).

Those two are obvious right?

Segment out people who delivered those two outcomes. Give them a warm hug and a kiss. Now go figure out what makes them unique when compared to everyone else who showed up at your website, all those other people who you worked so hard to impress but failed to.

Take the insights and do more of what works for this group.

Or segment out everyone whose order size is 50% more than the average order size. . .

These are your "whales", people who spend a lot of money with you. Don't you want to get to know them a lot better? : )

But there is more.

Remember macro AND micro conversions!

No one is going to sleep with you on the first date. (Ok maybe a few will!)

So focus on micro conversions that lead up to a macro conversion… like people playing a product video (or on content site watching five videos!). . .

Or adding a product to their Wish List.

Or signing up to show up for a protest for your ultra liberal policies!

Or apply for a trial, or download a trial product.

You can also focus on micro conversions that all by themselves are of value to you, even if not as much as the macro conversion.

For example submitting a job application.

Or signing up for a RSS feed.

Or clicking on a link to go to a different site you want them to go to (like clicking on the amazon link to go buy my book – great outcome :)).

Of course if you are really really good you'll also segment my absolute favorite metric in the whole wide world: Task Completion Rate. It is the ultimate measure of outcome (from your customer's perspective).

[If you use 4Q then now you can do some very very cool segmentation directly in Google Analytics! Watch this video to learn how to merge your quantitative GA data with your qualitative 4Q data. Pretty sweet.]

Net, net. . . it is absolutely critical that you segment your data by the key outcomes important to your business. Not just because your site exists to add economic value, but also because I cannot think of another way you can earn the love of your boss or get promoted.

By understanding what it is about people who deliver outcomes you can understand what to do with all those that don't convert.

Outcomes. Outcomes. Outcomes!

Pick at least two.

If you pick three or four that is ok.

If you pick nine it might be a signal you don't know what you are doing (and you want to corner your boss in a non-HR-violation manner and ask her to help you focus on the most important).

In Summary.

Segment or die.

It is as simple as that.

The next time you start to do true analysis of your data I hope you have your minimum six segments in hand (two for each category). If you do you'll find that web analytics, this world full of web metrics and what not, suddenly becomes a lot more interesting (and you no longer feel like jumping out of your office window in frustration!).

Love, money and glory await you.

Not to mention how proud I'll be of you when I see your analysis. ; )

Ok now your turn.

Are you a segmentation God? What are some of your favorite segments? Have you used this three category framework in the past to find segments? Do you think they'll work in real life? In the context of segments what do you think is missing from this blog post? What did I overlook / not stress enough?

What's your excuse for not leveraging segmentation? (Best answer to this question win's a copy of Web Analytics 2.0!)

Please share your thoughts / wisdom / critique / guidance.

Thanks.

PS:
Couple other related posts you might find interesting:

• Excellent Analytics Tip#2: Segment Absolutely Everything
• Google Analytics Releases Advanced Segmentation: Now Be A Ninja!
• Excellent Analytics Tip#4: Make Your Analysis/Reports "Connectable"
• "Dear Avinash": Be Awesome At Comparing KPI Trends Over Time
• 10 Fundamental Web Analytics Truths: Embrace 'Em & Win Big

Web Analytics Segmentation: Do Or Die, There Is No Try! is a post from: Occam's Razor by Avinash Kaushik

The hardest kind of "analysis" to provide is in response to open ended questions. That is why I love asking open ended questions!

They expose a person's critical thinking ability (something I highly recommend you test when you hire web analysts: Interviewing Tip: Stress Test Critical Thinking. Please).

They also help you understand if someone really grasps key concepts.

Recently on behalf of Market Motive, my start up that focuses on online marketing education, I had the opportunity to offer one scholarship for the latest round of Master Certification in Web Analytics.

So at the end of my 10 Fundamental Web Analytics Truths blog post I requested readers who were interested in the scholarship to complete this simple task:

Pick a site you love and tell me three things you would change about it, and why.

Seems straight forward right? It is not!

First I must say that I was overwhelmed by the responses (thanks!) and I was impressed with the time people took to do the analysis. I got wonderfully created pdfs / Word docs and well written emails. I was amazed at the creativity on display (which validated the fact that I have chosen to be in the right industry!).

Based on the responses, some wonderful and some not quite as wonderful (!), in this post I thought I'll share with you some tips should someone (like me!) ask you an open ended question ("what would you and why").

The first part covers 5 rules, sourced mostly from what people did not do. The second part contains 4 things people did that delighted me.

Let's go.

When someone asks you an open ended question, at least connected to web analysis, here's what's important. . .

1. Don't offer your opinion, at least not right away.

This is a very very hard temptation to resist. But try.

These were most common fixes people wanted to make on sites they loved:

Remove big header
I don't like the colors.
I would change the entire site design.
Reduce font size / increase font size.
The font type is not great.

I have to tell you that the last thing anyone wants to hear, in this context, is your opinion.

Not your boss. Not your friend. Certainly not the HiPPO (Highest Paid Person's Opinion).

Even if you believe that you are "absolutely right"! [Note: I often think I am "absolutely right". :)]

You and I are poor proxies for the customer. And just because you don't like something… how should I put it so you'll understand…. oh let's try this…. you not liking something is not a statistically significant sample of data!

On a serious note… offering your opinion on something, unsupported by any data except "I think", is probably a really poor way to start a conversation with anyone in the Analytics field.

If you express your opinion then present it in the from of a hypothesis that can be tested. Win-win.

So for example consider saying something like:

"I have viewed the site through Google Browser Size. The huge header on the website is causing the main content to be visible to only 40% of the website visitors. Based on this my hypothesis is that reducing the size of the header will reduce bounce rate and increase click-through rate to key pages/products."

See the difference?

It is ok that you started with a hunch. You went and got some kind of data. Finally you offer a hypothesis that I can test, and you were clever enough to point to two things of value to the business (both of which can be measured!).

Your HiPPO / Boss is much much more likely to listen to you and accept your wisdom.

In the rarest of rare cases if you must express your opinion, present your credentials. Something like:

"I would change the layout of the site and eliminate the images because I am Jakob Nielsen and I know what the heck I am talking about!"

See that would be acceptable. :)

Overall: if you can, try not to offer your opinions (at least not in the opening statement).

2. Always offer alternatives / Think things through.

One of the persistent flaws in Web Analysts (and Marketers as well I am afraid) is that far too often we take a siloed view of things. We only see our slice of data. We only see our little world. We only care about what bothers us / what makes us happy.

You should always take a much more expansive view of things and when you make recommendations think of the big picture, think things through.

Here is a good example.

I was astonished at how many Ninja's included this in their fixes: Remove Ads.

Now I love adblock as much as the next guy and wish advertising (especially Display) were more relevant.

But when you as an Analyst recommend removing ads because you find them annoying (and they can be super annoying) you are essentially recommending the removal of a revenue stream.

Ok so if I accept your recommendation of removing ads what do you recommend I do about the revenue stream?

The "remove ads" recommendations did not consider that implication of their recommendation.

Now I don't expect you to be an expert on the intricacies of the business you are analyzing when I give you an assignment to do "impromptu analysis". But I would have loved to know that you thought about the big picture, what you thought about the implications of your recommendations.

You could have said:

"I would remove the ads because they are super annoying. I would recommend replacing them with an investment in targeting email campaigns which I believe will more than make up for the missed revenue.

Or:

"I would remove the ads and instead add a prominent "If you love the content donate money" button on the top navigation. The money we lose in advertising we will more than make up in donations."

Or:

"I would remove the ads. While that will mean we lose revenue in the short term, my hypothesis is that customer satisfaction will improve by 18 points which will lead to increased Visitor Loyalty and is that not what ESPN really wants?"

Give me a clue that you have: 1. Thought through the implications of your recommendations. 2. Have some alternatives handy, no matter how pie in the sky.

Here is another recommendation that is more nuanced, and something I think we as Analysts rarely think through.

The recommendation was that Flickr should allow posting of anonymous comments because it will likely result in more comments being published on pictures which will potentially increase User Engagement.

A very nice suggestion.

But by now it has been well established that anonymous comments very quickly lead to unintended consequences. [New York Times article.] All kinds of people jump in and, quite literally, say all kinds of things.

I would have loved to hear what your suggestion was to deal with this absolutely sure to happen outcome from your recommendation.

Think things through. As an Analyst, as someone who thinks more broadly.

[Note: I am not saying comments are bad. I am not saying all anonymous comments are bad. I am not saying comments should be 100% moderated and neutered before being posted. There is a happy medium and there are many wonderful options to deal with this problem.]

3. Offer data, even when you don't have access to the site's data.

Alec shared a guidance with me after the contest was announced. He said, and I am paraphrasing, "award the scholarship to the person who says that they can't make any recommendations to fix the site they love because they don't have access to the data".

Really good point.

I had very much kept my question open ended because I really wanted to see if people got creative with how they arrived at the recommendations (beyond the "I think").

I am afraid no one provided data.

On the surface it is understandable. You are doing analysis, impromptu analysis, on a site that you don't own. Of course you don't have access to data to base your opinions on.

Unfortunately that is not quite true.

You ALWAYS have access to data. For ANY website.

If you want to understand the clickstream data for any website you could go to Compete (here's ESPN's data, or this blog's). If you want data for a international site use Google Trends for Websites (here's H M V's data, and here's data for people from Switzerland who read the French newspaper LeMonde).

Sure the data is not 100% accurate, but it is directionally accurate and it will take a few minutes on either Compete or Trends to dig a bit and find something interesting you could base your recommendations on. It should take you a few more minutes to compare data for one site to its direct competitor and identify something even more interesting.

If you want to understand the search engine ecosystem then use Insights for Search. Check out how much delightful data is available to you: Acne vs. Poison. [Look out, poison making a massive come back!!]

Spend time understanding the keyword market and consumer interest for the business you are analyzing. Find strengths and weaknesses. Find opportunities (by geographic region or in the cluster of top related searches or, my fav, fastest rising searches). There are so many sources, so many possibilities (many free!).

If you want to get demographic or psychographic segmentation data use the DoubleClick Ad Planner. In a few minutes you can understand the demographic make up of any site.

Male – female, age, education, household income, audience interest and more. In a few more minutes you can get down identifying the psychographic segments. Affluent 100k+? Brides-to-be? Gossip Gurus? Home Buyers? Moms? Technology Geeks? Who are we talking to? Who do we want to talk to?

And these are just the basics. Check out: The Definitive Guide To (8) Competitive Intelligence Data Sources.

You always have access to data. Regardless of if you own the site or not.

If you are put in a position where you have to offer impromptu analysis please use these (and other) data sources to add the kind of power to your recommendations that can only come from being backed up with data. Some data.

4. Always, always, always state what you think the Objectives are.

This is such a common mistake when we present our analysis. We make recommendations without saying what we are actually solving for.

Before you present your recommendations first tell me what you think the website's objectives are. What you think the purpose of the website is. What you think the site is solving for.

Often analysis is not valued very highly not because it is stinky, it is because the producer and the receiver disagree on what the objectives of the site are.

I might think the purpose is: Orders, Leads, Job Applications.

You might think the purpose is: Facebook followers, Brand Perception Lift, Product Reviews.

If you don't tell me what you assumed the objectives were you'll see very quickly why I might think you produced nothing of value.

So make it clear.

I might still think your analysis was poor (or awesome!), but at least I know what you were solving for.

I have context within which I can place your analysis.

You might think that it is obvious what the purpose of GoNomad or NBA.com or SFAF is. But I assure you that it is not obvious. So make it obvious, we'll both come to your analysis / recommendations from the same perspective.

In your daily jobs you should never present your analysis without having shared vision around the objectives. Otherwise the best result is no action will be taken on your recommendations. The worst result is… we'll I don't have to say it do I? :)

[Use this if it helps: Web Analytics Measurement Framework. Though for impromptu analysis you don't have to get that detailed. Just keep the framework at the back of your mind.]

5. Focus on the obvious, and the non-obvious.

Even if you spend only 30 mins on doing some analysis try to say something that I won't anticipate by spending 5 mins on the site's home page.

Surprise me [/ your boss / your audience / children / god].

Here is an example.

I can guess the Macro Conversion on site in two seconds. So tell me about the three Micro Conversions that are not obvious but of great value to the site.

Say you looked at Williams-Sonoma. Points for telling me about ecommerce. Bonus points for grasping and telling me how to improve qualified sign-ups for the Williams-Sonoma Catalog (which brings a lot more revenue in the long term than a quickie online order). Or how to improve number of brides creating Wedding Registries (huge money there). Or memberships to the Wine Club. Or Gift Cards (which are essentially customers making interest free loans to Williams-Sonoma!).

Surprise me.

Visit the website of the site's biggest competitor and tell me two things they do well that you think your site should.

Dig out industry standard scores for Customer Satisfaction & Task Completion Rates and use that to tell me areas of opportunities.

Give me three specific ideas for A/B or Multivariate tests and state your hypothesis for what will change.

Present your analysis / recommendations in a different format.

Shock me by including a framework you use for your recommendations (which one person did, it looked like a house! so amazing!).

Postulate a good enough reason to use Social Media (not just because everyone is doing it).

Tell me about how the inevitable demographic shifts in the US population will destroy the current business that this company has.

Surprise me.

If Scott or Brett or Dai or Trevor or someone else can spend a few minutes on the website and come to the exact same conclusions as you then it is unlikely that your analysis will be as impressive as you think it should be.

So… focus on the things that will be obvious to many and then include at least one non-obvious thing that almost no one will focus on because only you, the unique awesome genius person that you are, will see it.

Summary: Don't just offer opinions, think things through, offer data, clarify what you are solving for and finally do at least one thing that falls in the non-obvious category.

Amongst the submissions that was presented there were some common themes in the I was quite delighted by.

Here are a few of them, you should do these too when you do analysis…

1. "Why before the how"

Almost everyone focused on redesigning the home page, with one holy goal in mind: Make the value proposition of the company really clear really fast.

I love that!

One person framed it so well: "Address the why before the how."

Brilliantly put.

Use that mantra every day.

Some things were common in many submissions, and these I really really liked:

2. Obsess about SEO.

Some folks diligently focused on SEO, and I LOVE SEO!

From garbled urls to missing title tags to poorly linked internal pages to missing site maps. I am so happy people found these things (and EVERYONE of you can too with basic knowledge of SEO!).

It is "free" traffic, but more than that it is investing in the long term success. It is pretty attractive to jump to Paid Search recommendations or doing more Email Campaigns. You should do that, but if you come to me with that and not mention SEO you are going to break my heart.

[Even if you are an Analyst I expect you to have the knowledge described here: Official Google Search Engine Optimization (SEO) Starter Guide.]

3. Be different.

I covered this a bit in #5 above. But wanted to share more context with you.

In their analysis some people tried to be different. That is always a good thing.

Instead of sharing a site and three things one person shared three things they would change about the state of Texas!

Made me smile (and I sent him a free copy of Web Analytics 2.0 :)).

On a serious note… you know the obvious things people will say in these situations, and so do the HiPPO's (they have heard it all before). Try to be different (though not Texas different!).

4. Be sweet.

Without exception everyone was very sweet. Most people tried really hard to send me the best submission they could. I got special graphs, images, wonderfully formatted word documents… so much.

It was so nice. I feel profoundly grateful.

Life is short. Be sweet to those around you. They'll reflect it back. One person at a time we can make the world a better and less bitter place.

Closing Thoughts.

I recognize that you won't do all of the above for an "impromptu analysis", else there would be nothing impromptu about it.

I hope that you'll take the principles outlined in this blog post and make them a part of your DNA. When you are asked to do some quick analysis that you'll activate these principles, even without thinking about them too much.

When I have to analyze a site I quickly make a note of the two or three objectives of the site (and one of those falls in the non-obvious category). I log into Compete and Trends and get some data about clickstream. I see if there are clues in Insights for Search and Ad Planner about the site's business. Then I write down two of three things recommendations / fixes that I can back up with data, or in case of no data formulate and preset a couple hypotheses for testing.

It takes me between 30 mins to an hour. I won't change the website's trajectory in a massive way, but I'll definitely give them some concrete things that will have a short term noticeable positive impact.

And you can too!

Ok now it's your turn.

What is your approach when put on the spot and asked for some analysis of a site you don't own? What are one or two techniques that work for you? Thoughts on the above nine principles?

Please share your critique / approaches / feedback in comments below.

Thank you.

Analyze This: 5 Rules For Awesome Impromptu Web Analysis is a post from: Occam's Razor by Avinash Kaushik

It is surprising how often these "simple" things come up.

"What is the difference between a metric and a key performance indicator (KPI)?"

"What is a dimension in analytics?"

"What is segmentation?"

"Are goals metrics?"

And many more.

There seems to be genuine confusion about the simplest, most foundational, parts of web metrics / analytics. So in this short post let's try and see if we can fix this really basic problem.

Definitions and standard perspectives on these terms will be covered in this post:

1. Business Objectives
2. Goals
3. Metrics
4. Key Performance Indicators
5. Targets
6. Dimensions
7. Segments

A standard definition will be provided, but more than that my hope is to solidify your understanding with concrete examples and pictures.

The post will end with a "Web Analytics Measurement Framework" – a very lofty name for something that will help you put your understanding of this post into practice.

Business Objectives:

This is the answer to the question: "Why does your website exist?"

Or: "What are you hoping to accomplish for your business by being on the web?"

Or: "What are the three most important priorities for your site?"

Or other questions like that.

Without a clearly defined list of business objectives you are doomed, because if you don't know where you are going then any road will take you there.

The objectives must be DUMB: Doable. Understandable. Manageable. Beneficial.

90% of the failures in web analytics, the reasons companies are data rich and information poor, is because they don't have DUMB objectives.

Or they have just one (DUMB) Macro Conversion defined and completely ignore the Micro Conversions and Economic Value.

Your company leadership (small business or fortune 100) will help you identify business objectives for your online existence. Beg, threaten, embarrass, sleep with someone, do what you have to get them defined.

Point of confusion: People, like me, often also use the term Desirable Outcomes to refer to business objectives. They are one and the same thing.

[Full disclosure: Depending on the specificity of your business objectives my asking you for your "desirable outcomes" could refer to "what are your goals". See below...]

Goals:

Goals are specific strategies you'll leverage to accomplish your business objectives.

Business objectives can be quite strategic and high level. Sell more stuff. Create happy customers. Improve marketing effectiveness.

Goals are the next level drill down.

It goes something like this. . .

Sell more stuff really means we have to:

1. do x

2. improve y

3. reduce z

Improve marketing effectiveness might translate into these goals because currently they are our priorities:

1. identify broken things in m

2. figure out how to do n

3. experiment with p type of campaigns

Get it?

The beauty of goals is that they reflect specific strategies. They are really DUMB. They are priorities. They are actually things almost everyone in the company will understand as soon as you say them.

I would not have included the step of identifying Goals were it not for the fact that almost every C level executive, every VP and SVP, give very high level nearly impossible to pin down business objectives.

Point of confusion: Many web analytics tools, like Google Analytics, have a feature that encourages you to measure Goals. Like so. . .

It is possible that some Analytics Tool Goals directly measure your business objectives or goals. Usually though Analytics Tool Goals do not rise to the strategic importance so as to measure either your business objectives or your goals.

For example only one of the above, Subscribers, is an actual goal ("increase persistent reach")for me that lines up directly with a business objective ("effective permission marketing"). Others are nice to know.

So to be clear: Just because you have Goals in your analytics tool defined is not a sure sign that you know what your business objectives or goals are.

Before you touch the data make sure your business objectives (usually 3, or 5 max) are clearly identified and you have drilled down to really DUMB goals!

Metric:

A metric is a number.

That is the simplest way to think about it.

Technically a metric can be a Count (a total) or a Ratio (a division of one number by another).

Examples of metrics that are a Count is Visits or Pageviews.

Examples of a Ratio is Conversion Rate (a quantitative metric) or Task Completion Rate (a qualitative metric).

This is a crude way to think about it but. . . Metrics almost always appear in columns in a report / excel spreadsheet.

This is what metrics look like in your web analytics tool:

Metrics form the life blood of all the measurement we do. They are the reason we call the web the most accountable channel on the planet.

Key Performance Indicator:

Key performance indicators (KPI's) are metrics. But not normal metrics. They are our BFF's.

Here is the definition of a KPI that is on Page 37 of Web Analytics 2.0:

A key performance indicator (KPI) is a metric that helps you understand how you are doing against your objectives.

That last word – objectives – is critical to something being called a KPI, which is also why KPI's tend to be unique to each company.

I run www.bestbuy.com. My business objective is to sell lots of stuff. My web analytics KPI is: Average Order Size.

Business objective: Sell Stuff. KPI: Average Order Size.

I might use other metrics in my reports, say Visits or # of Videos Watched or whatever. But they won't be my KPI's.

Makes sense? No? Ok one more. . .

I run www.nytimes.com. My business objective is to make money. One of my KPI's is: Visitor Loyalty (number of visits to the site by the same person in a month) and another one is # of clicks on banner ads.

So one thing should be pretty clear to you by now. . . if you don't have business objectives (from your HiPPO's) clearly defined, you can't identify what your KPI's are.

No matter how metrics rich you are. You'll be information poor. Forever. So. Don't be.

Business Objectives -> Goals -> KPI's -> Metrics -> Magic.

Targets:

Targets are numerical values you have pre-determined as indicators success or failure.

It is rare, even with the best intentions, that you'll create targets for all the metrics you'll report on.

Yet it is critical that you create targets for each web analytics key performance indicator.

I am still at Best Buy. My KPI is still Average Order Value. But how do I know what's good or bad?

I'll consult with my finance team. I'll confab with my Assistant Senior Vice President for American Online Sales. I'll look over my historical performance.

Through this consultative process we'll create a 2010 AOV target of $95.

Now when I do analysis of my performance (not just in aggregated but segmented by geo and campaign and source and…) I'll know if our results are good or bad or ugly.

I will do this for every single KPI whose responsibility is thrust on em.

You can create targets for the quarter (Christmas!) or for the year or to any drill down level of specificity. But at least have one overall target for each KPI.

Business Objectives -> Goals -> KPIs -> Metrics -> Targets -> Minor Orgasms.

Dimension:

A dimension is, typically, an attribute of the Visitor to your website.

Here's a simplistic pictorial representation. . .

The source that someone came from (referring urls, campaigns, countries etc) is a dimension in your web analytics data.

So is technical information like browsers or mobile phones or (god save you if you are still doing daily reports on) screen resolution or ISP used.

The activity a person performed such as the landing page name, the subsequent pages they saw, videos they played, searches they did on your website and the products they purchased are all dimensions.

Finally the day they visited, the days since their last visit (if returning visitor) the number of visits they made, the number of pages they saw are all dimensions as well. I know, I know, they sound like metrics. But they are, as the definition says up top, attributes of the visitor and their activity on your website.

This is a crude way to think about it but… Dimensions almost always appear in rows in a report / excel spreadsheet.

Here are the metrics and dimensions in one of my favorite Yahoo! Web Analytics reports, it shows me how many clicks it takes for visitors to get to content I consider valuable. . .

Columns and rows. Get it?

Let's solidify this with another example of a report that shows metrics and dimensions. This report might not come to your mind most easily. I am looking at the internal site searches (on this blog) and the continent from where the search is done and a set of metrics to judge performance. . .

Dimensions allow you to group your data into different buckets and they are most frequently used to slice and dice the web analytics data.

In your web analytics tools you'll bump into dimensions when you are either creating custom reports (love this!) or doing advanced segmentation (worship this!). The chooser thingys look like this. . .

In Yahoo! Web Analytics they are called "Groups" or "Group Selection" but they are the same thing: Dimensions.

There are many long and complicated definitions of dimensions. There are some nuances that I have simplified. But I hope that this definition and explanation helps you internalize this key concept in web analytics.

Segments:

A segment contains a group of rows from one or more dimensions.

In aggregate almost all data is useless (like # of Visits). The best way to find insights is to segment the data using one or more dimensions (like # of Visits from: USA, UK, India as a % of All Visits).

You segment by dimensions and report by metrics.

Here are some examples of segments I use in my Google Analytics account:

Checkout the dimensions I am using to segment my website traffic to understand performance better.

• Analyzing people just from North Carolina (because there was an ad campaign targeted just to NC)

• People who spend more than one minute on the site

• People who click on the link to go to Feedburner to sign up for my RSS feed

• People who come from images.google.com and smart mobile phones

• People who visit from one source, Wikipedia, AND only one page on Wikipedia (the bounce rate article)

These are just a few of the 28 advanced segments I have created in my analytics profile.

And I am not even a real business.

Think of how many segments I would analyze to truly analyze my Key Performance Indicators to understand causes of success or failure of my Business Objectives!

The Analysis Ninja rallying cry: Segment or Die!

: )

So now you know the seven most fundamental, yet critical, things you need to know about online analytics.

If you fee that you did not understand it all, please go back and re-read it. You are very welcome to ask questions or for clarification via comments. Whatever it takes, make sure you are able to internalize this.

Let's move to the last step. . .

Web Analytics Measurement Framework

As promised I want to wrap up this post with a couple of examples that pull this whole thing together.

Let's say I am responsible for the National Council of La Raza (a wonderful organization I support). Here is how the measurement framework could possibly look for me. . .

Business Objective:

Attendance at immigration rallies.

Goals:

Increase web sign ups.

Key Performance Indicators:

# of NCLR Sign-ups for NCLR Action Alerts

# of Individual Memberships

Target:

Action Alert: 14,000 per month

Memberships: 4,800 per month

Segments:

Acquisition: Organic search, Email campaigns, Mid-western US states

Behavior: Visits to conversions (Action Alerts, Memberships)

All this before I cracked open any web analytics tool.

I have a framework I can use to ensure that the work I do is focused on what's important to the organization, what good or bad looks like in terms of performance and finally I have a segmentation plan ready to do the preliminary analysis of the data.

No fishing expeditions. No data puking. No begging people to pay attention to data!

One more example.

If you are a student in the MarketMotive Master Certification course as a part of your final dissertation you have to submit complete analysis of two websites. One eCommerce and one non-eCommerce. You are supposed to start from scratch, do all of the above and present actionable recommendations. The path you follow, the quality of your analysis and your insights determine if you are awarded the certification, or not.

One of the web analytics students in the just concluded course was Matt Smedley.

In his dissertation Matt used the above framework very effectively to focus and structure his analysis.

Here is Matt's actual picture from his dissertation that tells the whole story:

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

I really liked Matt's presentation for his motor bike company analysis. In less than half a page one could see the complete picture of what the business was solving for and what the expectations were.

Particularly clever I thought was his inclusion of the segmentation in his framework presentation. At a glance for the most important goal for the quarter (build a robust customer database for future marketing) you can see how their campaign strategy worked.

Don't even get me started on how awesome it was for him to including Profit as a KPI. Truly heart warming.

I hope you will find inspiration from Matt's presentation to go create a version of this framework for your company.

We worry so much about tags and data collection and Omniture vs. WebTrends. What we should actually worry about is above. It is not easy to arrive at, but without it all you have is unlimited potential for failure.

And I know that is not going to happen to you.

I wish you all the very best.

Ok now your turn.

What do you think of the seven fundamental terms and their definitions? Agree? Disagree? Which one surprised you the most? Was there a point you think I missed in explaining these complex concepts? Do you have a measurement framework you use in your company you want to share with us?

Please share your feedback via comments.

Thanks.

PS:
Couple other related posts you might find interesting:

• 10 Fundamental Web Analytics Truths: Embrace 'Em & Win Big
• Actively Avoid Insights: 4 Useful KPI Measurement Techniques
• The "Action Dashboard" (An Alternative To Crappy Dashboards)
• Kill Useless Web Metrics: Apply The "Three Layers Of So What" Test
• Six Web Metrics / KPI's To Die For

Web Analytics 101: Definitions: Goals, Metrics, KPIs, Dimensions, Targets is a post from: Occam's Razor by Avinash Kaushik