May 2010

18 May 2010 01:24 am

piecesMy 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.

google analytics default segments

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.

analytics acquisition segment

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. . .

web analytics custom report

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. . .

page depth segment

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. . .

home page entrances advanced segment

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. . .

days to transactions

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. . .

count of visits advanced segment google analytics

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. . .

segmenting 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!). . .

tracking video events analytics

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.


Couple other related posts you might find interesting:

04 May 2010 01:44 am

up closeThe 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. . .

your opinions

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).

alternatives big picture

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.


"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."


"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.]

competitive intelligence tools

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.

business objectives

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 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.]

surprising outcomes

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.

all aces 1

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.

wrap a bow

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.