It is sad that we spend so little time on brand analysis, primarily because 1. there is such little accountability to brand marketing and 2. it is such a strategic part of any business.

So let's fix that problem in this blog post. Let's become BFFs with a lovely hidden gem that helps you leverage one of the largest source of data on the planet to understand the strength of your brand over time.

[Bonus One: Read: Brand Measurement: Analytics & Metrics for Branding Campaigns]

There are many different tools, both online and offline, that measure the elusive metric called brand strength. It's elusive because brand strength is, at its core deeply qualitative and none of us measurement types can really see inside your hearts and draw charts of the evolution of what's in your heart over time. So we use proxies, and we do the best we can.

One of my favorite tools to do that is Insights for Search which provides an incredible way to see how interest in your brand has grown over time and whether you are strengthening your brand over time.

Brand Strength via Unaided Brand Recall

Insights for Search sits on top of all of Google's organic search data from around the world. I believe it is one of the best possible ways to measure what humanity is thinking, and telling us via the queries they run on Google. I love using this tool to measure "unaided brand recall ."

The stronger your unaided brand recall, the more likely people recognize you, think of you, consider you when they need what you have to offer. I never search for a sports car. I search for the "best Nissan sports car."

You increase unaided brand recall by creating great products (its not called a tablet, they are all called iPads), delivering fantastic service ("their return process is as good as Zappos"), and of course online and offline advertising.

Sometimes it all works together. Recently I saw a TV ad by eBay for designer jeans. I typed designer jeans into Google (for that is what people do when they watch TV). The first ad was for Amazon. No eBay PPC ad or SEO listing showed up. Clever Amazon tying its online advertising with a competitor's offline advertising. Now I search for "amazon designer jeans." :)

For your brand Insights for Search provides an incredible way to see how your brand has grown over time, and whether you are strengthening your brand. If you strengthen it, you drive people to look for you (and not your competitors), and you can capture them more easily using Search (Organic or Paid). Brand queries, obviously, also convert better.

Leveraging Google Insights for Search

So over time, how's your brand doing?

Step 1: Type your brand name, and your direct competitor, into the Search Terms area of Insights for Search .

Step 2: Pick the right country, time period, and -this is important – high-level category in which your brand belongs.

Step 3: Click Search.

Step 4: In the middle of the resulting report you'll see a trend that looks like this:

This shows the number of searches for your brand, relative to the total number of searches done on Google over time (for the geographic region and time period you've chosen). The data you see is normalized and presented on a scale from 0-100.

This is interesting. You can see that eBay (green) rose for a while but has been essentially flat. During the same time period Walmart (red), Amazon (blue) and Target (orange) have done exceptionally well.

But (as every Analysis Ninja knows) competitive context (above) is good, but industry/category context is even better! So…

Step 5: Click on the tab that reads "Growth relative to the Shopping category" and boom!

This is a lot more interesting. [Click on the above image for a higher resolution version.]

The graph shows the change over time, starting in Jan 2004. On the right axis you can see how each brand has grown over time in terms of its brand strength, in context of the growth of the Shopping category.

It is pretty amazing to see that even as eBay has massively ramped up its offline (including big TV) advertising, at least in this context its growth (unaided brand recall) has actually lagged its competitors quite a bit.

eBay's green line is very close the performance of the category (and you'll see that often at peaks in the shopping category queries, eBay actually does worse starting holiday season 2009).

The tussle between Wal-Mart and Target is interesting. It used to be cat and mouse, but over the last three years Wal-Mart is clearly leaving Target in the dust (just look at that spike during this past holiday season, omg!).

Amazon is an interesting example. It used to fall behind lag the other two in brand queries, but you can see how starting late 2009 (bad year for Target in this context) Amazon overtook Target and now (2011, 2012) is casting a big shadow over Target. For a real appreciation of how amazing this accomplishment is, consider the TV ads Target runs, the number of Saturday mailers it sends out, the number of billboards it buys, etc.

The above trend lines, when viewed in context of your category, helps you understand how well you are doing in terms of increasing your brand strength.

Do this analysis for your company.

Brand strength is important because when I type "ebay big screen tv" in the search field, I essentially eliminate everyone else. If I type in just "big screen tv", I'm going to Amazon (they just rank so well).

Brand strength is built over time using online and offline advertising. Brand strength is not built by playing a "let's bid on just our brand terms" strategy, but by complementing that strategy with a super-smart organic and paid "let's capture all our brand and category terms" strategy.

[Bonus Two: Video: Enhancing Brand Strength (and Avoiding Brand Destruction) via Social Media]

"Timing The Market"

One thing about Amazon looked particularly interesting to me.

You'll notice that Amazon's Christmas peak comes a few weeks after Walmart and Target. See if you can notice it here:

For Walmart (red) and Target (orange) this is not surprising. These are traditional retailers who have a fixed calendar of marketing execution with an overwhelming emphasis on Thanksgiving. After that, things ramp down.

Traditional retailers often have a fixed multi-channel schedule based heavily on past traditional media plans with less flexibility in being able to incorporate real time odd trends on the web.

But look at Amazon (blue), keep an eye on the highlighted time period above and look at this:

Notice they hit their peak exactly at a time when the Shopping category hit its peak! +25% in the first image above and +37% in one immediately above.

Amazon does such a great job that their brand queries also get an extra spike during that time, from +413% to +525%. You have to hand it to the Marketing folks at Amazon. When their competitors are ramping down (perhaps due to their inflexibility), Amazon can read the market much better (notice Christmas 2010 as well) and are well placed (thanks to Paid and Organic Search strategies) to grab all these new people who are coming into the market to shop.

And precisely at that time both their large competitors are rapidly ramping down their spend! You would think that with actual stores they would ramp up during December because Amazon is at a disadvantage having to use shipping!

Here's the link that should take you directly to the analysis in the images you've seen in this post: http://goo.gl/JbUzK

#rockbranding

Data? Check. Actions?

So what can you do with this data? How can you go and destroy your competitors? :)

I've written a comprehensive post with very specific guidance on how to leverage Insights for Search to identify actions. Please check out that post here: Competitive Intelligence Analysis: Google Insights for Search

In context of the above findings, I would focus on trying to identify the geographic locations in which unaided brand recall is stronger for my competitor(s) compared to me. I would use online and offline brand marketing campaigns to shore up my brand strength.

I would also focus on the very bottom of the Insights for Search report where you are able to see the cluster of search queries most closely associated with a brand (on the left), and the most statistically significant rising terms (on the right). They are full of specific insights you can use to optimize your online search campaigns.

Please check out the blog post above for more detailed guidance.

Five Caveats!

Life would be so much better if we did not have to caveat everything. But, sadly the life of an Analyst is imperfect. :)

Here are some caveats to keep in mind when you do this analysis…

As Google Flu Trends has proven, online behavior is a very strong predictor of offline reality. I hope you'll do this analysis for your brand, get context from other data sources, and get your company to take very smart action in moving the dial on brand strength.

As always, it's your turn now.

How does your company measure brand strength/unaided brand recall currently? How cognizant are you of how your competitors are doing? Have you tried to use online data, like Insights for Search, to do this important analysis? What other caveats would you add to the four I've listed above when using this data?

Please share your experience, critique, examples, ideas and feedback via comments.

Thank you.

Excellent Analytics Tips #20: Measuring Digital "Brand Strength" is a post from: Occam's Razor by Avinash Kaushik

I believe deeply in that quote. In my life I spend an extraordinary amount of time understanding the problem and attempting to define it clearly. As if by magic, I find that it is then much easier to find the optimal solution (or realize none exists!).

Multi-Channel Attribution is a red hot topic in our industry, and yet it is so poorly understood. I'm convinced that the resulting problems (confusion, FUD, angst, daily prayers, and wasted budget) are due to the lack of a clear framework that can help clearly define the problem.

In this post my hope is share a framework that will help define the problem clearly. Included in the post are recommendations for measurement and data analysis. And as if that was not enough, :), I'll close the post with my thoughts on digital marketing attribution models.

This is going to be a lot of fun. Roll up your sleeves, put a smile on your face, grab a pinch of common sense, a heavy dose of reality and let's go…

Three Types of Multi-Channel Attribution Problems.

A huge amount of confusion and disagreement on this topic exists simply because there is no general consensus about those three words. Multi-Channel Attribution.

So let's try and fix that problem.

There are three types of attribution problems in our non-line world.

Multi-Channel Attribution, Online to Store:

This is the attempt by Marketers and Analysts to try and understand the offline impact (revenue/brand value/butts in seats/phone calls/etc) driven by online marketing and advertising. We'll refer to this quest for doing effective attribution as MCA-O2S.

While I'm using the term Store here, it encompasses sales (or leads or catalog requests) driven to a retail store or company call center, people driven to donate blood via online campaigns, or essentially any offline outcome driven by the online channel.

An example of MCA-O2S is Verizon wanting to know how many in-store offline phone activations are driven by online search advertising, for every online activation that the same search advertising drives.

[In case you were curious... It's 5 new accounts activated offline for every 1 activated online! If you are not calculating the offline impact, and you are not giving your online channel due credit. In this case, it would be 5x less credit! You can see why MCA-O2S is supremely critical for every company on the planet.]

Here's the Post-It on which I'd sketched MCA-O2S in planning this post. The red dots represent activity we would like to ensure we are measuring to 1) ensure we understand behavior, and 2) deliver insights that will influence our marketing and advertising…

I spend a lot of time with CEOs and CMOs and when they talk about multi-channel attribution, they're invariably talking about MCA-O2S. Yet when most of my digital peers talk about this topic, they're not talking about MCA-O2S. You can imagine why things might get a little confusing.

So when you meet a CEO and they use say "Help me solve the amazing multi-channel attribution problem", you say: "which type of MCA are you interested in?" Clarity will help foster a valuable conversation.

Almost all current, hot and heavy, literature on the topic of attribution modeling does not cover MCA-O2S. That's because when it comes to MCA-O2S your only bffs are a set of 16 strategies I've outlined over two posts (links immediately below) or the fantastic world of controlled experiments (as in the Verizon case above). So less automated algorithms "distributing credit" and more thoughtful deliberative discreet measurement strategies that inform strategic decisions.

Two helpful blog posts on multi-channel analytics: 1. Tracking online impact of offline advertising. 2. Tracking offline impact of online advertising.

MCA-O2S. It's mandatory. Attribution is driven by experiments. And when you win, you win huge!

Multi-Channel Attribution, Across Multiple Screens:

Senior leaders, especially in larger companies, have started to refer to this when they use the magical words multi-channel attribution.

With the massive adoption of mobile phones and tablets we are all increasingly "four screen" people (TV, desktop, tablets, smart phones). That has directly translated into a more complex fragmented influence landscape (drives the "old timers" bananas). That in turn has translated into many senior leaders deeply desiring, as they put it, "multi-channel attribution." What they really mean is MCA-AMS.

What they really really want is to understand how individuals experience a company or government's digital existence across multiple devices, what media (advertising and marketing) they are being exposed to, and what outcomes (conversions!) are happening as a result.

An example of MCA-AMS is the ability to understand that a search I did on my tablet computer while watching a television commercial resulted in a click on a paid search ad to a camera site which logged into my memory which later caused me to read reviews of the camera on my Nexus S while stuck in traffic and that finally caused a sale for Sony when I got home and happened to be on my laptop.

Attribution in this case is the quest to apportion credit across the TV commercial, tablet paid search ad, reviews read on on the mobile phone for a "direct" conversion on the PC. Amazing, right?

Here's my sketch on MCA-AMS and the raw complexity of the customer experience that we are trying to understand… the red dots indicate what we're trying to measure and understand the impact of…

The primary challenge is that as we switch devices it is increasingly difficult to keep track of the same person as they interface with our digital existence (and are exposed to online and offline marketing and advertising). Actually, I should not say increasingly difficult, I should say almost impossible (cookies, uuids, privacy, government, et al).

Perhaps the only exceptions to the "its almost impossible" scenario would be companies that service customers who are mostly logged in (think Amazon, NY Times) across all four screens all the time. Such companies usually also own massive data warehouses where they have an ability to periodically do cannonballs into the data and identify correlations in consumption and purchase patterns. Often, though not always, they can also tease out causations between devices used during outcomes (five-second segmentation in say Google Analytics) and their media plans while focusing on customer analysis (not visitors, not cookies, not uuids, customers).

Even then it is hard, very hard. And for the rest of us this will remain a complex, and I'm sorry to be so real, unsolvable challenge. At least for now.

Some ideas from the two multi-channel blog posts above can help with MCA-AMS. I've leveraged controlled experiments to get very good "kinda sorta understanding" of reality.

I believe that real solutions will come from the evolution of cookies, updating privacy policies, government decisions and evolving user habits. All that first, then our ability to have nonline data.

Because of all of the above you can see why attribution models don't even enter the picture with MCA-AMS. But when you meet executives and they say "help us with our multi-channel attribution problem", most definitely ask the clarifying question: "do you mean MCA-O2S or MCA-AMS?"

MCA-AMS. Complex, hard challenge. Not a huge problem yet for most, but heading in that direction.

Multi-Channel Attribution, Across Digital Channels:

Almost all of the time when people in our ecosystem (unlike CEOs, CMOs) talk about Multi-Channel Attribution, this is the one they are referring to.

MCA-ADC is the effort to understand which digital marketing channels (Social, Display, YouTube, Referral, Email, Search, others) contributed to a particular conversion (or multiple conversions).

At the moment all web analytics tools, like SiteCatalyst, WebTrends, Google Analytics, CoreMetrics, and others, by default attribute a conversion to the channel immediately prior to the conversion. This is also known as last click attribution.

With MCA-ADC you are trying to go beyond the last click and get this, complete, picture of all marketing activity prior to the conversion (in this case from Google Analytics):

For this website, 767 conversions came from people who visited the site in the above precise order starting with social then a direct visit then an organic search then a referral click-through and finally one last direct visit which lead to the conversions.

The attribution bit here is the burning desire inside all digital marketers to figure out how to dole out credit for the above conversions. Should Direct get 50%? How about Social? 100%? What about Organic? 2%? But let's put that delightful thought on the back burner for just a minute while we understand a critical, often hidden, nuance. [Analysis Ninjas are magnificent at understanding nuance!]

When people talk about MCA-ADC they are still just talking about one device. Because in very close to 0% of the cases do any of these analytics tool have an idea about the behavior of one homo sapien across multiple screens (AMS).

So what you are seeing above are all the conversions that can be tied to multiple visits by a unique browser (notice I did not say person) to your website/digital existence. BTW it is fantastic that GA does this because most other tools don't even show you this.

Say, the Organic Search above had happened on a mobile phone… regardless of the digital analytics tool used, to most websites today that visit would be invisible in the above chain (cookies!). #omg

Hence it is important to separate out MCA-AMS (across multiple screens) from MCA-ADC (across digital channels) – at least for now, until the cookies, ids, privacy policies, government guidance and user habits problem is solved.

When it comes to measuring MCA-AMS you'll use the guidance provided in the above section. For MCA-ADC you'll use a different set of reports (multi-channel funnels ) and attribution models.

I'm sure you are already familiar with nuance number two when it comes to MCA-ADC. A blind-spot if you will.

The above picture does not capture what the impact of this behavior was on your offline existence (O2S). Web Analytics tools are not awesome at that. Ok, they stink at it.

So it is possible that an additional 3,835 people went and made purchases in your stores or via your phone channel (taking the Verizon numbers from above). That would also be invisible from the above report. None of the channels above, whether glorious social, beloved direct, magnificent search, sweet referral, would ever get "credit." Unless you are willing to use the methodologies outlined in the MCA-O2S section above.

When you talk about MCA-ADC, ensure that you are aware and communicate to your leadership, that you are not reporting on MCA-O2S (online to store) and it is extremely unlikely to be reporting the impact of MCA-AMS.

Here's one last Post-It sketch. The red dots are what you are likely measuring when you attempt MCA-ADC…

And if I wanted to be pedantic I would say it is really MCA-ADCFOD. Multi-channel attribution across digital channels for one device.

Now it is true that with sufficient analytical skills, time, patience, and God's direct blessing to you, it might be possible to do complete multi-channel attribution analysis where the multi-channel includes multiple online ad channels, behavior of the person across devices and the impact online and offline. Sadly, that is incredibly hard to do as a whole. And when I say incredibly hard, I mean almost impossible. And when I say almost impossible, I mean only attempt that after you know you've fixed all other problems with your advertising, your online and offline existence and your people. All three.

I know that sounds like a bummer, but a dose of reality is particularly needed in this discussion. There are simply too many fake promises being made by vendors, consultants, tweeters, gurus and fairies. That is unhelpful to the entire ecosystem.

To close this section…

Next time you hear someone utter the words multi-channel attribution, the single greatest gift you can give yourself is to ask in your sweetest possible voice: "Are you referring to MCA-O2S, MCA-AMS or MCA-ADC?"

You'll earn their respect for knowing that there are three types, and you'll be able to put into context what they are asking for and proceed to have a career and business-enhancing discussion.

Multi-Channel Attribution Models.

For MCA-O2S and MCA-AMS, it is a complex undertaking to identify "which advertising/marketing vehicle deserves how much credit." It requires patience and skills. And it requires your execution of multiple of the 16 strategies I've outlined for tracking online impact of offline and offline impact of online. Even more, it requires an ability (people + skills + desire) to execute controlled experiments.

So the question "who deserves how much credit" is tertiary at best.

With MCA-ADC that quest is a little bit easier. We have the multi-channel funnel reports at our disposal. Additionally in some tools we also have an ability to apply attribution models to the behavior you see in the two pictures above in the MCA-ADC section. #sweetness

The most common attribution models bundled into even the simplest web analytics tools are: Last click, first click, and even distribution.

If you are lucky, you have access to a more sophisticated tool which would include: Adjustable, based on mathematical algorithms, time decay model.

If you are among the chosen few, you'll likely have access to a digital analytics tool that allows you to create a customized attribution model.

Each of these models are applied to MCA-ADC (still without benefit of O2C or AMS) and provide you with incrementally better understanding of your digital media spend.

Each of these models comes with its own pros and cons. [If you have my book Web Analytics 2.0 please jump to page 358.] Some of them have more cons and barely any pros. Those should be avoided like the plague.

A couple of them pass the common sense test, and hence will put you in a better place than staying with last click attribution.

But most of what you'll get out of playing with these models is a deep and profound appreciation for how they'll, even in their most shining moment, give you directional guidance how to adjust your media spend (shift dollars/euros/pesos from Search to Display or from Display to Email or… other combinations).

You'll realize (even if you use the greatest customized model created by your most magnificent consultant at a equally magnificent cost to you) that success then will come not from that rough output, but rather from your ability to take that rough output, make changes, observe the impact (over weeks, or months if you are small sized), identify insights and be less wrong over time.

If you happen to be in a larger company, say you spend more than $10 million on digital marketing per year, you'll quickly see, having learned to be less wrong over time, that the question you want to answer with multi-channel attribution modeling is not "who gets how much credit" but rather "how can I optimally balance my digital marketing portfolio."

That will then drive you to seek solace in the arms of the only solution that actually works. The solution that is hard. The solution that requires unique people skills and an undying desire to scale un-imagined heights of glory. Media Mix Models. Executed via persistent controlled experiments.

When you reach that point, fame, fortune and happiness will be yours.

Multi-Channel Attribution: Closing Thoughts

This is a tough challenge. Simply because reality is complicated.

Customer experiences are ever more complex, influence channels intersect a lot more, content consumption is fragmented, the three-step "attract, acquire, retain" model is now broken into 37 different pieces.

So, you don't have a choice. You are going to have to deal with the multi-channel attribution problems, all three of them, if you want your company to have an effective advertising and marketing strategy.

Here's the good news: You don't have to try to boil the ocean in one go. In fact, that might be hazardous to your health if you attempt to do that. Take gradual steps. Increase your sophistication over time.

Here's what I recommend:

1. First clarify what problem you are solving for your management team. O2S or AMS or ADC.

2. Use the appropriate set of solution (see sections above). If MCA-ADC…

3. Get really, really good at understanding your multi-channel funnel reports. They are free. They are awesome. Use the Venn diagram in the Overview report to display reality to your management team. They'll love you, and stop wasting money.

4. Start to experiment with the simple models. You are moving away from last click, you'll abandon first and even very quickly. Spend some love and attention on the time decay attribution model (ideally with several mathematical options to apply).

5. Experiment with changes in your digital portfolio based on your time decay results.

6. Measure outcomes. Go back. Analyze the data. Change some more.

7. As you master that, shift slowly to playing with media mix modeling type controlled experiments.

If at any step you notice diminishing margins of return, go back to the previous step and optimize that one some more until it is truly worth the incremental company investment to take the next step.

If you understand the frameworks, if you internalize the challenges, if you define your company's immediate unique problem clearly, and follow a step wise approach outline above you'll not just do fine. You'll be fantastic.

Good luck!

As always, it's your turn now.

Which multi-channel attribution problem are you solving in your company? Do you distinguish between the three outlined in this post? Is there a fourth one not covered in this post? Which one do you find to be the most challenging? Are you more optimistic that we'll solve AMS (across multiple screens) than I am? Which MCA-ADC attribution models do you swear by? Who's your BFF? Do you have a attribution model that's not covered in this post?

Please share your thoughts, feedback, critique, and brilliant new ideas via comments.

Thank you.

Multi-Channel Attribution: Definitions, Models and a Reality Check is a post from: Occam's Razor by Avinash Kaushik

So what is big data really? No one quite knows.

As I interpret it, big data is the collection of massive databases of structured and unstructured data. The data sources include traditional (now considered puny) sources like corporate ERP/CRM systems and non-traditional (massive) sources like every technical ping from every human or mechanical sensor, all web behavior by everyone across the entire Internet, increasingly digital data from analog sources like hospitals or the atmosphere, and (good lord!) our collective tweeted wisdom.

That is a lot, right?

Because so much of the big data talk is focused on the promise of zettabytes of data, big data also tends to be about massively parallel computing, fantastic storage systems, the "cloud," Hadoop and MapReduce and other such deeply technical delights.

That explains why so much of big data talk comes from Oracle, IBM, Microsoft, SAP and other vendors. And not so much from practitioners, yet.

I believe in the promise of big data and the awesomeness of the insights that can come from it. But that should not come as a surprise. All the way back in 2007, I was evangelizing the value of moving away from the "small data" world of clickstream data to the "bigger data" world of using multiple data sources to make smarter decisions on the web. Clickstream + qualitative data + rigorous statistical analysis of outcomes + deep mining of data from competitive intelligence sources + rapid experiments + more.

Here's the "bigger web analytics data" picture from 2007… Multiplicity!

The big data we are dealing with today puts the 2007 picture to shame. We have even more types of data, becoming ever more complex, distributed across multiple existences, and we are left with the task of parsing out terabytes of noise to get to a megabyte of signal.

That last part is what I love to focus on, what I worry about, what I think everyone should focus on. It is great that we have big data. It is greater that we have such amazing promise in that big data. It is sucky that almost no one knows what to do with it in the context of driving actual business value.

Hence my interest in big data is not about the zettabytes or Hadoop or unstructured variables or one of the n technical things that seem to dominate big data conversations.

My interest is deeply and passionately rooted in trying to figure out how to ride big data all the way to the bank (or world peace). How to find insights? How to structure organizations that will use this data to ensure that they get timely value from it? How to drive action? How to find frameworks that force a different type of thinking so we don't make the mistakes we so brilliantly have made in the world of small data?

If we don't answer all those hows big data will be a big disappointment.

Avoiding big disappointment and the hows were on my mind as I prepared my keynote for Strata 2012 Big Data conference . My goal was to take my TED-ish 15-minute timeslot to present my perspective on why driving big action was the big imperative for big data.

It was an incredible challenge, thanks to Strata co-chairs Edd Dumbill and Alistair Croll. In this post, I want to share the result with you.

I'd structured my keynote into three big pieces:

Here's the keynote…

[You can also watch this video on YouTube. You're also welcome to Like, Share, Tweet, Facebook, +1 it on YouTube as well.]

It is not my hope to encourage you to copy/paste the strategy outlined, or to use the tools shown.

My hope is to simply inspire you to think a little differently about organization design, share a framework to influence the focus of your analysis, and find the types of practical solutions that will really spark profitability from all this big data.

I welcome your feedback and thoughts on the video and the solutions via comments. Please also share your experience with big data. Any big or small success you've had would be inspiring to all of us.

Preparing for my keynote also got me thinking about all the implications of big data and my own longish career in trying to create superb decision support systems. The database has moved from my floppy disk (true story) to an infinite storage cloud, yet, amazingly, some of the biggest challenges have remained the same.

So big data revolutionaries…

Six Rules That Should Govern Your Big Data Existence.

Here are some rules from my experience in the small data world that I've come to believe also apply to the big data world, perhaps even more so. As you go about your big data journey you'll meet with even more immense success if you consider these valuable life lessons:

1. Don't buy the hype of big data and throw millions of dollars away. But don't stand still.

2. Big thinking about what big data should be solving for is supremely important.

3. The 10/90 rule for magnificent data success still holds true.

4. Shoot for right time data, not real time data.

5. "Data quality sucks, just get over it."

6. Eliminating noise is even more important than finding a signal.

Six simple rules for you revolutionaries to follow to ensure, well, revolutionary success.

Notice, none of them have to do with hardware or Hadoop. One important reason is that I'm solving for the CEO and not the CIO/CTO, so it is a matter of perspective. The second (main) reason is that we do face some big data technology challenges for now, but the things that will determine if big data will deliver big value have nothing to with technology. They have to do with the six rules above.

If you are really thinking big data value, think CEO and not CIO/CTO. It will dramatically change the focus of your work, in a good way.

As always, it's your turn now.

Did you find the keynote to be of value? Did you find the framework to be of value? Will it drive you to change your approach to big data? With regards to the rules above … is there one rule above that is your favorite? Is there one that should have been there but is missing? What is the biggest big data advice you would share from your experience?

Please share your wisdom, recommendations, and feedback via comments.

Thank you.

A Big Data Imperative: Driving Big Action is a post from: Occam's Razor by Avinash Kaushik

I mean really use the tools. Ravage all the features. Exploit every possible button. Produce built-in visualization magic. Poke into the hidden crevices and discover exotic delights. Nourish yourself with the "info snacks" the tool's engineers and product managers cooked up.

This post is all about that.

When it comes to data analysis, you are usually more likely to see me share guidance on advanced segmentation or custom reports or advanced social metrics or controlled experiments or economic value or competitive intelligence or web analytics maturity or one of an infinite number of difficult, if hugely rewarding, things.

Not today.

Today is going to be about healing heartbreak. Ravaging data. Poking and prodding. Nourishing ourselves. And doing so with simple mouse clicks inside the standard tool interface (!) with the reports and features you can already access.

Here is a summary of the eight incredible recommendations in this post:

#1. Create a Customized Dashboard – Earn Love, Drive Change

#2. Leverage Custom Alerts – Let Data Kick Your Butt Into Action

#3. Use Table View Options (Comparison, Pivots, In-line Filters) – Faster Initial Insights

#4. In-Page Analytics – Re-imagine Traveling Through Data

#5. Perform Recency, Frequency & Pan Session Analysis: Fall in Love with People not Page Views

#6. Matched Query Type, Keyword Position, Day Parts: Sexier PPC Analytics

#7. Custom Report Filters, Tabs: Bring Deeper Relevance To Your Custom Reports

#8. Quit Google Analytics: Move Beyond Tool/Creativity Limitations

If you are an Analysis Ninja, focus on the mental model and approach used in each recommendation. If you are an Analysis Ninja in-the-making, close the door to your office/room – you are going to repeatedly squeal with delight.

Ready?

#1. Create a Customized Dashboard – Earn Love, Drive Change!

Who does not love dashboards? Humans love them. Aliens love them. HiPPOs adore them.

So why is it that we don't spend time creating customized ones for our stakeholders? After all, humans, aliens and HiPPOs have different needs.

Pledge to shift away from a one-size-fits-all data puke, and use your web analytics tool to create a customized dashboard.

One day, Google Analytics will default to be the Home tab when you log in, but until that blessed day arrives, just click on the Home icon in the orange top navigation. Then click on Dashboards, and what do you see? Oh yes! + New Dashboard. Click!

I love that phrase "Blank Canvas." So open. So full of possibilities. So much hope and wonder.

Now just because you can do anything does not mean you should. My process is to name the dashboard first. Seems odd, right? But by naming it, I am giving it a purpose; and a purpose requires asking questions and focusing. And great, relevant, dashboards spring from asking questions.

I named my dashboard: VP, Digital. It now has a specific audience and a purpose. Rather than data puking, I'm now forced to go talk to the VP of Digital and ask this question: "What are your business priorities for the next six months?" That will lead to: "And how will you know if we've successfully executed on priority x?" That will lead to: "Awesome, I know exactly which critical few Key Performance Indicators I'll be showing in our dashboard."

Boom!

Every element in the dashboard has a purpose and is tied to a business priority. She/he wants more Social traffic. You, the Ninja that you are, are showing all segments of traffic to give context (you rock!). She/he wants time on site, you have no idea why, but you add it (along with a sparkline that shows the trend – sweet!). It is a content site, so rather than silly things like page views you use Loyalty (more on this below) and you also show consumption of videos (events). Finally, you bring together Conversion Rate with the Goal Value delivered by the Social obsession.

Charming!

[Update: If you would like to download the above mentioned dashboard into your Google Analytics account please click on this link: VP Social Media Performance Dashboard.]

Pro Tip: Always, always, always let the Acquisition, Behavior and Outcomes framework be your guide. After you've created a dashboard, check to see that you have all three elements. If you don't, you are not showing the end-to-end picture. Without this you fail in your duty (and the data recipients will make poor decisions).

Create a customized dashboard for your Search team, one for your Display team, one for the folks doing onsite merchandizing, one for the nice lady that owns the ecommerce shopping cart and all the other key clusters of your audience. Give them hyper-relevant starting points, collections of "info snacks."

The cool bit is that in addition to standard widgets and simple tables, you can also bundle along your smarts into the dashboard and delight your users.

One way is to use the awesome built in inline Filters feature when you use the dashboard widgets, to show just the data that is relevant (did I already say less data puking? :).

In this case, I've done that by adding a filter to segment revenue to only show social value.

And it is not all social media, it is just the money made from the company's own social media efforts by using the right campaign parameter. I'm (secretly) trying to show the VP how much (or how little!) money our own efforts are generating. Smart widget, smart insights, smart decisions.

So go forth and multiply! Create a small cluster of hyper-relevant (secretly smart) dashboards!!

#2. Leverage Custom Alerts – Let Data Kick Your Butt Into Action!

Sometimes (actually frequently) it is not enough to rely on our own diligence in terms of remembering to log into SiteCatalyst and look at the right set of numbers (across a hundred reports!) to know what's up with the business. It is especially undesirable to be surprised about something awful happening to our digital existence.

We can't predict the unknown unknowns easily, but we can be magnificent at proactively identifying the known unknowns by leveraging the custom alerts feature in our web analytics tools. Here's a screenshot from Google Analytics:

These alerts will let you know if engagement on your website crosses certain thresholds or when the bounce rate spikes for traffic from Google or if there is a spike in conversions (praise the lord!). All things you know will happen, you just don't know when. Known unknowns.

With smart alerts set, you don't have to remember to check the data every eighteen seconds. An email, or a text message, will poke you into action. Your boss will be impressed at how you seem to always have your act together!

Here's one of my favorite custom alerts. I would like an alert when goal conversion rate for any day is greater than 25%. My normal is around 18%, so if it jumps up by that much I can get an alert and I can do deeper analysis to figure out what might have caused the spike.

You pick the period for comparison, your the necessary dimension and metric, add the condition, type a value and you're in business.

If you don't have at least five custom alerts set up, you can't call yourself an Analysis Ninja in training. At least not a serious one.

Five of my favorite alerts are in the second part of this blog post: Identify The Known Unknowns: Leverage Analytics Custom Alerts Here are more clever examples from the team at Google: Five Custom Alert Examples

Don't rely on yourself to remember to look for your site’s magic moments. Put yourself in position to be proactively informed when they happen.

#3. Use Table View Options – Faster Initial Insights!

Enough dancing around the outside of the tool. Let's rip off our clothes and jump into the cold inviting water!

It is very hard to quickly understand a lot of numbers when they are presented together. When you log into WebTrends or Google Analytics or CoreMetrics, you're lucky if the standard report does not contain five or seven metrics at the very least for every table row. Data puke!

Not only will you not see the forest, you'll be lucky to even see the trees.

My preferred path is to leverage the tool's built-in features for filtering/visualizing the data.

In Google Analytics there are a few super cute options. Click on the table like icon next to View. You can see five different ways to look at the data in any table: Percentage, Performance, Comparison, Term Cloud and Pivot. All exist to make your life easy.

My personal favorite is Comparison. This option takes the site average for a metric and compares the individual performance of every row to that average, and it visualizes the data for you.

For the top websites that refer traffic, I wanted to know quickly (without having to do the math) which source sends traffic that tends to see more than one page. AND I want to know contextual performance of every row with site average AND every other row. Hard? Nope. I simply choose Comparison. Then I choose Bounce Rate. And in two seconds…

Like every two-year-old child, I know that red is bad and green is good. GA is telling me is that Twitter (t.co) traffic bounces 14.59% more than site average. Ouch.

Scanning the rest of the table, remember I want contextual performance analysis, I can quickly see that I should love the GA blog, Linkedin and SEOmoz more and other folks a little less. :) But I am also now a lot more curious about Ycombinator. That is a lot of traffic. What post on YC did they come from? What content did they read here? Why might they not have cared for anything else? I can analyze and then identify an specific optimization/engagement strategy to reduce bounce rates.

You can literally do this for any metric in the standard tables in GA. Try to look at your top 25 campaigns and compare conversion rate. Or open the new search engine optimization reports in Google Analytics , for your Queries look at Impression and try Comparison for CTR.

Pretty cool. But that is not all.

I've always been partial to pivot tables in Microsoft Excel, hence it is not surprising that my second favorite view option in Google Analytics is Pivot.

Now I can create a lovely report, for example, to find "arbitrage" opportunities across search engines? Here's how you do it.

1. Go the keywords report (in Traffic Sources section). From View choose Pivot (as above).

2. Click on the box next to Pivot, type in Source, select it.

3. Click the box next to Pivot metrics and choose Visits (or whatever else you like, go crazy!).

4. Look at the performance. I typically look for anomalies. For which keywords do I get more traffic from Bing when compared to Google. Or Yahoo! compared to Ask, etc.

Every search engine's SEO algorithm is unique. For example I get twice the traffic for "digital marketing" from Bing than from Google. I use the data above to customize my SEO strategy for each search engine.

You can use pivot tables in pretty much every GA report.

In this case, I can more easily figure out which of my top pieces of content are delivering the micro-conversions that are valuable to me. I track these micro conversions as Events, here's my Pivot table:

Use your creativity when it comes to pivot tables and you'll be delighted at how wonderfully they help you answer hard questions.

One last bonus item when it comes to using tables in web analytics tools spectacularly: Use the in-line table filters. Just click on the link called advanced next to the magnifying glass on top of the table you are viewing (in any report).

Now, rather than looking at half a million rows and trying to find an answer, you can simply type in your question. In this case I only want the rows of data (keywords, campaigns, pages, products purchased, videos watched, whatever) only for those people who:

1. Saw more than 3 pages during their visit AND

2. Entered my website on the cluster of 900 pages about Aruba.

These people are of particular interest to me … I click Apply and, voilà, I have them cornered!

Using this strategy I can go to the standard table with hundreds of thousands of rows of data and quickly only look at data for my brand keywords or just for my email campaigns or just for people who visited more than 10 times or just for those who came via Yandex or just those that read a segmentation post or just those that donated or…. anything. And I can do it fast.

Why stare at a table, or worse just the top ten rows, wondering what to do? Speed up your time from data to information by using the Comparison view, Pivot tables and in-line Filters.

#4. In-Page Analytics – Re-imagine Traveling Through Data!

This is one of the hidden gems of Google Analytics, especially for traversing lots and lots of data in context of the web page itself. It is fantastic at communicating data, complex data, to people whose primary job is not data analysis.

The In-Page Analytics report takes all the data you would find in the Explorer and Navigation Summary reports (essentially all the links you have on a page and their performance) and shows it to you in an elegant visually appealing view.

There are two ways to get to this report.

1. Just go to Content > In-Page Analytics.

2. Go to Content > Site Content > Pages, then click on the URL you want (or use the in-line table filter mentioned above to find the URL), and click on In-Page at the top.

On top of the report you'll see the scorecard, or aggregate performance of the page via metrics like Pageviews, Unique Pageviews, Time on Page, Page Load Time (!) and Bounce Rate. Having the % of Total (grey text, small font below) provides great context.

Below that, in blue, green, red and orange I see the percentage of clicks on each link. I don't have to infer data in the table, it is all laid out for me nicely!

And note the orange bar at the bottom, it is particularly nice. It shows how many people click on links below the fold. The fold is defined by your browser size. As you resize the browser windows you'll see that number dynamically change. This data is extremely valuable for long pages, especially if you have valuable links below the fold. IF you're New York Times or Amazon, you want to know if people scroll!

This is so important if you are responsible for merchandizing. If you have a few different layouts of your web pages, this is a great way to know which links, promos, and annoying dancing banners are attracting the clicks.

But you don't have to watch clicks. Aren't clicks are the new HITS :).

You can click on the Viewing drop down (#1 below) and choose any goal. When you choose a goal, the display changes to show what percentage of people who click on a particular link go on to complete a goal in that same session!

In my case, below, 15% of the people who click and read the comments end up meeting my goal of going to Market Motive (and hopefully sign up for the Web Analytics Master Certification program!). But only 1.9% of the people who visit the Digital Marketing section of the blog do the same.

In this case you can also see that the links on the top are especially valuable for this goal. Only 9% of the people who ultimately went to Market Motive clicked on any links below the fold (and the fold here is pretty much the top of the blog post!). So I have to be particularly good at the information architecture on top of the page. Once they scroll, the chances for goal conversion go down dramatically.

I can do this type of "conversion click" analysis on any of my 8 goals. How awesome is that? With those insights, I can go and optimize my key pages for my individual business goals.

Imagine what you can do with your home page optimization if you know this. Now when everyone wants a link on the home page or the category pages you can show them which links your visitors are actually interested in and let data fight your political battles!

I rarely find anything really sexy (in an analysis context :) unless it comes with segmentation. You saw that in every single recommendation above. And my choice for this report is no different. You can segment like crazy.

When I use the In-Page Analytics report I don't want to look at all the traffic in one ugly bucket. I want to analyze groups of like type people, like type behavior. For example, I want to know how the behavior of search traffic is different from direct traffic. How hard is it? Three simple clicks…

1. I click on the Advanced Segments drop down and choose the standard segments (or one of my 50 custom segments).

2. I click on the In-Page tab to go to the report. (I was in the Pages report.)

3. I choose the metric I want. In this case I, selfishly, want to know if there is a difference the money I make (Goal Value) if Visitors from Search and Direct traffic click on the exact same link on the page.

4. Bam! Bam! Bam!

There is a substantive difference. When people come from search I make $142, on average, when they click on that link, but if they are direct I only make $58 (boo!).

Imagine what a gift this is when it comes to figuring out how to create the best landing pages. I know what the Search Traffic gravitate towards, I can now optimize their experience on the site rather than serving them random/generic links!

You can do this analysis for social media visits, for a particular keyword, for people who watch videos or download catalogs or, well, anything you can segment in Google Analytics (which is pretty much everything).

Forget tables. Be sexier. Let your site tell you what to do.

But there is one fly in the ointment.

The implementation of In-Page Analytics in GA is frustrating and silly. When you first go to see that report (if you are using Internet Explorer), you are going to see this insane warning:

If that box was not scary enough, the whole darn text is wrong. My ga.js (and most likely yours) loads from Google, and I have the snippet on my site. #aaaarrrrrhhhhh

In addition to the above you'll also see this at the very bottom of your browser window at the same time…

So, how do you make this report work?

It is supremely annoying that the Google Analytics team and front end does not make that clear.

But it is simple. Ignore the first error, and click the "Show all content" button on the second error. Magically, everything will work.

If you are using an older version of IE you might see this error:

Classic useless error. Don't click the default Yes – just click No and the report will work fine.

In Chrome, mercifully, it works fine with no errors.

While it is disappointing that the error shows up initially, the report itself, as you can see above, is quite valuable. I hope you'll give it a chance.

#5. Perform Recency, Frequency & Pan Session Analysis: Fall in Love with People not Page Views!

I'm a big fan of pan-session behavior. What happens across multiple visits by the same person? (And are there multiple visits at all in the first place?)

Having grown up in the traditional business intelligence and direct marketing world, I'm also a huge fan of RFM analysis .

In Google Analytics, you'll find them in the Audience Section under Behavior.

Here is a great example of the type of business-critical question you can answer with these reports. We are a photo-sharing website (think little sister of Flickr ). We make money on content consumption (via display ads) and premium subscriptions to the site. But we can only make money if other people come and upload their photos, and still others come to view those photos. Long-term success is achieved if our audience becomes loyal and we don't have to keep spending money on Google and MSN and Yahoo! renting traffic.

So, are they loyal? Check out the Frequency (count of visits) report. It shows how many people visited only once (42%) and how many 2 times and 3 times and… so on and so forth.

For this business the results are fantastic:

While a chunk of people come only once and never again, notice how bottom loaded the report is. 43% of the traffic comes to the site between 9 and 200 times in a month! That is loyalty! We can feel better about our marketing and engagement strategy.

How about for your site? Are you having one-night stands or building longer-term relationships with your audience?

Another nuance of loyalty is that you not only want people to come to the site multiple times, you want a shorter gap between two visits. You're looking for recency. This report show us how spectacularly we are doing for our photo site:

The vast majority of visitors visit the site every day! Analysis Ninjas know that the 83% number above includes new visitors to the site, so we should subtract that (why are web analytics tools so annoying some times!). But, it is still a huge number, and we should be happy.

How about for your site? Does the recency line up with, for example, the rate at which you publish new content/launch new products/execute new marketing campaigns?

Another facet of pan-session analysis is looking at the number of visits it takes to convert our visitors. Not everyone wants to marry you on the first date, right? (Yet almost all digital marketing and almost all landing pages are constructed as though this were the case. Sad.)

My favorite report to use to answer this question about customer behavior is the Path Length report in the new Multi-Channel Funnels section in Google Analytics.

In our case, around 23% of our conversions happen in the first visit, and then there is a long tail and then look…

OMG! 48% conversions that took 12+ visits to convert! We can specifically look at that segment of customers and figure out what combination of Google, Atlas, YouTube and Email Marketing (or whatever) it took to get that conversion!

We can use this data to create better experiences for our users. We can optimize the ads and marketing messages (across channels) it took to get these folks to come to our website multiple times, prior to conversions.

This is hard work. Most definitely senior Analysis Ninja work. But that is how you win big. When you skip this type of analytical effort, you doom your company to live on scraps. And really, who wants that?

#6. Matched Query Type, Keyword Position, Day Parts: Sexier PPC Analytics!

I've always been a bit miffed that most web analytics users are less than sophisticated when it comes to analyzing search/AdWords campaigns. So many companies spend so much money. Why not do some incredible analysis? Especially when our web analytics tools make it so easy.

My first example is a good representation of that.

Most people don't realize that when you view the keyword report in the AdWords section, you are looking at the key words/key phrases you bid on, not the queries that were typed by users into Google. If you base you AdWords success on just the keywords report, you might end up making substantially poor decisions.

For that reason, I love and adore the Matched Search Queries report (in the Advertising section). It shows what users typed into Google when your ad was served. The report is standard in Google Analytics.

All you have to do is click on the box next to Secondary dimension and type in Keyword. Now you are looking at both the word you'd bid on (right) and the word the user typed (left):

You can quickly see the differences between your bid and the matched query (#2 above). The next obvious step is to look at the performance and optimize your Match Type strategy based on the results.

In the screenshot above you can see that the keyword bid on was "calico critters toys." Those ads were matched to the user queries "little critters toys" and "calico critters cloverleaf manor." And there was a 9 points difference in the bounce rate (ouch!). Good to know. Go back, optimize your match types in AdWords and optimize your landing pages.

Fun right?

My second favorite? Keyword Positions report. Why? SEOs obsess about their rank on the search engine results page (SERP). That obsession is often valueless. But for your PPC campaigns? Obsession will deliver glory!

So why not analyze which position your ads show up in when it comes to AdWords?

A combination of your max bid, your quality score, match type will determine the position of your ad for every search query. Google Analytics will show you that information beautifully.

Here it is…

Just click on a keyword and the visualization on the right comes to life. Now you are better able to determine which position gets you the most clicks. Top 3 is better than Top 1 (the position your boss was obsessed about – "I WANT #1 RANK!!"), and neither can beat Side 1 (the cheaper position!).

Another lovely thing you can do with this report is look at the performance once those clicks (ok, people) land on your website. Just click on the down arrow and choose the metric you want, Bounce Rate in my case below:

You can see that every position has a bounce rate. Side 1 still has the best performance. You don't have to just use Bounce Rates. You can also use % New Visits, Time on Site and Pages/Visit as your metrics. The goal is still the same: find the position that delivers best performance.

If a position works optimally for you, then you can use AdWords Automated Rules to have your ads show up in particular positions.

You use your money wisely and get higher ROI. #winning

One small bonus tip: I love looking at the AdWords Day Parts report a couple of times a month. Most of the time, the data shows the normal trend, more clicks and conversions during the business day.

But every once in a while for certain keywords, or segments, I'll discover that the pattern is very different. For example, you can see below that the conversion rate actually peaks at midnight…

We did not know that people were searching for us late in the night, and they were highly qualified (!). Hence sadly our AdWords budget was capped at that time, we did not to "waste" money. Sad. Once we saw this data we loosened up the budget and picked up loads of extra conversions.

You'll discover other delights like this. In the view above I'm using the Compare Metric feature of Google Analytics. It is cleverly hidden in light gray text on white background on the top right of the main graph in every report. Just click on the drop down and choose the comparative metric you want.

If you spend money on AdWords, be smarter about the analysis you do. There is no better way into your boss's heart. If you spend money on other types of campaigns, I hope you'll find inspiration above to do interesting off-the-normal analysis.

#7. Custom Report Filters: Bring Deeper Relevance To Your Custom Reports!

It is hard to keep pace with all the changes that web analytics vendors make to their tools. I wanted to share two clever features in Custom Reports that make them even more super magnificent (and mandatory if you are a Ninja!).

The first one is the filters that are built right into the custom report you are creating.

I love custom reports because you don't have to data puke any more, you can just show the data that is needed. [Helpful post: Leverage Custom Reports For Better Insights]

Now you can focus even more by embedding the segments your leadership cares about right into the report!

Above is my awesome Visitor Acquisition Efficiency Analysis report (click link to get it). But if my leadership team is only interested in understanding how good the company is at acquiring mobile traffic, I can include a filter right into the report (see above) to just show mobile traffic.

And if they only care about USA (and why not?), I can limit my custom report to show just that. Why bug them with everything?

Now my custom report is not just relevant, it is hyper-personalized. I have shortened the distance between data and insights.

Your imagination is the limit in terms of the clever filters you can build into your custom reports.

Second tip on custom reports: Create micro-ecosystems.

I was not too pleased with the eight or ten standard mobile reports and their data views and all that. So, why not create my own custom report? Wait, not just a custom report but rather replace all the standard reports with my one Awesome Mobile Report? [Click to grab it!]

My primary strategy was to create three tabs. One for device drill downs and metrics, a second one for search performance, and a final one to understand performance of content:

Each tab has specific metrics relevant for just that dimensions (Device, Search, Page), and it is all in one place to give decision makers one go-to place for all their mobile performance needs.

Same outcome: Faster movement from data to insights.

You'll know you are an Analysis Ninja when you can replace 100% of your company's reporting needs with just five such micro-ecosystems. (Not 100% of the analysis needs, 100% of the reporting needs.) It is entirely possible, and think of how easy your life will be then…

And I have to tell you it is a tremendous amount of fun.

One final, surprising, way to do the data hustle with GA…

#8. Quit Google Analytics: Move Beyond Tool/Creativity Limitations!

Sometimes all the reports and features are simply not enough.

You can't understand why it is impossible to see Keywords in rows and a monthly count of Visits in columns. Weird, right?

You can't fathom why something so amazing and straightforward as tag clouds are so uncool and utterly useless in Google Analytics.

You are frustrated with the insane report/table formatting requirements by your business leaders. They want a particular font type, or your dashboard goes into the junk folder!

When you run up against the tool's limitations, weird implementations by tool vendor, or hard-to-please clients… quit the tool. Get the data out. Unleash your creativity.

It is, of course, possible to take data out of Google Analytics. The straightforward way is to simply use the Export button in the top nav.

The problem is the second image above. You can only download 500 rows easily, when you actually, in this case, have 122,397 rows of data. [And you all know how much I love mining the long tail by moving beyond the top ten rows of data! Not possible with 500 rows.]

Option one is simple, yet slightly painful: "Trick" GA into giving you all the data that you want to download.

Happy?

Now here's the caveat.

Using the method above it is possible to download all of the 122,397 rows of data. The challenge is that you might not have enough cache allocated to your browser. Or you don't have enough memory. Or you might have an older browser. Or one of so many things that will cause your browser, not the web analytics tool, to hang. It is just hard to get that much data rendered into a browser.

Of course where there is a problem, there is an incredible solution.

If you want to export all your data frequently just use the free Google Analytics API. It is pretty cool. [Tools like WebTrends and Adobe have APIs as well. WebTrends is free, for Adobe API pricing please call your Account Rep.]

If you want to have a quick naughty flirtation with the GA API, visit the Data Feed Query Explorer. If you enjoy that (and you will, because that is what naughty flirtation is all about) get more context about the Google Analytics Core Reporting API. End your journey devouring the handy dandy Dimensions & Metrics reference guide.

Now allow your inner geek to rejoice!

If, like a majority amongst us, you want to skip the flirting and jump to marriage, mosey over to the Google Analytics Application Gallery. Everything you can dream of is there. Data Warehouse integration? There. Business Intelligence? Got it. Campaign Management with a side of Email Marketing? Sure. Mobile Apps and Widgets and Gadgets? Absolutely!

It is pretty cool to use the API to integrate your offline phone call data with your Google Analytics data, understand the demographics, gender, income, etc. of people who come to your site, or overcome the sub-optimal standard GA Funnel report by using PadiTrack.

Going back to extracting data efficiently and making magic, three apps you'll find particularly useful are Excellent Analytics , Nextanalytics and GA Data Grabber.

Excellent is free (hurray!). Nextanalytics costs $199/year and GA Data Grabber costs $299/year. Both tools are full of pre-built dashboards, reports, cool visualizations and easy ways to collect data from tons of sites and pull it all nicely into one report. Both also contain loads and loads of automation capabilities. They allow you to shift from 90% data collection and 10% actual work, to 10% data collection 70% data analysis 20% social media time-wasting. What's not to love? :)

It may seem odd to spend money on a free tool. But not paying just one dollar a day to make your life better is most likely a Class 1 analytics crime. Don't commit crimes!

Regardless of if you use WebTrends or Google Analytics, the API allows you to do better reporting, smarter analysis (with offline data) and automate the mundane. Create a better life for yourself.

So that's it.

Eight simple ways you can hustle with data, convert skeptics, earn the love of your website visitors, and improve profitability of your web business. All without leaving the confines of standard reporting features already inside your tool (except that last tip).

I hope this post will accelerate your mastery of Google Analytics (or IBM or Yahoo! Web Analytics or Open Stats). And I hope it will mean less time spent wrestling data and more time taking action based on intelligent insights.

Good luck!

As always, it's your turn now.

Are the strategies outlined above already a part of your daily data hustle? Which recommendation surprised you the most? Which one do you think is most over-rated? If you are a GA power user, did I miss a feature or approach that you love a lot? From your experience, with any tool, do you have a tip to share with your peer readers?

It would be wonderful to hear from you. Please share your feedback, ideas and awesomeness via comments.

Thank you.

Google Analytics Tutorial: 8 Valuable Tips To Hustle With Data! is a post from: Occam's Razor by Avinash Kaushik

In this blog post we are going to bring the sexyback. I am going to attempt to significantly simply your life by recommending the critical few metrics you should use to analyze performance of your digital marketing campaigns and website. You'll be able to quickly go from "omg what can I do!" to "omg what am I going to do with all the money and fame I'm earning!"

The approach I'm going to use is to 1. Use my Acquisition, Behavior and Outcomes framework to ensure an end-to-end view of important activity and 2. Recommend metrics / KPIs you can use based on the size of your company.

Each recommendation comes with hints on what analysis to perform once you have the data, and what changes you could make to your campaigns, content and overall digital strategy. [A summary in pictorial format is at the end of this post.]

Excited? Let's do this!

Best Metrics / KPIs for Small Business Websites

Small business websites are a very fragile ecosystem. People working hard to do the best they can on the smallest possible budgets. But not to worry. They have to start with just four simple metrics to start rocking!

Acquisition:

Clicks? Visits? Backlinks? Impressions? No. We have something magnificent.

Cost Per Acquisition.

Obsess about this metric. You have very little money. You need to know, obsessively, what you get for it. This metric delivers that insight. Oh, and everything has a CPA (not just your paid search or display/banner ads). If you are doing SEO then you are likely paying for someone. That's the cost.

Kill things that don't have an optimum CPA. Invest more in ones that do. Simple enough, right?

Tip: Remember this is just cost, not profit. If your product costs you $15 to make then, in the above scenario, you are shipping a crisp $5 bill along with every Social Media order!

Where is it? Most likely in Excel. For Search it is in your Google Analytics or Omniture Site Catalyst reports. But for most other programs (Affiliate, Email, Social, Display) your Cost is likely sitting outside your web analytics tool. So extract the # of conversions, import into Excel, add a column for Cost, do the math, sing or weep (based on what the data says!:)).

If you are paying someone to do web analytics and this metric is not on top of the dashboard they've created for you, it might be time to say sayonara to them.

Behavior:

Page Views? Time on Site? No. You can do so much better!

Bounce Rate.

I continue to be a believer in trying to prompt love at first sight. Okay, okay, I'll settle for delivering relevance. :) Bounce Rate helps you identify campaigns where you might be targeting wrong people (who then come to your site and leave right away) or sending relevant traffic to irrelevant (and often flash-filled hideous) landing pages.

Bounce rate helps you find campaigns and landing pages that need to be killed / improved. Everyday.

Where is it? Standard metric in every web analytics tool worth anything. Look at your All Traffic Sources report and your Landing Pages report.

Checkout Abandonment Rate.

I find the fastest way to make money is to take it from the people who have already decided to give it to you. Obsess about checkout abandonment rate (the percentage of people who click Start Checkout to those who complete that process).

Focus on checkout steps with the highest abandonment. Tweak like crazy. A/B & Multivariate tests are a good option. But you are a small business… so just take away as many fields as you can, play with where to show shipping cost (I vote for way up front), reduce the number of checkout steps if you can, ask for account creation at the end of the process rather than at the start. Try, test, measure, be rich.

Where is it? In Excel. Or if you use Google Analytics: In Paditrack for free. (Google Analytics' native funnels are pretty sub optimal, ignore that entire feature.) For other tools: In KissMetrics. Create a funnel just for the checkout process (from clicking Start Checkout to Thanks for your Order) and both these tools will give you the metric automatically. They also allow you to segment the data! Make love to it.

[Bonus: What is abandonment rate?]

Outcomes:

My favorite Economic Value? No. As a small business I recommend…

Macro Conversion Rate.

You are a small business. Obsess about conversion rates, and everything connected to improving them. What products are people buying? Every single day (okay week) look at the All Traffic Sources report and seek out the Conversion Rate metric. Ruthlessly punish sources that are not working well and reward the pretty babies. Be they Earned, Owned and Paid media – oh and have a marketing strategy that has each of those elements or as a small business owner you are not going to win a lot.

I love creating an advanced segment with just the people who buy twice the average order size. I call them the Whales. Look at sources, locations, product bundles purchased, keywords and campaigns and all that to learn where/how you can find more Whales.

Where is it? Standard metric in all analytics tools. Remember to look at both the rate and the raw number of conversions for context. People make silly decisions when they don't do that.

That's it!

You are a small sized business and these four simple key performance indicators will literally rock your world as soon as you start measuring them. Cost Per Acquisition. Bounce Rate. Checkout Abandonment Rate. Macro Conversion Rate. Don't look at any other metric until you feel you've mastered them.

Tip: If you've hired the right analytics talent/consultant to help you, they'll be measuring these fabulous four.

Best Metrics / KPIs for Medium Sized Business Websites

What if you are a medium sized business? What key performance indicators are optimal for you?

First, you are going to measure the KPIs mentioned above. But because you are running a bigger and more complex business you'll also measure…

Acquisition:

CPA

+ Click-through Rate

While CPA is a macro metric about your campaigns' bottom-line performance, Click-thru Rate (CTR) is a deeper dive into analyzing the creativity and relevance of your affiliate deals / search listing / blinky banner ads.

In the context of Search (Paid or Organic), the text in your ads, the number at which your listing is ranked, the match between the user query and your ad's intent all help you receive a higher CTR. And if someone comes to your site (and does not bounce!) then you get an opportunity to convince them of your product or service's glory.

Small tweaks to the subject line of your email campaigns can have dramatic improvement in CTR. Recency and Frequency capping of your display remarketing campaigns can have a huge impact. Changing demographic targeting options in your Facebook ads can work wonders. Etc., etc., etc.

Put another way… CTR helps you understand if you showed up at the right place for your first date. Are you dressed okay. And if you are smiling the right smile. Helpful to know, right?

Where is it?

Everywhere. Start at a campaign level. Drill down to individual creatives. Kill badness. Promote goodness. Rinse. Repeat.

Behavior:

Bounce Rate

Checkout Abandonment Rate

+ Page Depth

A very tiny percentage of visitors to your site will see more than a couple pages. That's the internet for you. As you improve the user experience, information architecture and relevancy of content on your site, it is important to keep an eye not on the rather useless metric of Average Page Views per Visit or Average Time on Site but rather on the distribution of page depth. Here's how that picture might look like (from a post I wrote in July 2006!)…

From the deep detail reported by your web analytics tool you can choose to aggregate into buckets you most care about (like mine above). Categorizing the visits into Abandoners, Flirts, Browsers, One-off-Wonders, Loyalists will dramatically change your view of content consumption. Over time, as you move to deeper consumption, you'll see direct business rewards.

The above image emphasizes a sale/conversion at the end, but even if you are a content-only website improving Page depth helps you because more pages equal (at the very minimum) more ad impressions!

Where is it? The final table will be in Excel. If you use Google Analytics the data you need is here: Audience > Behavior > Engagement > Page Depth tab. If you use WebTrends, Yahoo! Analytics, Coremetrics please click around to find the data. They all have it.

+ Loyalty (Count of Visits)

If Page Depth helps you optimize for a single session experience, Loyalty helps you optimize pan session behavior. Put another way… how good are you at getting the same person to visit your website multiple times? For ecommerce or non-ecommerce websites, loyalty can mean the difference between life of survival and raking in profits like crazy.

First set a goal for the % of site Visits you would like for people who've visited more than x times. [Set a goal for x too. :)] For ecommerce websites use your Days to Conversion report (more on this metric below) to set your goal. For content sites perhaps mirror your content update schedule. If you are the New York Times and you update the website 24 times a day then should the average person be visiting the site at least 90 times per month?

Your BFF, as always, is analysis and not just reporting the metric. Create this simple segment in five seconds…

Apply to your keywords and campaigns and referring sources reports and identify which sources drive loyal traffic. Apply it to your content reports and figure out which content drives Loyalty (Sports? Op Ed? International? Cat Stories?).

Where is it? In every web analytics tool on the planet. If you use Google Analytics the data you need is here: Audience > Behavior > Frequency & Recency.

Outcomes:

Macro Conversion Rate.

+ Micro Conversion Rate

Pick your favorite benchmark and you'll notice that less than 2% of visitors convert. Focusing on just the Macro Conversion Rate means you don't care if you received any business value from the 98% that did not convert. I refuse to accept that uber-lameness.

Identify your Micro Conversions (/Goals) and obsess about the long and short term business value they deliver. You'll quickly realize the Economic Value they create for you is often far greater than the Revenue your Macro Conversion reports! And optimizing for that will ensure you win HUGE.

Where is it? In Google Analytics it is here: Conversions > Goals. Even if you are a content site the data is there. Details in the Goal URLs report. Setting up goals takes two minutes, setting goal values might take you a week (see measurement strategies here). If you use other tools, please check with your vendor.

+ Per Visit Goal Value

This Key Performance Indicator 1. helps you move beyond the obsession of focusing on the 2% (because it forces you to focus on Every Visit!) and 2. encourages you to create a business that uses the web to deliver multiple outcomes to your visitors.

Every visitor will not convert, but every visitor will, hopefully, deliver some Economic Value. Looking at this metric helps you identify Goals that contribute higher value, and and understanding of simple things like where you should focus on. If Twitter delivers 87 cents of Per Visit Goal Value and Google delivers 97 cents then perhaps I want to keep focusing on my SEO strategies rather than following the advice of the Social Media Guru who's just informed me Search is dead.

Where is it? In pretty much every single report in every single web analytics tool. Click on the Goals tab.

That's it!

For a medium sized business we ended up with nine metrics. Seems about right if you are making more than five million dollars of economic value. They key difference from websites that are in the small business category is that we are going to shoot for multiple conversions, deeper site engagement and better analysis of acquisition efficiency.

Time now to deal with the big boys and girls… large websites!

Best Metrics / KPIs for Large Sized Business Websites

Acquisition:

CPA

Click-through Rate

+ % New Visits

My choice of this metric perhaps betrays my refusal to rest on my laurels. There are clearly a finite number of people in the world relevant for any business. But staying hungry and staying foolish is a popular mantra for me. I use this metric to constantly calibrate my acquisition strategy to understand which inbound marketing efforts are bringing new "impression virgins" to the business.

If you look at your Earned, Owned and Paid media then this metric is especially important for your Paid media efforts. Except for your re-targeting / behavior targeting campaigns, you want your paid search, display, affiliate, and social efforts to bring new visitors to your franchise.

Where is it? It's like air, everywhere! Don't forget to segment for optimal analysis.

Behavior:

Bounce Rate

Checkout Abandonment Rate

Page Depth

Loyalty (Count of Visits)

+ Events / Visit

Every awesome large website delivers complex experiences (videos, demos, dynamic slideshows, configurators + + +) via sophisticated technologies (Flash, AJAX, Gadgets + + +). Almost all of the time we leave measuring their effectiveness on faith (or the HiPPO). I love event tracking because it helps us measure these often astonishingly, expensive initiatives.

Of 110,842 visits to the site, 9,054 interacted with your delightful experiences and each of those visits had 2.24 Events per Visit. Is that good? Bad? Could be better? Are these 2.24 interactions delivering higher economic value to your business?

In the above case the answer was a big NO. In your your case you'll decide based on your strategy and goals. At the end of the analysis you'll make significantly smarter decisions about your content (especially because the Analysis Ninja that you are, you'll triangulate performance of this metric with first, Page Depth and, second, Loyalty).

Where is it? Most web analytics tools do some type of event tracking. Please check with your vendor (it might not be called event tracking in their lingo, just describe my first paragraph above). In Google Analytics the data is here: Content > Events.

Outcomes:

Macro Conversion Rate.

Micro Conversion Rate

Per Visit Goal Value

+ Days to Conversion [or Time Lag for Content sites]

Another pan session metric I adore.

Life, no matter how hot you are, is not a series of one night stands. Yet because of how they analyze the data most companies end up optimizing their web marketing campaigns for one night stands. Come here and convert NOW! If yes: Oh, I love you. If no: Kill the campaign!

That approach is not just short-sighted; it is an insult to your visitors. Convert them at a pace they are most comfortable with. This metric helps you understand how quickly or slowly your visitors convert. You can, at the very minimum, change your campaign messaging and come hither calls to action and adjust your landing pages. If the Days to Conversion are much longer, then create a robust (slow dance) micro conversion strategy.

If you have a non-ecommerce website then there is something delightful for you in the Google Analytics Multi-Channel Funnel reports. Checkout the Time Lag report . It is showing you exactly the same data as the Days to Transaction for Ecommerce sites. The metric you see immediately above is called Conversions. It is essentially your Goals (/micro conversions).

Optimize your "hello, nice to meet you, what would you like, here is what I have to offer, why don't you check with your spouse, come back and check it out again, multiple times, I'm still here, you ready to convert / deliver economic value, here's how… " process.

Where is it? Days to Conversion is in the Ecommerce section of your web analytics reports. It is a standard report. (Don't forget to segment your sources. Deep insights await.) Time Lag may or may not be a standard report in your tool. Please check with your vendor. In Google Analytics it is a standard report here: Conversions > Multi-Channel Funnels > Time Lag.

+ % Assisted Conversions

This is the newest metric I've made standard for all my clients / partners / BFFs. And it is a sweetie.

Assisted Conversions builds on the above mental model. It takes a while for a majority of your visitors to convert (macro and micro conversions), so why does almost all of web analytics focus on single channel analysis and optimizing that single channel in a silo? Just because the Affiliate click was the last one before conversion should it be optimized for that conversion? Especially if the Visitor originally came via Facebook (or Google or whatever)?

How many of your conversions had more than one ad / media / marketing touch prior to converting? Really smart Analysts at really successful companies understand that…

…and then use that data to optimize the portfolio of channels rather than individual channels for the company.

Even if you don't do portfolio optimization (and desperately hope you do) you can easily see how the above data will cause you to execute a different marketing optimization and expectation strategy for Email (1.18 Assist / Last Interaction rate) vs. Organic Search (0.61).

I am being modest when I say that this metric and subsequent analysis will have a fantastic impact on your company.

Where is it? % Assist Conversions may or may not be in your web analytics tool. Please check with your vendor. In Google Analytics you'll find it here: Conversions > Multi-Channel Funnels > Assisted Conversions.

And we are done!

For large businesses we've identified 13 key metrics that would give a robust end-to-end view of business performance. The key difference vs. medium sized businesses is that we are really, really, really focused on pan-session (multiple visits) behavior. Put another way, we really care about people here and not just a single visit.

Here is a summary of the metrics I am recommending in this post…

I hope the picture above will quickly help diagnose where current gaps in your measurement strategy might be.

Additionally if you are a small business you'll know what else to measure when you start to become medium sized and if/when you cross that threshold you'll know the metrics that come with your large business status. :)

You'll notice that I'm not focusing on KPIs like AdSense Ads CTR or Page Load Time or Actions per Social Visit or Search Exits (I love this metric!) or Content Distribution vs. Content Consumption Rate or Conversation Rate (in case of a content site) etc. That's simply because these KPIs tend to be unique to the type of business you are running. My strategy above was to focus on just the KPIs that would be applicable across all types of businesses.

That brings me to a very important point.

While it is my hope that you'll find my recommendations above relevant and yummy… the most optimal way to identify that best key performance indicators for your company will come using the process and structure outlined in the Digital Marketing & Measurement Model.

I'll end with the thought I started this post with… we have more data than God wants anyone to have. But web analytics does not have to be scary or impenetrable. Use the roadmap above, focus on all three elements (acquisition, behavior, outcomes) and I promise you'll soon be on your way to being as happy as God wants everyone to be.

I wish you all the best!

Okay as always it's your turn now.

Does your business use the above recommended metrics / key performance indicators? Do you have an absolute favorite metric that's not mentioned above? Which metric above do you find most useful? Which one most useless? What is your strategy for identifying the most relevant metrics?

Please share your suggestions, critique, and helpful best practices via comments.

Thank you.

PS:
Couple other posts on metrics / KPIs you might find interesting:

• Web Metrics Demystified
• Your Web Metrics: Super Lame or Super Awesome?
• Kill Useless Web Metrics: Apply The "Three Layers Of So What" Test

Best Web Metrics / KPIs for a Small, Medium or Large Sized Business is a post from: Occam's Razor by Avinash Kaushik

In this post about an important Google change, I want you to focus less on the data and focus more on the methodology. And – so important – I want you to help me with your ideas of how we can do this impossible analysis better, in the complete absence of data :). So please share your ideas via comments and let's together make a smarter ecosystem.

On board? Let's go….

In an effort to make search more secure, on Oct. 18th Google announced that users logged into their Google accounts using www.google.com would be redirected to https://www.google.com. The search queries by these users would hence be encrypted and not available to website owners via web analytics tools such as Omniture, WebTrends, Open Stats, Google Analytics etc.

Switching from have all the search queries in the keywords reports was our normal state, not having them feels different. As the change ramped up and more user queries came to be represented, in at least Google Analytics, under the moniker "(not provided)" we all got worried. From our perspective it would be immensely preferable to be able to analyze all the keywords individually. Sadly we don't have that now.

The wonderful thing is that in addition to passionate commentary on Twittersphere / industry blogs / gurus, we also have access to data for our own websites. We can, and should, look beyond simplistic "it is this high or that low" to see if we can understand something (anything!) deeper.

Most analytics vendors, including Google Analytics, reacted immediately to the change in order help us quantify the impact of this change in multiple ways. As you can imagine my reaction was to unleash a flurry of custom reports and apply smart advanced segments and compare data pre and post change and go down a bunch of holes.

From that experience here are five steps I recommend you follow to gain a smarter understanding of this change…

1. Establish macro context.

On Oct 20th on my Google+ page I'd shared a custom report for Google Analytics that makes it extremely simple for you to look at this data. Visits, Unique Visitors, Bounce Rates, Goal Completions for (not provided).

You can download that report into your GA account by clicking on this link after you are logged into GA: Google httpS Change Impact.

Here's what the data for this blog looks like for one month:

Like me first you should compute the high level impact of the change. From Oct. 31 (when the trend started to spike and subsequently stabilized) to Nov 15…

Please note that this number will vary dramatically depending on the type of website you have, audience attributes, geographic location and a number of other factors.

Now you know what the number is for your site, and you can keep the custom report handy to continue to watch what happens over time. Remember to divide the number by total Google traffic. I see people using total search traffic or total site traffic or… other imprecise metrics.

All numbers in aggregate are at best marginally useful, and that rule applies to this one too.

We want to know more. Who are these people? Are they people I should care about? Not care about? And what kind of search queries are these? Brand? Non-brand? What else?

Sadly we can't answer all of those questions, but we can make a small clump of informed judgments based on data we do have. It just needs a pinch of passion, some smarts and a lot of effort.

Let's deep drive into some very cold and choppy waters…

#2: Understand the performance profile of the (not provided) traffic.

One of the things I hate about standard reports in all web analytics tools is that they scatter necessary data across tabs, multiple reports, or outright hide it. #aaarrrrrh

So I always use custom reports . In most web analytics tools it takes as little as 20 seconds to create one. I did one for this particular purpose. It provides me the end-to-end view of search keyword performance in one place.

Here is what it looks like:

You can download it into your Google Analytics account by clicking here: Keyword Performance Analysis Report

Two quick things to note.

So how does (not provided) look? Here's my end to end view:

The numbers in red were added to the report by me. I wanted to know what percentage of the total Visits and Goal Completions (not provided) was. [On that last point, if you have an ecommerce website you can use Orders or an appropriate proxy instead of Goal Completions.]

Bottom-line: 18% of the Visits and 22% of the Conversions.

Big numbers! But with a quick scan of the report, I think I already see that there is something delightful going on here. Stick with me. I think we have a surprise coming.

The custom report has eight metrics (two more than I normally use) simply to try to tease out some nuance of the performance as we look across keywords.

One hypothesis I had was that (not provided) might be mostly returning visitors. The overall search avg % New Visits is 67.96%, for (not provided) it is 65.06%. Very similar to the "average site visitor." But notice that all Brand Terms above (avinash, kaushik, occam's razor) have very low % New Visits. So it is possible that (not provided), contrary to my hypothesis, are mostly new people.

Overall bounce rate is 70.2% (not unusual for a blog/pure content site), and (not provided) is 66%. Again, scanning across the top ten terms you can see higher rates for non-brand searchers (people looking for specific, perhaps quick, answers) when compared to brand terms.

Content consumption, Pages/Visit, seems to be a bit on the higher side compared to the average (1.76). But like the other metrics above, there is a pattern between brand and non-brand (with brand higher on this metric).

I really, really care about Goal 2, hence that conversion rate is in the report. The average is 2.21%, (not provided) is around 2.37%. There's not much conversion going on with the broad non-brand terms (you can't get lower than 0% :).

Goal Completions is very interesting. (not provided) is a huge bucket of goal completions (and it is easy to understand why so many SEOs and Marketers and Lovers are in a tizzy!). The thing to note here are the numbers in red (% of each bucket compared to total Goal Completions, 4,816). See how quickly thing fall off the cliff. Note the difference between brand and non-brand.

Finally, my absolute favorite: Per Visit Goal Value. There is no obvious monetization on this blog, but I have 8 distinct goals and I have goal values assigned to each for the long term impact each adds. (How's that for focusing on customer lifetime value? :)). $1.27 for (not provided), compared to overall of $1.01, and the number does not come close to the other brand terms.

We still don't know what keywords are contained in the (not provided) bucket.

But what we do know is that for this site (not provided) visitors fits this bill: They seem to be new people with behavior that is quite distinct from the "head" brand terms and closer to the non-brand terms.

In the past I've lovingly termed non-brand long tail visitors as "impression virgins." The hint at the end of this step is that I've got myself a lot of impression virgins in (not provided)!

Let's go and see if we can validate that theory.

#3: Deep dive: Match up performance profile to Brand & Non-brand visits.

Based on the clues above, I'm going to try to understand whether the performance profile for (not provided) is indeed closer to brand searchers.

I create this simple segment in GA… should take you five seconds to do it for your own business…

Apply it to my custom report and boom!

[sidebar] A quick thing to note is the ratio of Unique Visitors to Visits. In context of % New Visits that makes sense. But just make a note of it. [/sidebar]

How does this compare, purely from a performance of the key performance indicators perspective, with (not provided) for the same period?

Quite a stark difference as you look across metrics like % New Visits, Bounce Rate, Pages/Visit, Conversion Rate and Per Visit Goal Value.

So how does the performance of (not provided) compare to that of non-branded keywords? Not a difficult question to answer.

Back into GA to create a segment like the one above, expect change "Include" to "Exclude" and I have my non-branded traffic segment.

Here's how those numbers look like in the aforementioned custom report:

When you do this with your data you'll have a similar image and you'll compare it to your (not provided) segment performance, and your brand segment perfromance. In the comparison above it is clear that these three buckets are distinct, but that the performance of (not provided) is not as close to brand as it is to non-brand. Even though the (not provided) segment is small (4.6k) compared to non-brand (21.9k) – thinking about impact on averaging these metrics.

There are two likely scenarios in terms of what you'll find…

In your case (not provided) segment might match overall Google traffic or one of the above segments. In which case you continue business as usual with the assumption of an even distribution.

It is possible that (not provided) segment does not match overall Google traffic, or one of the above segments, in your case. In this chase you understand a bit better how to treat it in your thinking (more keywords connected to your brand or non-brand segments). At the moment you can't take action based on this information (how to you react to visitors whose keyword you don't know at all). But when presenting to your senior executives you can give them a bit more context.

It does not eliminate all the questions, but it does help me go from "I have no idea who all these people/keywords are" to "Okay looks like it might be my non-brand possibly long tail traffic."

Something of value, right?

All of the above is still kind of at an aggregate level. But we all have a lot of keyword level historical data. At some point we should have enough post change data that we can throw it all into a delightful regression model to fine tune our understanding at a keyword level.

At the moment we just know a little bit more than "here's my total (not provided)."

#4: Tentative conclusions. Why this seems so scary, but might not be (at least for now).

Most, but not all, of my branded traffic is my "head" traffic, i.e. traffic that results from a few keywords used by lots of visitors. After all your brand is unique to you and, for any type of website, drives loads of search traffic to you because you rank high in SERPs for those brand queries.

Most of my non-brand traffic is my "tail" traffic, i.e. traffic that results from a lot of keywords used by a few people each. For example you'll notice at the very start of this post that during this time period I had 27k visits. Of this my "tail" traffic comprised of 21,921 visits. These delightful folks used 10,498 distinct non-branded key phrases to find my website.

10,498 distinct search queries drove 21,921 visits!

Remember the two scenarios I'd mentioned above? Let's look at one of them (performance closer to non-brand traffic) and understand what is happening a little more visually. What is happening when (not provided) shows up as your #1 metric in your search keyword reports?

In my case above, closer to scenario #2 for me, the performance of (not provided) as shown by the metrics above looks more like that of the visitors who came via those 10,498 non-branded search key phrases.

Here's what's happening when (not provided) shows up #1 for me (clear in the screen shot in part #2 above), as explained by my head – tail illustration :

Prior to this change by Google, the gray slivers above represent traffic that became (not provided) after the change.

In the past only a small part, if any, of this traffic, for me, would ever show up in the top ten or twenty keywords in the report (head traffic). Because much of it was in the long tail I never noticed it (it is hard to look at all 10,498 key words individually! :).

But after the change by Google, these tiny, in the past invisible, slivers combined look like one scary beast. I've painfully combined every pixel of gray sliver above:

OMG! I've lost a huge chunk of something that was a very important part of my traffic!!

Not really. It just looks scarier than it really is because tiny shavings of your other keywords (now used by logged in users who are opted into https sessions on google.com) appear in one big piece. Individual cells don't look that scary. But combined they look like Darth Vader himself. :)

Let me hasten to add that this does not mean that these "slivers" from user search queries are not important. Or that just because they are mostly non-branded traffic we should ignore them (I argue 100% contrary to that here: Monetize The Long Tail of Search ). Or that you should not worry and that the sun is shining, there is no US debt problem, we have universal health care and Ashton and Demi are still together.

No. Not at all.

But the sky is not falling either.

We can use the actual data we have to keep a very close eye on this traffic and its performance. We can use advanced segmentation and custom reports to understand where this big scary block of traffic used to be. Is it (to repeat the scenarios we outlined at the end of part 3 above) closer to the average performance and hence possibly evenly distributed or closer to non-brand and less evenly distributed.

We sadly still won't know what actual long tail or non-brand keywords or overall keywords they represent or how much of a particular keyword/phrase they used to be. But my POV is that we'll be in a better place.

You can be, if the data in your case justifies this, just a little less worried.

#5: Additional awesomeness: Landing page keyword referral analysis.

One final idea I had was to wonder if the (not provided) traffic enters the website at a disproportionate rate on some landing pages when compared to all other traffic from Google. If that is the case we could do pre post analysis on referring keywords to those landing pages and get additional clues.

It is not very hard to go checkout that theory.

First, create an advanced segment for the (not provided) traffic:

Then go and apply it to your standard Landing Pages report in Google Analytics (or SiteCatalyst or WebTrends or Yahoo! Web Analytics):

The analysis from here on is not very difficult (though in the new version of GA it is harder as the UI designers got rid of the % delta for comparative segments – what a shame). Just use our bff MS Excel.

For example 14% of the (not provided) traffic enters on the home page.

I was able to find a small clump of pages where the (not provided) traffic, at least currently, entered the site at a higher rate than overall Google traffic. I can see the referring keywords to those pages prior to the change and after the https change and attempt to identify which keywords might be contributing traffic to (not provided).

For me this analysis provided a better idea about some long tail non-brand keywords. But it was not as much as I would have liked to learn. Partly that is a function of the fact that those keywords are used by a handful of people and, this makes it worse, they are quite transient – they are not used too many times again.

But since everyone's site and visitor behavior would be different I did want to share this idea with you. It is not a hard bit of analysis to do, and you can let the data tell you something (or not).

That's it.

A simple five step process to go from reacting based on an aggregate number in your keyword reports to a much more nuanced (if imperfect) understanding based on your own data.

Caveats:

Before we go, a few important reminders that are spread throughout the post above but bear repeating….

Anything else I should have here that I've forgotten?

I would love to know how you would go about doing this impossible analysis? What other path would you take in your web analytics tool? What segment, report, metric, walk on water effort would you undertake? Regarding my five step effort above… what flawed assumptions am I making? What would you change in terms of the approach/conclusions in any of the steps?

Was this nuanced understanding of what might be happening better than where you started?

Please share your alternative ideas (please!), critique of the above analysis, ideas for world peace via comments.

Thank you.

P.S: A request. This blog focuses on digital marketing and web analytics, it is not a policy blog. If you are up for it I would love for your comments to focus on the former and not the latter. If for no other reason than that my skills don't extend to the policy part and I would not be able to share anything of value with you.

I appreciate your consideration.

Smarter Data Analysis of Google's https (not provided) change: 5 Steps is a post from: Occam's Razor by Avinash Kaushik