Metrics are the marketer’s microscope. They show him what his customers are actually doing, as opposed to what they say they are doing or intend to do. With proper metrics he can make decisions faster and more accurately.

You can decide to measure anything, but what metrics matter and what ones are just for show? Here are some rules I hope will guide you toward creating meaningful metrics that help, rather than hinder, the decision-making process.

Be Actionable

Be Understandable and Trustworthy

Measure Results

Understand the Downside

Understand the Upside

Don’t Be Ambiguous

Segment by Purpose

Appropriate Granularity

See the article at 8 Tips for Crafting Metrics That Matter

The title is Web Analytics 2.0: the Art of Accountability and the Science of Customer Centricity.

I read his blog regularly, and I look forward to the book.

He was recently interviewed on Mitch Joel’s excellent podcast Six Pixels of Separation. I recommend that readers visit the Six Pixels of Separation blog to listen to the entire interview (and other podcasts).

There are a couple short passages that I want to call out from the beginning of the interview. He says about his upcoming book:

“You’ll see me frequently use the words analyst and marketers….to me, really there isn’t an analyst that isn’t a marketer behind the scenes, and there isn’t a marketer that isn’t an analytst behind the scenes.

We live is such a data-driven world where the web experience and the customer experience is changing with every passing day. Every marketer needs to know that data, analytics, qualitative quality is going to give him or her the edge. And every analyst needs to know that unless he deeply understands marketing, he or she can never do analysis that creates great customer experiences.”

This is precisely the concept that led to the term “creatalytics.” It is the confluence of creative and analytics, of marketing and data. These can no longer be separate disciplines.

Links:

Six Pixels of Separation podcast interview of Avinash Kaushik
Avinash Kaushik’s blog Occam’s Razor

“Those hoping for a turnaround in the U.S. ad economy next year could be sorely disappointed, according to a new forecast from WPP’s GroupM. What’s worse, the new research predicts that outlays in 2010 will decline even more than they will this year.”

“Global ad spending in measured media is expected to drop 4.4 percent to $425 billion in 2009 compared to 2008 when spending was up 3 percent”

GroupM Futures director and chief forecaster Adam Smith said … “The 2008-2009 period is now a more serious advertising recession in scale, duration and relative to the global economy than the extraordinary 5.1 percent real-terms post-dot-com global advertising correction of 2001,” Smith said.

This article and study were brought to my attention by Tony Brock of thinkLA. Here are my thoughts:

Reduction of budgets will certainly continue throughout 2009.

But an equally dramatic trend will be a substantial shift of budgets from brand campaigns to paid search and SEO work. With the right analytical discipline, SEM in particular can be fine tuned and refined very quickly, giving it the agility to always be optimizing for maximum return on advertising dollars.

Here is a relevent, albeit anectdotal, blog post from Craig Hordlow at Red Bricks Media: http://www.redbricksmedia.com/blogs/craig_seoblog/?p=129

Tony raised the question of what ad agencies are doing or should be doing. In my opinion, they should be working very hard to build and grow SEM and SEO services, with a very serious commitment to analytics.

I can’t say this loudly enough: data-driven marketing is not a trend. It is the new toolset.

And when I talk about a commitment to analytics, I don’t mean as an *alternative* to focusing on creative. Not at all. The two need to meet. Fuel one another. This is what I mean by Creatalytics.

If agencies haven’t embraced the use of data analytics to get a better understanding of what works in creative, then they will be left far behind.

(AdWeek: GroupM Projects Ad Spend Slump Will Last Into 2010)

“What do you, the marketers, want to see on LinkedIn from an advertising perspective? What questions do you have for us about our members or about how they’re using LinkedIn”

She is mainly talking about brand marketers. That’s their big push recently. I’m rarely in that category, but I am part of an extremely lucrative marketing of advertisers. We are the advertisers that is purely ROI-driven. We’re driven by data, not brand. I’ve managed significant expenditures on Google and other search engines, and I’m always looking for more positive-ROI ad impressions.

So…What do I want to see?

From my perspective, I’m open to trying any new advertising platform, so long as it provides two main things:

• 1. ease of use, so any investment of time learning a new system is minimal, and
• 2. presence of the tools and data that accelerate the Creatalytical cycle of creative development, testing, analyzing, iterating to optimize the mix for ROI.

Notice I didn’t saying anything about demographics, the targeting capabilities, cost, etc. All those things can be tested, so long as you are enabled to be effectively and expeditiously creatalytical. Reid Hoffman is giving interviews about how LinkedIn is essential for brand advertisers. He talks about how the demographic on LI compares to the Wall Street Journal, and how this is valuable to brands. And he’s completely right.

But what I’m interested in is a platform for advertising that is not about brand building: it’s (primarily) about selling. I don’t know that first thing about average salary or household income of a Google user, and that hasn’t stopped me from generating billions of impressions, hundreds of millions of clicks, and tens of millions of dollars of net revenue in my career.

What do I have with Google? Lots of data and tools that allow the campaigns and sites to evolve quickly.

So for me, what I want to see on LinkedIn is that which is squarely in my comfort zone: pay per click text advertising, with lots of available data for analytics. I’ve not started with DirectAds, but I assume this is the PPC engine they are using.

The way I see it, there will be two big challenges for LI.

First, inventory for ads. There’s a lot happening on the average LinkedIn page, and the LI team will have to make some interface choices to free up some space, and that could be quite a hearty debate, both internally and with users.

Suggestion to LI: find space for text ads, and then commit to that real estate. As Google has shown, a fixed position for texted ads will increase user experience while delivering ROI to advertisers.

Second, will a homegrown PPC text ad system (DirectAds?) make more sense than simply syndicating Google, whether it’s AdSense or likely something more “partner-ish,” giving the close relationship of the companies? As an advertiser, I’ve spent years becoming an expert at Google AdWords. The tools, the interfaces, the tricks, the algorithms, the policies, the competitive position dynamics, etc. I’m still a ways from my Gladwellian 10,000 hours, but certainly far enough along that it’s increasingly hard to justify spending time learning new systems.

And also, along the lines of this second issue, there is the issue of relevancy of ads. And the question is: can LI build something at least as good as Google on this? LinkedIn users are serious people, usually on a serious mission while on the site, and if they’re going to sacrifice some screen real estate for ads, they’re going to want to make darnn sure that the relevancy and targeting algorithms are spot on. Not only for ROI (for advertisers and for LinkedIn) but also to keep customer satisfaction up.

It’s quite a balancing act, I’m sure.

In a post, he points to a video from an AdWeek Quantcast panel last Fall. There is a bit in this video from Andy Fisher, the Analytics Director and National Lead at Avenue A | Razorfish, that really caught my attention.

The moderator asked the panel of ad agency data analytics pro’s how agencies can use insights from digital data to inform the creative process and advertising.

Mr. Fisher opens his comments with “we are drowning in data…we have more data than we know what to do with.”

Here’s the excerpt that is particularly interesting to me, and quite relevant to what I’m trying to communicate in the blog (emphasis is mine):

 ”I would argue that it all comes down to a fundamental understanding of the consumer. And when we use all this data to understand the consumer, if you can get an idea of the consumer, you can then build the website, craft the digital experience, you can build the creative, and you can plan, buy, measure and optimize the media, all with that common framework.

…And we found that if you can do that, it can be a very powerful thing. But I will be frank, it is a real challenge to get people in the industry to begin to think that way, to be able to do things like data-driven creative, or data-driven user experience, or data-driven design. It can be very challenging to get people over that hump. My experience has been, when people get over that hump, it can be very powerful, but it’s really hard to do.”

To me, this is interesting for a couple reasons.

1. this is precisely the work I’ve been doing for the last 5 years. Not in an agency, but as owner/operator of sites. We’ve gotten over the “hump” that Fisher talks about, building all our sites and advertising campaigns from the data up, so to speak, with analytics driving all decisions on web design, user experience, advertising and more. This is exactly what I mean by “Creatalytics.”
2. I’ve been thinking quite a bit lately about what I perceive as an opportunity in the agency business to create an uber geek, super cruncher SEM/SEO/advertising agency, and what kind of form it would take, how to do great client work, how to sell the value prop, etc.

I may get into this second point in another post, but I will say this now: as I’ve talked about this idea to a few people lately, I’ve specifically (and coincidentally) used Razorfish, and how it was positioned in it’s early days, as an example.

The way I saw it in the last 90’s, Craig Kanarick and Jeff Dachis were successful selling their services to the big brands, because their clients were terrified of missing out on the Internet thing, and they saw Razorfish as the young, hip, artistic kids that “got it.” The black clothing, the piercings and tattoos, the purple hair, the deviant content sites…that was all part of the mystique. And ultimately, they sold to the fear of missing it.

These days, data analytics isn’t going to make anyone look hip or artistic. But it’s analogous in the sense that its the next big wave. And it’s slightly out of the comfort zone for those who do things the traditional way. And ultimately, even traditionalist are beginning to realize that either you get on board with the Datarati, or you miss the boat. And missing this boat? If that prospect doesn’t scare, it damn well should.

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The video can be found here:
AdWeek ‘08 Outtake: We’re Drowning in Data, Long Live Data!

In 2006, Hans Rosling gave a presentation at TED, the context of which was income distribution, health, and tech adoption in the third world. But mostly, the presentation became known for the method of presenting the data more than the data itself.

He had wanted a way to visualize data that went beyond the X and Y axis, a way that could show many dimensions, including time, in a visually compelling, animated fashion.

In short, a way to clearly communicate the nuance that is often buried deep in complex data, underneath the convenient summarizations that can cause us to miss more granular opportunities.

From this, he worked to create gapminder.org which in turn created the software he needed. And it’s this software, called Trendalyzer, that is demo’d in his presentation at TED.

About a year later, Trendalyzer was bought by Google, and is now part of the Google Visualization API.

Here is the awesome presentation at TED. If you’re a data geek like me, this is well worth the 20 minutes. Perhaps the most entertaining presentation of 3rd world life expectancy data ever.

I’m eager to dive into this API and use the tool to create motion charts that will help me understand complex PPC campaign performance and site conversion data.

If you’ve used this tool, I would love to hear about your experiences with it.

(And here is Hans Rosling’s Bio on the TED website.)

I was just reading it in the gym, while riding the bike, and I hit a paragraph that I liked enough to grab my pen (what kind of geek takes a pen on the exercise bike?) and highlighted it for a blog posting.

This paragraph is in the first chapter, where he’s discussing his methodology for the research that went into the findings in the book. The relevance to this blog is in his approach to data analysis and the distillation of actionable knowledge from the analysis.

I want to quote it directly:

“I’ve tried to come up with a simple way to convey what was required to go from all the data, analyses, debates…to the final findings in this book. The best answer I can give is that is was an iterative process of looping back and forth, developing ideas and testing them against data, revising the ideas, building a framework, seeing it break under the weight of evidence, and rebuilding it yet again. That process was repeated over and over, until everything hung together in a coherent framework of concepts. We all have a strength or two in life, and I suppose mine is the ability to take a lump of unorganized information, see patterns, and extract order from the mess — to go from chaos to concept.”

From chaos to concept. Yes.

I can identify with his process, and with what he describes as his strength. I can get so immersed in the data that I reach a sort of “River Runs Through It” zen fly fishing, connection-with-your-surroundings level. There are times when I see patterns and glitches in the Matrix, feel tremors in the Force, and…ok, I’m out of movie metaphors.

But what I really love in this description is something else I think about in my work: a process built on humility. Sure, it helps to have smarts, and skills, and subject area expertise. And creativity, and energy, and intelluctual curiosity. And a great team that possesses all these things. But all this will only get you so far.

By keenly applying humility to the process, you recognize that your ideas could be wrong. In fact, you realize that if you really do have all those things I mentioned, you’re very like to be wrong very frequently.  

A motto/credo that I’ve used in hiring: at our company, if you’re not wrong frequently, you’re not trying hard enough.

And to return to the “process built on humility,” it’s not just an attitude: its an approach. Jim Collins was talking about business research. For me—for the Creatalytics approach—I’m talking about building technology, data analytics, and discipline to drive marketing in a way that assumes mistakes will be made, and allows you to iterate very very quickly, and forces your learning to push every aspect of marketing to higher levels of effectiveness.

And so, in the case of Creatalytics, we don’t try to go from “chaos to concept.” Concept is fine when you’re researching a book: but if you’re spending tens of thousand of dollars a day to drive traffic to your sites, you need results. Conversions. Maximized Ticket Value. Lifetime Value. Referrals. Customer Satisfaction.  Etc.

And Creatalytics is an ongoing cycle. By design, it goes from chaos to concept to implementation to testing to analysis and then, quite purposefully, back to chaos for another run at it. Again and again.

And all with great humility. We’re smart because we admit we’re not so smart. We build a system, with a simple set of rules, that leverages our smarts to give us answers and actions.

Seems like a fitting day to introduce a concept that has guided much of my thinking, something that is key to what I’m writing about on the Creatalytics blog.

Darwinian Marketing is the term I use for an approach that involves applying the framework of natural selection to systems that test website variations, ad copy variations, conversion scheme variations, and much more in a manner that allows an evolutionary process to occur and ensures that the strongest survive and an evolve.

On the very last page of The Origin of Species, Darwin writes:

“Thus, from the war of nature, from famine and death, the most exalted object which we are capable of conceiving, namely, the production of the higher animals, directly follows. There is grandeur in this view of life, with its several powers…from so simple a beginning endless forms most beautiful and most wonderful have been, and are being evolved.”

There is no simpler, yet more important concept, in understanding nature. And this concept can inform our understanding of many things far beyond natural science: complexity, emergence, economic systems, and yes, data-driven performance marketing.

It provides the key insights that allow us to bring analytics and creative thinking together in a way that propels  our online marketing efforts.

In my posts thus far, especially in my post “K.I.S.S., my ass,” I’ve started talking about how a savvy Internet marketer can and should feel confident embracing complexity, in building systems that allow them to learn lessons and take action based on lots of data.

I’ve written that “keeping it simple” in the sense of “less is more” is no longer necessary to get actionable knowledge out of data, because if you’re smart in building your systems, processes, approaches, and tools, you won’t need “fewer” to have simple.

Rather, if built correctly, simplicity—and elegance—will emerge from complexity.

For me, a key part of this approach is in understanding how the lessons of natural selection apply to this work. Creative iteration, copy fidelity, periodic mutation, and either a punctuated or gradual evolution. Conversion path optimization as ecosystem.  

Darwinian Marketing.

More to come on this concept. Stay tuned.

I saw an eHarmony commercial on TV, where they seemed to describe a method of matching singles based on obvious similarities discovered in some sort of interest inventory questionnaire.

I thought to myself, admittedly a bit smugly, “how clueless.” Compatibility is way more complicated than that, and either they don’t get that and their “statistical methods” are shoddy, or this marketing is just dishonest.

This isn’t the exact commercial that I saw, but close enough. Take a look:

eHarmony Ad (will launch new window: they don’t allow embedding)

You get the idea: their marketing approach is to describe a higher level of sophistication on matching. You can almost see the white lab coats, clipboards and pocket protectors in the other room, behind the cool West Elm-decorated walls.

“We prescreen each and every member for you, to determine your matches based on compatibility. So when you get to that first date, you know you’ll have something in common. eHarmony does the matching for you, based on who you are, at the deepest level.”

My personal belief is this: true compatibility rarely lines up with obvious similarities that can be addressed in a questionnaire. And I was skeptical that they were doing anything that diligently addressed the true complexities of a magical match.

It sounded like they were taking an interest inventory and match up people with similar interests: artists with artists, sports fans with sports fans, Christians with Christians. Keep the crazy fun-loving-youthful-spirit types away from the serious, sober folks. Hide the Ph.D.s from the GED’s.

In my opinion, and from my experiences, this was an irresponsible course for a couple reasons:

1. commonalities in interests, background, hobbies, etc. often have very little relation to true compatibility, and
2. people are notoriously inaccurate when describing themselves and what they’re looking for.  It’s sort of an unintentional dishonesty that leads us down the path toward describing how we want like to be perceived.

So, it seemed to me like they are doing people a disservice with false and irresponsible analytics, by applying conventional wisdom and intuition to a system to match people who were as similar as possible. Idiots!

But, then I read Super Crunchers, which explained why I was dead wrong (not about what makes people compatible, but about the eHarmony folks being idiots.)

The book talks about how the regression—a statistical procedure that takes historical data and determines how various causal factors influence a single variable of interest—helps us make decisions by improving predictions.

Author Ian Ayres’ first demonstrative case study: eHarmony’s predictive statistical model.

As it turns out, eHarmony’s founder, Neil Clark Warren, surveyed over 5,000 married couples. He developed a predictive model that includes 29 different variables that relate to emotional temperament, social style, cognitive mode, and other factors. He used a regression to predict how these factors influence a variable of particular interest to him: compatibility of a couple.

Super Crunchers (emphasis mine):

“There is a new wave of prediction that utilizes the wisdom of crowds in a way that goes beyond conscious preferences…Unlike traditional dating services that solicit and match people based on their conscious and articulated preferences, eHarmony tries to find out what kind of person you are then matches you with others who the data say are most compatible”

The regression is about working with data and finding the “levers of causation that are hidden to casual and even expert observation,” as Ayres puts it.

So with this approach, eHarmony tosses conventional wisdom or user stated preferences out the window. The data tells them what “works.” On some personality dimensions, the data will say that we should be similar to our match; on others, its best that we’re dissimilar.

The regression drives this kind of data-based decision making by improving predictions. If you’re trying to decide where to take your marketing next, the answer may be right there in your database.

This is one of the tools that allows you to do more with more. As I’ve said, forget “KISS”: embrace complexity by letting data analytics converge with and inform your creative.

And with code, that meant making a clean plan for development and keeping the code organized, economical, and efficient.  The fewest and most straightforward instructions to accomplish the goal.  Elegance.

Wikipedia says:

“The KISS principle states that design simplicity should be a key goal and unnecessary complexity avoided.”

One of the main ideas that I’d like to impart in this blog is that simplicity and complexity are not mutally exclusive. The key to the above definition is the word “unnecessary.” And as data capture becomes easier and analytics becomes more sophisticated and manageable, one is able to maintain simplicity with greater levels of complexity.

So on this blog, I will speak to this issue frequently. I want to explain how I’ve come to embrace complexity in data-driven marketing, and how I believe this approach has been a major contributor to business successes.

I want to be clear: I’m not against “simple.” I just know a different kind of simple than the one I hear about frequently. For me, it’s taken on new meaning, one which does not equate “simple” with “less.”

My kind of simplicity is about being smarter and more effective with more.

It’s about taking whatever limited insights, experience, and intelligence we may possess, and using data analytics and smartly designed organic systems and processes to leverage our way to greater heights.

And for anyone out there, trying to succeed in competitive online business verticals, I hope to show you how you too can Live Simply with Complexity.

More to come…