Brand Awareness is a Metric, NOT a Goal

OK just a quick one. Here it is 1:00 in the morning, and I have to write this because it's on my mind. I'll regret it when the alarm goes of at 6:something.

I read a whitepaper recently, and it started off by making the critical point that in order to design metrics, you must be clear about your objectives. OK we all agree.

But then this same document goes on to outline brand awareness as an objective.

Now at first, this didn't jump out at me.

But has brand awareness ever been a business objective? Do people go into business to maximize brand awareness? Or is brand awareness "quantified", a measure of your effectiveness at reaching your target audience and disposing them to consume your product?

The Acid Test for Custom Reports

OK, I'm speaking to the people who work in small entrepreneurial comapanies here. For those of us who chose to work in a dynamic entrepreneurial setting do it for the rush of immediacy. The ability to move quickly. to stay light on our feet, and make real change.

Lots of times, in performance meetings, someone at the table (ok, it's usually me) will pipe up and say "How hard would it to be able to just find out ..." It's a theoretical discussion only.

Someone has a new report request.

Maybe its something they've thought through, and is going to revolutionize the way we do our business.

Maybe its just idle curiosity.

The tech people at the table immediately tend to jump on the "How do we do this?" bandwagon, totally bypassing the "Is this something we should be allocating valuable resources to?" train.

And usually, at first blush, the answer looks and feels like ...pretty quickly.

But how often is that true?

Sometimes, what happens at that point, is someone pipes up and "authorizes" the report, based on the assumption that someone else can whip it up over sandwiches today at lunch.

Then that someone else spends the whole afternoon on it, because they ran into a few unforeseen stumbling blocks. Oh, and by the way, they have a couple of questions about how the reports should be formatted...so now followup meetings are being scheduled, and the person who first requested the report, is now piling on additional feature requests, unchecked. And the dominoes start to topple....

The project has outgrown the petrie dish, and is limping hideously through the corridors of your place of work, wreaking havoc. People have forgotten its original, innocuous status as a "theoretical discussion". The hours invested in it have infused the project with the value of human sweat.

Let's take a peek a few weeks down the road, and go ask how much time the new report is saving? A couple of possibilities (terribly overgeneralized of course)

1. The person who first requested the report has a new spring in her step, and has lost the haunted look that comes from too many hours manually calculating stuff that is better done by a computer.

This is the result you're going for. Congratulations.

That young genius is probably going to start poring over the reports and come up with a great recommendation that will materially alter how you do business...save you a gazillion dollars, and pay for the implementation 40 times over before next week's meeting.

2. The person who requested the report has relegated it to the pile of "stuff I don't need to babysit anymore." Ask her how it's progressing, and she'll pull a report for you while you wait. ....and oftentimes discover that the data is being pulled incorrectly or the report is garbled, or not in a format that is useful to anyone. ***By virtue of having been authorized in the first place, the project has been elevated to the status of "stuff worth doing properly"****. At this point countless additional hours may be sunken into the pursuit, before any kind of cost:benefit analysis happens.

3. The person who first requested the report is completely buried. The time requirement to analyze the implications of the new report is consuming them and they no longer seem to have time to stop and think about how much benefit the new information affords them(factoring in all of the data exceptions, annotating the performance anomalies that affect the data output, and closing the knowledge gap between the data and the information that data stands proxy for).

The Three Things That Should ALWAYS Happen Before Anyone Builds a Custom Report

1. Write a SPEC. Even for a little thing. This process is great for shining a light on the holes that are so easy to gloss over in discussion. We sometimes want to rush past this step. We know exactly what we want. We think we have expressed it clearly. We think there is no room for error.

I'm married to a system architect. I've tried asking him to just whip me up a report (I am a self-confessed data junkie). Without a written spec, he refuses, even when I assure him its quick and easy. Even when I say pretty please or bat my eyelashes.

Try this. If is a simple report, it will only take you a few minutes to write out the functional spec. Describe all the inputs, the data sources, and all the possible outputs, depending on what inputs the report receives. This will bring you a lot closer to a shared understanding with the people who are building the report.

If having the report is not worth the time it takes to properly specify how it works, you probably don't need it.

2. Remember that data is only a proxy for information. It is imperfect, and prone to misinterpretation. It can act as a red herring, or mask important trends. Be sure that everyone involved understands how the data is being calculated. Call the data what it is, not what it is supposed to represent.

Create a A data Glossary of Terms.

3. Things automated are easily forgotten. Ensure you build in a mechanism for following up on the results of the report.

Final Notes

There is a lot of room for assumptions when people toss an idea about over the boardroom table. But programming a custom report is an exact science. Many a programmer has misinterpreted the requirement, and built a report that does not meet the need. And often the report that DOES meet the need is a LOT harder to build.

Multivariate Test Conversion Page

Thanks so much for participating in my multivariate test....

Or, if you came straight here from somewhere else, and are willing to take 2 seconds to participate, I'd appreciate it. Just click the link.

Multivariate Testing, A Work in Progress

I am very excited about google's web optimizer, which makes multivariate testing easy and manageable. I have run various A/B tests for the pursposes of website optimization in the past, but always with unweildy manual systems. the variables very quickly spun out of control, and the results had to be manually processed and interpreted.

I have struggled for the last few days to set up a multivariate test on a dynamically generated site, with limited success (server problems have meant limited ftp access to try out the quick/easy wordpress plugin that I found after a little searching. Its called Google Website Optimizer from Content.Robot.)

So, I thought I would set up the experiment here instead. Of course, I'll have to pay all of my friends and loved ones to come and visit the site achieve statistically significant volumes, but it will at least give me practise at setting up the process.

So, first I have to identify what action I want people to take. I'll start simple and just ask them to

click this link to participate in my multivariate test.

The variables I'll test will include

1. put the link in the middle of the page versus the top of the page
2. including an image or not
3. 2 alternate headlines

And I'll create a "conversion page", to use the google website optimizer lingo, thanking site visitors for playing.

p.s. clearly I should have posted my conversion page first, so they would appear in order

Emerging Keyword Trends With Hittail and Blogpulse

How do you identify appropriate keywords phrases when they are only hours or days old?

Take the phrase "subprime crisis" for instance, or "global food crisis". These are subjects that have only recently been thrust into the public consciousness. Language is constantly evolving, but SEO experts need to be ahead of the curve.

Using keyword research tools like wordtracker or Trellian is excellent for understanding the "voice of the customer" - words that prospects use to describe the product or service that you offer- as long as the phrases are well established. But what about keyword phrases that are evolving or being coined today? By the time an SEO expert has found them, he is already behind in the race to get search engine position.

That is where tools like Hittail and Blogpulse come in.

I've written about Hittail a couple of times, but if you haven't downloaded it, you can get it here free: www.hittail.com.

Blogpulse is a tool from Neilsen Buzzmetrics. You can check it out at www.blogpulse.com.

Hittail give you a realtime stream of keywords, as well as the ability to easily repeat the search that brought your visitor. It allows you to identify emerging keyword combinations quickly.

What it doesn't do is help to expand your keyword list to related terms.

If the phrase is very new, the keyword research tools won't help you here.

So how can you identify related keywords to the longtail phrases Hittail shows you from your site traffic?

For this I have recently started to use Blogpulse. Results are fresh. timely. and framed in the language of the people who are most interested...niche bloggers and the site visitors who post their comments. I have to comb through the results, looking for useful terms and phrases. It is not neatly packaged like a keyword tool, and volumes are anyone's guess. But the wording in use by the bloggers and commenters is a good starting place for building out a list.

The site also displays helpful charts showing incidences of the phrase over time.

I am on the hunt for a more immediate keyword research tool. Can anyone suggest one for me?

I'm Trying Out Hittail - Part 2

I wrote a couple of weeks ago that I had installed Hittail on this blog, in order to get a better sense of what subjects my readers are interested in. I wanted to see which topics the Hittail engine would recommend that I focus on.

Well, after a week, I took the next step and installed it on several higher traffic blogs.

So far, the results are encouraging....and somewhat addictive for the compulsive web-analyst. (Did I mention I'm thinking of starting a support group for marketers who feel compelled to while away their nights studying emerging user trends?)

At the office, we've taken a couple of (I think) exciting steps to align our editorial and marketing efforts, and the Hittail application is playing a role in that process. It is helping us to determine which stories to feature, allowing us to craft more targeted headlines (both to improve SEO and the user experience), and increasing our awareness of which keyword phrases we should be monitoring position and optimizing for.

A Word on the Hittail Suggestions Tool

Hittail provides suggestions as to which keywords your website should focus on optimizing for. As a self-professed metrics junkie, I've wasted a few thoughtcycles pondering the algorithm that identifies words.

Hittail is about the longtail strategy, so the recommendations are not based on keyword volume. Some of the words it has selected are pretty obscure, so it doesn't seem to be any kind of keyword clustering. From what I can hypothesize, it seems to select words for which your site appears fairly low in the rankings, but for which you still managed to attract a visitor (implying that those who ranked above you provided unsatisfactory results for the motivated searcher). Presumably, I guess, if you could improve you rankings on these phrases, you would attract much more of this type of motivated/previously unsatisfied traffic to your site.

I have no real idea if this is how it works. I'll be posting to the forums at hittail.com to see if I can ferret out an answer. Meanwhile, I can recommend the tool, and report that it is proving useful for me on numerous levels. You can download it



Anyone else out there using hittail? How are you implementing its recommendations?

Predictive Versus Descriptive Modeling with Analytics

I wanted to post about approaches to using web analytics data that recognize its limitations, and its power.

Many organizations use historical analytics data as a basis for forecasting future growth, and establishing performance goals and budgets. This applicaton for analytics data can blur the distinction between predictive and descriptive data. Understanding this difference is critical to an effective analytics program. It generally falls to the analytics professional to ensure that the difference is clearly understood within the organization.

I'm going to start out with a couple of definitions. What do I mean when I say predictive versus descriptive modeling?

Predicitive modeling refers to a mathematical model that can accurately predict future outcomes. For instance, I know that if I apply sufficient heat to water, the water will reaach 100 degrees celsius and begin to boil (barring slight variations for altitude which are also predictable). The rate at which this happens and the amount of energy required can be mathematically described.


Descritive modeling refers to a mathematical model that describes historical events, and the presumed or real relationship between between elements that created them. For instance, yesterday when I went to the store to buy milk, it cost me $1.00 a litre, last month it was 95 cents, last year it was 80 cents.. Based on historical events, I assume it will cost me roughly $1.05 to buy a litre of milk next month.

Web analytics falls in to the latter category. It is a set of descriptive, historical statistics.

Past Versus Future Performance

I direct marketing activities for a division of a financial publisher. The company has an outstanding track record for identifying and recommending market-beating stocks. Historically, they've made recommendations that represented a lot of money for a lot of investors worldwide.

But at the end of each financial report that we send out....and those of virtually any financial advisory service or market report is a warning to readers something like:

Past performance may not be indicative of future performance.


***Web Analytics reports should carry the same user warnings***


Have any of you ever sat in a management meeting in which company representatives have said something like, "We have the data to demonstrate the relationship between ad spending and revenues, so if we want to grow sales by 20%, we just need to ratchet up our ad spending accordingly."?

This kind of thinking fails to recognize the rate of change in markets, technologies and the competitive landscape, and fails to factor in the concepts of resource scarcity and the law of diminishing returns, as well as the potential from economies of scale. That's without even considering the inherent flaws in any data gathering and processing system.

So, if the operation of complex markets can not be accurately predicted by simplified analytics models, what are the more tangible uses of historical analytics data?

1. To identify broken systems. A significant change in performance data can often indicate a technical problem, overloaded systems, broken links, faulty logic etc.

2. To select between alternatives. Analytics is particularly apt for testing market responses to different offers, creative, or sales processes with A/B or multivariate testing. It can also provide a guide as to which channels or markets tend to be most lucrative or most cost effective among existing channels. (The challenge, then, is often to find ways to expand the more lucrative channel).

3. To flag new market opportunities. A careful study of web analytics data can reveal new opportunities for cost savings, revenue generation or operational improvements.

4. To extablish a meaningful dialogue with existing and potential customers. Web analytics data can help us to learn about customer needs, desires and propensities. It can teach us the language that the customer uses to articulate their needs, so that we can respond meaninfully. It also can give us parameters to personalize the user experience to better meet their needs and create loyalty, trust, and ultimately customer satisfaction

One final VERY POPULAR use for historical analytics data

And hey,...IF your company persists in falling in to the infinite resources analytics model trap. IF they continue to hold fast to the belief that past performance predicts future performance, you can always use your historical analytics data as a roadmap to all of the external factors that caused your company to veer off its charted course.