Pageview is Giving Way to New Metrics and it's About Time

MarketingVox, one of my favorite marketing sites has an excellent article about page-view becoming obsolete as a major metric for site success.

This is an issue I've been very passionate about. While page-views are important for determining part of the overall site metrics, I personally feel that in the new era of social media, social networking, social sites that combine different technologies like Ajax and Flash, it's becoming less and less relevant and important. Steve Rubel's article about page-view is a brilliant description of how other aspects and other metrics are becoming more important.

One of the aspects of the Web 2.0 mindset is to have users interact with the website. As more and more people spend more time online, companies are finding ways to encourage user interaction. Sometimes this happens on a single page, other times it's browsing. With the Web 2.0 mindset, page-view is less descriptive of the behavior of the users.

In the past, we looked at page views as a way to see where people are going on a website, we can see them migrate from the homepage, to the category page to the product page and finally to the checkout and purchase page. This linear model is becoming less and less relevant in socially based sites. YouTube and MySpace rely on people to bounce from profile to profile, video to video and interact with the elements on the page. This creates less of a linear pathway and more of a meandering pathway.

Steve Rubel describes tracking "events" as a more important way to look at analytics and user behavior on a site. He makes an excellent point that page-views and even unique visitors don't account for multiple monitors, multiple windows or in Firefox (I would assume) multiple tabs. As I write this post, I have 9 tabs open.

He makes an excellent point that I completely agree with:

Time Spent

With the rise of online video and other rich media, marketers also rely on time spent to measure attention. This is a good metric and it even holds as people interact with embedded video and widgets on whatever platform they choose.

Unfortunately, time spent fails to capture the most engaged users who like to peruse RSS feeds. For example, I subscribe to multiple RSS feeds from the Wall Street Journal but I only click through on those that I want to dig deeper. Still I spend up to 10 minutes a day with my Journal feeds and over an hour a day overall within my Google RSS reader. That time is not accounted for - at least by the Journal, but certainly by Google. There's the dilemma

His conclusion is that the more we track events and time spent, the more accurate the data is going to be to determine user behavior, site value and overall marketing efforts.

Right now, the industry still values some of the more traditional methods of determining and interpreting metrics, but I agree with Steve Rubel. There's an analytic shift that corresponds with the new way of internet marketing and Web 2.0 that current habits dont fully describe.

Business Intelligence Needs to Support the Front Lines

James Taylor, Vice President of Product Marketing/Management at Fair Isaac runs an excellent blog called Enterprise Decision Management. Recently, he posted about an article from Informatics about businesses not understanding business intelligence.

Nigel Rayner says:

BI has been around a long time and people do get returns out of it, but it has been technology and infrastructure driven and is not addressing the needs of senior management.

Mr. Taylor expands that comment to say that,

not only does it not address the needs of senior management, it does not support the needs of operational staff either. Not management, not analysts, but people working on the front lines helping customers. It also does not help the software that support customers.

When using business intelligence, it's always important to define the end results and the goals of the endeavors. Data that goes directly to the senior management doesn't necessarily mean that it makes people more efficient, infrastructure more manageable or the company more competitive.

BI (business intelligence) is the process of obtaining data from various sources, but that endeavor must be driven by the goals and real issues that companies face. One of the litmus tests of any BI driven decisions must be: how it helps operations from the bottom up.

This article has intrigued me, I added his blog to my RSS reader and I'm going to do some serious back reading on it. It seems that every article I read from him inspires a few questions.

Standard Deviation When lnterpreting Web Analytics

One of my favorite blogs, Good Math, Bad Math has an excellent article describing standard deviation as it relates to the mean of the data.

The mean is more commonly called the average. It's calculated by the sum of the total data points in the population, then divided by the number of data points. A simple example of that would be the data set: (1, 2, 3, 4, 5). The sum of this population equals 15. There are 5 data points in this population, so the mean would be calculated as 15/5=3.

A fancier way to put it would be the following formula:



We can see through web analytics the average visitors per period of time... daily, weekly, monthly. However, measuring by mean alone can be deceptive. The mean doesnt describe some of the more important data sets that are important in determining the meaning of analytics.

The mean wont give you information on the low points, the high points, nor will they tell you the relationship of the mean between the rest of the data. In the earlier data set, the relationship between the data was very easy to determine. In web analytics, those relationships can be a little trickier.

I'll take an example that's near to me. My own web analytics.

Currently, I average 42 page views per day. This means that 42 of my unique pages are viewed... this is not a site visit. My low point is 4 page views in a single day and my highest is 124.

From this, we can tell that there was most likely a spike in my page views at some point. Because the mean is less than twice the largest data point, we can automatically start with that presumption. However, in order to get more information, we must take into account the standard deviation. It is defined as a measure of the spread of its values also, the square root of the variance. (From Wikipedia)

Each differently colored area is the standard deviation. Each section is the same length, but not the same area under the curve. This means that within one standard deviation of the mean, most of the data falls under those data points.

In my case, my standard deviation is calculated as 9.4.

What this means, is that using Chebyshev's Inequality rule,

At least 50% of the values are within 1.4 standard deviations from the mean.

At least 75% of the values are within 2 standard deviations from the mean.

At least 89% of the values are within 3 standard deviations from the mean.

At least 94% of the values are within 4 standard deviations from the mean.

At least 96% of the values are within 5 standard deviations from the mean.

At least 97% of the values are within 6 standard deviations from the mean.

At least 98% of the values are within 7 standard deviations from the mean.

At least 1 - 1/k² of the values are within k standard deviations from the mean.

When you apply this information to web analytics, one of the things I do is look at the geographic distribution of the users. When I find hubs of higher consumer acitivity, I start getting a clearer idea to who my users are. This could help me target my paid search campaign more accurately, this could let me know that if I provide content, analysis or a blog, a nice mention of something applicable and interesting in their area might be appropriate.

The standard deviation is a powerful method to segment your analytics into greater specificity. When Chebyshev's Inequality shows you that 75% of the data is within two standard deviations, then you have some focused and applicable data to improve your messaging and targeting.

Defining Search Metrics: Search Engine Presence

In an earlier post, I mentioned (without explicitly defining) the term of "search engine presence". This came from the dissatisfaction I felt when talking to clients about the health of their search engine optimization/ marketing campaigns. All too often, I would hear the same mantra..."I want to be number 1 for the term X" or "Why aren't I number 1 for the term X?"

This felt inherently wrong to me. However, I couldn't really answer their question, nor could I give them a sure-fire way to attain that position. I sometimes felt that I should be glib and say "If I knew that answer, I'd be working at Google as one of their engineers, right?"

At the same time, everyone who's been in search marketing for more than a week knows that it's best to rank well on a variety of keywords, while remaining true to the core goals of the site. After looking at some WebPosition Gold ranking reports, something struck me as odd about them, they gave the ranking reports, but the data it gave seemed too myopic. This is when I started thinking about "presence" as a metric for measuring the health of the search marketing campaign.

I went to Adam Schultz, and I proposed to him a creation of a simple program that we would later called the Competitive Analysis Baseline Reporting tool. This program would take the core, top level keywords from the client's input... adjusted and perfected by some keyword research and take a look at which sites ranked for those keywords. We wanted to get a good look at the entire search spectrum, so we took the top 15 results in Google, the top 10 in MSN and the top 10 in Yahoo!. This way we would get an overview of what I later called the search engine marketspace. It's a capture of data at a specific time of what the marketspace is.

For a practical example, lets take a few keywords... "iphone, apple iphone, ipod phone" for this example, we don't need a lot of keywords because I'm looking at defining, in a practical sense, "presence".

So, with 3 keywords and (15 Google + 10 MSN + 10 Yahoo) we can expect to have a sample size of 105 potential slots for search engine results to appear. When the same company, like Apple shows up across the search engines and at different positions, their presence is counted as 1. Each presence is counted and sorted for the total presence. In this case, the top 1o results is as follows:

	1/10/2007
Domains	SE Presence
www.apple.com	7
www.thinksecret.com	7
www.engadget.com	7
www.gizmodo.com	6
www.appleinsider.com	5
www.mobilewhack.com	5
www.everythingiphone.com	5
gizmodo.com	4
en.wikipedia.org	4
news.bbc.co.uk	4
www.businessweek.com	3

For those terms, Apple.com shows up in Google, MSN and Yahoo 7 times, as does Thinksecret.com and Engadget.com. Gizmodo shows up 6 times, and so on. The idea here is not to diminish the actual position, or rank of the site, but emphasize the presence in the overall search marketspace. When your company relies on capturing qualified traffic from search, it's obviously better to have several keywords working for you, rather than focusing on only one keyword. Unfortunately, all too often, SEO/ SEM companies attract the client by either telling the prospect what they want to hear, or implying that ranking on their top keyword is paramount to success.

What we see here is a lack of education and a hype of expectations. When the client is properly educated on the strategies of SEO and SEM, they're more likely to abandon the expectation of the single keyword on top hope and adopt a more gestalt view of the search engines as an environment that changes, evolves and fluctuates. Once they see that, they'll recognize the value of having several keywords that work for them and not just one.

Perception Blindness and Competitive Intelligence Methodologies

For those of you who know me, you know that Michael Shermer is one of my heroes. Ever since I read his book "Why People Believe Weird Things", I realized how much of my life I had spent being a pattern seeking animal. I loved science, but never took it to be much more than a series of facts or theories. In fact, I couldnt even define "theory" very well.

It is due in part of Shermer and the Skeptic Society and Magazine that I live and breathe evidence. I need evidence in life and evidence in my work. Analytics seem to me a perfect example in marketing that evidence is needed. Before I do that, I wanted to show you a video... and you must watch this before you read on... from Michael Shermer's talk at TED - (it's short... less than 3 minutes).



Did you watch that? Did you miss it? Did you see it?

Perceptual Blindness hits all analytics readers at some point or another. Often times, when I'm speaking to a client about competitive intelligence, they think that it's a "thing" that I do. In reality, it's a process... like science is a process. Competitive Intelligence is a methodology and a way to process information in order to make predictions about something. Often times, it's about M&A's... in my line of work, it's about looking at an existing strategy of a selected sample of companies who dominate a keyword marketspace and determining what they're doing and potentially, what they're going to do... then comparing that against the goals of the client and the trends of the industry and making recommendations.

Simply put, it's multivariate research and statistics with evidence guided intuition and theory... and this is the important part - It has to be measurable. Perception blindness occurs when the analyst goes into the research with more than just the goals of the clients. The client often has their own expectations of what you can do, or what the industry is like. I used to tell people that "you know your business, I know your business online". It's two different animals.

One of my old clients wanted me to do an intelligence report on some of their competitors. They gave me a list of competitors and I went to work. When I finished it and presented it, they were impressed, they loved it and all was good. However, I had a nagging feeling in the back of my head. I felt that I had missed something. When I got back to work, I used a program we developed called the "competitive analysis baseline reporting tool". While it was a fairly simple scraper and easy to program through an excel macro... the reporting capability was, in my opinion, very powerful. I found that looking through their online competition, only one of the competitors they mentioned even had a presence (from a search engine marketing perspective). As soon as I found who their online competitors were, I was able to do some more research, report it, and the result was a 20-40% increase in all of their benchmark analytics. If I hadn't done that, if I hadn't recognized my own perception blindness, I wouldn't have done everything I possibly could to help that client.

In the future, I will be talking more about perception blindness in analytics and how to avoid some common mistakes.

For now, if you liked the 3 minute clip... here's the entire lecture from Michael Shermer from TED. It's brilliant.