Web Analytics 2.0: The Art of Online Accountability and Science of Cus

Book Notes by Julaine

Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity

0470529393.jpg
Publisher: Sybex
Pub. Date: October 26, 2009
Print ISBN: 978-0-470-52939-3
Web ISBN: 0-470529-39-3 

Must-Have Elements For Web Analytics

The following is my list of the must-have elements that different businesses should consider to join the Web Analytics 2.0 world; they are ranked by priority and show the minimal areas that should be addressed:
  • Small businesses: 1. Clickstream, 2. Outcomes, 3. Voice of Customer.
  • Medium-sized businesses: 1. Outcomes, 2. Clickstream, 3. Voice of Customer, 4. Testing.
  • Large, huge businesses: 1. Voice of Customer, 2. Outcomes, 3. Clickstream, 4. Testing, 5. Competitive Intelligence, 6. Deep back-end analysis (Coradiant), 7. Site structure and gaps (Maxamine).

Questions to ask before purchasing fee-based services:

"What is the difference between your tool/solution and free tools from Yahoo! and Google?”

"What kind of support do you offer? What do you include for free, and what costs more? Is it free 24/7?”

"What features in your tool allow me to segment the data?”

"What options do I have for exporting data from your system into our company's system?”

Web Metrics
Before we continue, here's a quick clarification: a metric is a quantitative measurement of statistics describing events or trends on a website. A key performance indicator (KPI) is a metric that helps you understand how you are doing against your objectives. That last word—objectives—is critical to something being called a KPI, which is also why KPIs tend to be unique to each company. 
Visits and Visitors form the bedrock of nearly every web metric calculation. You'll see them prominently displayed in your web analytics tool, but you'll also find them in your search reports, your exit pages, your bounce rate computation, your conversion rates, and so on. So, your Visits and Visitors are very important.

3.1.1.1. Visits

Visits report the fact that someone came to your website and spent some time browsing before leaving. Technically this visitor experience is called a session.
In most modern web analytics tools, a session, or visit, is defined as lasting from the first request to the last request. If the person simply leaves the browser open and walks away, then the session is proactively terminated after 29 minutes of inactivity.
Please check with your web analytics vendor to learn what sessions are called in your tool. They could be masquerading as Visits, Visitors, Sessions, or some other label.
Even with the previous caveats, the Unique Visitors metric continues to be a superior approximation of the number of people visiting your website.
The task of calculating your true real Unique Visitors number across an arbitrary time period or across multiple weeks or months is computationally intensive. That means more processing time and higher costs for the vendor. So, doing daily, weekly, and monthly counts (and then summing them up) is cheaper for them.
Google Analytics, XiTi, and Nedstat are amongst the rare vendors that provide the truly de-duped Absolute Unique Visitors metric by default, that is, at no additional cost to you.

3.1.2. Time on Page and Time on Site & 3.2. Bounce Rate

3.1.2. Time on Page and Time on Site

After Visits and Visitors, perhaps the next foundational metric in web analytics is Time. It measures the time that visitors spend on an individual page and the time spent on the site during a visit (session).

3.2. Bounce Rate

I have been known to call Bounce Rate the sexiest web metric ever! I am fond of measuring Bounce Rate for several reasons:
  • It is a metric that is available as a standard metric in pretty much all tools. (In cases like Omniture, where it is not, you can still easily compute it.)
  • It is really hard to misunderstand what Bounce Rate measures.
  • It is actionable on multiple levels, especially at identifying the low-hanging "fix me now" fruit.
  • It measures customer behavior, perhaps the most holy of the holy goals in measurement.
Here are additional tips for actionability:
  • Measure Bounce Rate for your website's top referrers. Your top referrers tell you who your true BFFs are. These are not the referring sites that just send you traffic but rather sites that send you traffic that does not bounce.
  • Measure Bounce Rate for your search keywords (paid and organic). Perhaps you are optimized for the wrong keywords, or perhaps your landing pages stink; either way, you need to fix them.
See what I mean when I say that Bounce Rate is a hugely actionable metric?

Exceptions and Excuses for Bounce Rate

Exception
There is one obvious case where measuring the Bounce Rate metric in aggregate might be suboptimal: blogs.
Blogs are a unique beast amongst online experiences: people mostly come only to read your latest post. They'll read it, and then they'll leave. Your bounce rates will be high because of how that metric is computed, and in this scenario that is OK. 

3.4. Conversion Rate

Is there any another metric that we focus more of our love and attention on than Conversion Rate? Not yet. And perhaps that is how it should be. We are investing in our websites, so we should measure what comes of them.
Conversion Rate, expressed as a percentage, is defined as Outcomes divided by Unique Visitors (or Visits). Outcomes are customarily the submission of an order on your ecommerce website.

3.5. Engagement

The Merriam-Webster dictionary defines engaging as "tending to draw favorable attention or interest." We should all try to create website experiences that draw favorable attention or interest. The challenge in the context of measurement is that "favorable attention or interest" is incredibly hard—if not impossible—to measure.
The 2009 Econsultancy Online Measurement and Strategy Report (http://sn.im/starep) identified the following 11 barriers to successful web measurement strategy:
  • Lack of budget/resources (45 percent)
  • Lack of strategy (31 percent)
  • Siloed organization (29 percent)
  • Lack of understanding (25 percent)
  • Too much data (18 percent)
  • Lack of senior management buy-in (18 percent)
  • Difficulty reconciling data (17 percent)
  • IT blockages (17 percent)
  • Lack of trust in analytics (16 percent)
  • Finding staff (12 percent)
  • Poor technology (9 percent) 

These are notes I made after reading this book. See more book notes

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