Links on Web Analytics

A Framework for Implementing Web Analytics

In order to truly get the most value out of web analytics, it’s important to have a holistic approach to implementation.

Web analytics is nothing new to digital marketers. In fact, most brands and agencies have been doing web analytics for years. If you’re one of those marketers, then ask yourself honestly the following questions:

  1. Do you know the purpose of looking at certain metrics?
  2. Do any of your web analytics reports tie to your business objectives?
  3. Do you have any insights after reading your Google Analytics or Omniture report?
  4. Do you know what actions to take after you read your reports?

The author’s guess is you answered no to more than one of these questions. That’s because most companies treat web analytics as a reporting tool, while it’s really an approach to accountable and data-driven digital marketing. A complete web analytics implementation should fundamentally change the way digital marketing is done in your organization.

According to the Web Analytics Maturity Model by Stephane Hamel, a clear direction is needed in all the following areas for web analytics to be successful:

  1. Management/Governance
  2. Objectives
  3. Scope
  4. Team and Expertise
  5. Process and Methodology
  6. Tools and Technology

A Framework for Implementing Web Analytics

Beyond Goals: Site Search Analytics from the Bottom Up

Avinash Kaushik demonstrated that site search analytics is a powerful tool you can use to assess customer intent quantitatively. In site search analytics, as with all flavors of web analytics , you can work from the top-down; by starting with clear, measurable metrics based on your organization’s goals, you can benchmark and continually optimize the performance of your content and designs. While goal-driven analysis is wonderfully useful, the author explores a different, ‘bottom-up’ approach that relies on pattern analysis and failure analysis to help you understand your users’ intent in qualitative ways that complement the top-down approach.

Beyond Goals: Site Search Analytics from the Bottom Up

Practical Guide to Controlled Experiments on the Web: Listen to Your Customers not to the HiPPO

Partial Abstract
The web provides an unprecedented opportunity to evaluate ideas quickly using controlled experiments, also called randomized experiments (single-factor or factorial designs), A/B tests (and their generalizations), split tests, Control/Treatment tests, and parallel flights.

Controlled experiments embody the best scientific design for establishing a causal relationship between changes and their influence on user-observable behavior. We provide a practical guide to conducting online experiments, where end-users can help guide the development of features. Our experience indicates that significant learning and return-on- investment (ROI) are seen when development teams listen to their customers, not to the Highest Paid Person’s Opinion (HiPPO). We provide several examples of controlled experiments with surprising results.

Practical Guide to Controlled Experiments on the Web: Listen to Your Customers not to the HiPPO (PDF, 490 kb)

Internal Site Search Analysis: Simple, Effective, Life Altering!

Understanding of your site visitors’ intent is one of the most delightful parts of web data analysis. In this article, we’ll learn five ways to analyze your internal site-search data—data that’s easy to get, to understand, and to act on.

  1. Understand site-search usage
  2. Where visitors search and what they do next
  3. Measure site search quality
  4. Segment, segment, segment
  5. Measure outcomes

Landing Page Testing: Choosing Between A/B Or Multivariate Approaches

The author desribes A/B tests and multivariate tests (MVT), the difference between them and how one can choose which best fits their needs. A comparison between the techniques is mentioned, taking into consideration the overall use of the testing technique, coding needs, design needs, granularity of results and other considerations.
Landing Page Testing: Choosing Between A/B Or Multivariate Approaches

The key components of Web Analytics 2.0

The key components of Web Analytics as defined by Avinash Kaushik of Web 2.0 are:

  1. Clickstream- The What
  2. Multiple Outcome Analysis- The How Much
  3. Experimentation & Testing- The Why
  4. Voice of Customer- The Why
  5. Competitive Intelligence- The What Else
  6. Insights- The Gold!

Key components of web analytics 2.0 by Avinash Kaushik

Web Analytics 2.0

Web Analytics: An Hour a Day by Avinash Kaushik

Book cover- Web Analytics: An Hour a Day by Avinash Kaushik

Title & Author

Web Analytics: An Hour a Day by Avinash Kaushik

Description

Web Analytics: An Hour A Day is the first book by Avinash Kaushik, Analytics Evangelist for Google, is an expert in web analytics and the author of the top rated blog Occam’s Razor. An in the trenches practitioner of web analytics, he provides a unique insiders perspective of the challenges and opportunities that web analytics presents to each person in your organization that touches the web.

In this best selling book he goes beyond web analytics concepts and definitions to provide a step-by-step guide to implementing a successful web analytics strategy.

The book includes an innovative CD that will include over five hours of insightful audio podcasts, a 45 minute video presentation, PowerPoint presentations, and other useful web analytics resources.

Being the wonderful guy that he is, 100% of the author’s proceeds from the book are donated to charity. The proceeds will go to The Smile Train and Médecins Sans Frontières (Doctors Without Borders), to assist in their efforts to make our world a better place.

Publisher

Sybex (June 5, 2007)

ISBN

ISBN-10: 0470130652
ISBN-13: 978-0470130650

Purchase

India- From Flipkart
Elsewhere- From Amazon

Website for the book

Web Analytics: An Hour a Day- by Avinash Kaushik

Author’s website

Occam’s Razor by Avinash Kaushik

How to build a web analytics measurement framework

The concept of ‘drowning in data’ cannot be understated when it comes to web analytics. Apart from the sheer quantity of information available, the situation is worsened because the tools we use are so terribly fast and effective; it has never been easier to slice, dice and peel your way through such huge mountains of click-stream data. But just because it’s there and easy to access certainly doesn’t mean it’s easy to make sense of. Most companies that fail in this arena do so because they simply don’t know what to look at, but rather flail around in the data following endless and infinite pathways that, whilst ‘interesting’, ultimately lead nowhere fast.

The article describes a ‘Measurement and Optimisation Framework’, which might sound complicated but is, in fact, simply a strategy for: what you should be measuring; how to do it; and what you should do with the information once you get it.

Web analytics measurement framework

How to build a digital measurement framework

Web Analytics Blog- Occam’s Razor by Avinash Kaushik

Blog on web analytics by Avinash Kaushik- analytics evangelist for Google & author of Web Analytics: An Hour a Day
(a book that benefits The Smile Train and Doctors Without Borders. 100% of the proceeds are donated to them).

Web Analytics Blog | Occam’s Razor by Avinash Kaushik

Measure Map

Measure Map is now a part of Google.