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

Although it is only January, I can say with a pretty high degree of confidence, that the analytics book of the year is Avinash Kaushik's new book Web Analytics 2.0. As a follow up to his first book, Web Analytics: An Hour A Day, I think Avinash out did himself with this book. Web Analytics 2.0 is a nice balance of theory and practical tips, but more importantly, provides guidance for readers with a wide range of skill and experience. Over the 14 chapters of the book, Kaushik covers almost every aspect of web analytics, from competitive analytics to optimization, even guides for picking a solution vendor and starting a career in the industry. Overall the book is an easy read for anyone interested. Avinash's causal writing style and frequent examples makes the text engaging and entertaining. For the most part, you can dive into any area of the book without missing too much context from the rest of the book. Of course I recommend reading the whole thing, but even if you just read one chapter, say on social measurement, you'll still get a lot out of it. On top of being a well-written resource, all of the profits from this book (and his previous one) go to two charities: The Smile Train and The Ekal Vidyalaya Foundation. Get the book and start the new year off right with a quick education on the in's and out's of web analytics.

What a fun to read author! This is one book I actually looked forward to reading, not only for how absolutely informative it was, but for the author's fun sense of humor. Kaushik is a foremost authority on all things "web metrics" and has a great blog as well as being a great author. I can't imagine anyone being disappointed with this book as far as wanting to learn the basics (or remembering what is important, for those already experienced). For beginners, Kaushik covers the most important things to focus on early, so you don't get overwhelmed. He then provides the extras that are good to incorporate once you get comfortable with the beginning stuff. For experienced users, Kaushik reminds you of how going back to the basics and getting out of the weeds will improve your results, and again, provides the advanced concepts as well. This book was easy to understand for me, as a beginner and had lots of great examples.

Lots of good information, but there are no descriptions for any software or how to get the reports seen in the book. I am trying to recreate these reports using Google Analytics, Coremetrics and Omniture. It seems that most of the reports are the standard reports out of Google Analytics, but I am having a difficult time recreating some of these with other software. I think this was a great book, but I have a few things I disagree with: Page 85, he says if he could only have one report, it would be Outcomes by All Traffic Sources. This report shows Goal Conversion Rates, but he does not describe what these are. In Google Analytics, these are custom, so this could be anything. I am disappointed, he does say it is important to measure ROI, but does not talk about how to do this. The author says that you can do this by comparing the data from Google to your campaign data. It is not that easy. You have to know how much was spent, and you have to know how much incremental revenue came in from SEO/PPC efforts. It is not an easy task. Test and control or some other method should have been addressed. In calculating ROI for PPC in chapter 11, he assumes that all visits from PPC are ones you would not have without the ad. Not necessarily true. In Chapter 7, testing is finally addressed. I disagree with his method of testing the impact of PPC by turning it off and on completely; this does not take into account any seasonality that may occur naturally in web traffic. This is also a problem if there is a lot of variation in web visits and sales over time. Why not try test and control markets: turning it off in some regions and have it on in others? This method would allow you to compare the on and off markets and find incremental sales. In the marginal attribution model from page 368, you change the spending for one type of online marketing, then attribute any sales higher than last month sales to the additional marketing. In my experience, web sales tend to have a large variation in sales from month to month making it difficult to say what the cause of any increase is without any kind of confidence bounds. The "controlled experiment" on page 375 is a really bad example. The ad is run at the same time in all markets and then compared to pre and post ad time periods. What if at the same time as the ad, some celebrity tweeted that they loved your product or some news program aired a warning about your product. There are too many uncontrollable situations to compare pre and post ad sales. You should have test and control markets to compare sales in the same time period. On page 377, the Author says: "The analyst at Walmart.com can use the previous URL to track how many people use the website and then visit the store." A view the store locator on the web does NOT equal a visit to your store. In his example, a user on walmart.com views a camera and then the store locator. It is very possible that the customer viewing the camera at walmart.com may also go to target.com and find the same camera at a similar price and find that the target store was much more convenient to visit. There is no way in this case to tie a store locator and product page view to an offline purchase. Using a discount code or unique offer would provide a better method of tracking online to offline behavior. In Chapter 14, the BMI is introduced. But on page 419, the author says this method is preferred because it has a scale of 0 to 100. It actually has a scale of -100 to 100. If 5 responders all gave a Not Satisfied or a Not At All Satisfied, the score would be [(0+))-(5):]/5*100=-100. The other method, weighted means can also give a scale of -100 to 100 if the right weights are used. Not Satisfied At all:=-1 Not Satisfied =-.5 Satisfied=0 Very Satisfied= .5 Extremely Satisfied= 1 With these weights the scale is also -100 to 100.

Web Analytics 2.0 is not a sequel to Kaushik's first book Web Analytics: An Hour a Day. The latter was a hard core offering that covered all aspects of the subject. 2.0 is a more general book that covers a wide range of topics related to and around Web Analytics. The coverage of Social Media and Mobile analytics is sparse and that's my only gripe. Considering that both topics are quite hot and that Social Media has gained maturity it would have been helpful to have both these covered in depth. That said the book is pretty robust in its coverage of a wide mix of topics. The list of tools mentioned is also quite exhaustive. Key Takeaways · Paid Web Analytics providers are better than the free ones if you need advanced reporting. The other reason is that the paid tools integrate well with other allied offerings/tools. (A project that I'm working on validates both these points) · Data needs to be actionable. No point collecting old data if the business cannot use it · Keep an eye on the competition using Google Insights For Search(contains search keyword data on[...] only), Google Trends (contains broad web usage data), Compete, Hitwise. Also check Google Ad Planner and Quantacast since both use self reported data. Most analytics tools now allow you to benchmark against specific verticals. · Use tools like page level/site level surveys to gather user feedback(kampyle, uservoice, opinionlab). The Voice of the Consumer is necessary to fill in the gaps Now if only we could get key sales and marketing folks to read this book and understand how much data is there for them to use Tools Web Analytics: Omniture, WebTrends, CoreMetrics, Google Analytics Mobile Analytics: Bango Analytics, [...], [...] Experimentation and Testing: Google Optmizer, Omniture Test and Target, Optimost, Sitespect Voice of the Customer: 4Q, iPerceptions, ForeseeResults, Ethnio Competitive Intelligence: Google Insights For Search, Google Trends, Compete, Hitwise, Technorati, Google Ad Planner, Quantacast Analytics Tags Audit: SiteAudit(ObservePoint) SEO gaps, Web Application Performance Management, more : Maxamine, Coradiant Page level/site level surveys to gather user feedback: Kampyle, Uservoice, Opinionlab Usability: Ethnio, Usertesting Analyze Actual Online Experiences: Tealeaf, Clicktale Information Architecture: OptimalSort, [...] Visual heat maps: Feng-gui.com, Crazyegg Keyword Analysis: Google Adwords Tool, Wordtracker, KeywordSpy Onsite Behavior Targeting Platforms: Audience Science, kefta, Netmining, BTBuckets(free) Paid Search Tools: Marinsoftware, Kenshoo, ClickEquations For this and other Web Marketing articles, my blog: [...]

Avinash did it again! His first book "Web Analytics - An Hour a Day" was a big hit and helped many in the industry get a better understanding of the foundational elements of web analytics as well as useful and practical tips for day-to-day measurement of marketing activities. Well, the web is evolving and measuring things on the web is getting more challenging but Avinash's new book comes to the rescue! If you want to get a fresh look at how to use/report/analyze traditional data and metrics, Web Analytics 2.0 is for you. If you are into cutting-edge web 2.0 mobile/video/blogging/twitter/etc. this book is also for you. And the book doesn't just cover how to measure and improve outcomes on your own site, but it covers qualitative data, competitive intelligence techniques, and social media tools and shows how to blend it all together to help site owners and marketers find insights and take action. If you are doing anything on the web (and I don't know what business can afford ignoring the web these days), then this book is a must read. It's definitely a must read where I work :). Excellent job again Avinash!

To Kaushik's credit he ventures into true analysis that was missing in `Web Analytics: An Hour a Day'. Early in chapter 3 he writes that "when people say "web analytics", they really mean web metrics". Kaushik was aware of the need, and has done a solid job of addressing the earlier analytical shortcomings. Some of the methodologies may not be mega-profound, but enough is done to whet the reader's whistle as to the possibilities. On the downside Kaushik's writing is irritating. He is very repetitive. For example, he overly evangelizes the need for context. While I fully concur with him on this need, how many times does he have to tell me?!?! In general his writing style is higgledy-piggledy. If he had an editor, his editor let him down. The book is a good 100 pages longer than it need have been. I saw the same not-getting-to-the-point in the one video presentation of his I watched. I was somewhat perplexed by Kaushik's Analytic Ninja. The analogy is inappropriate. I have always pictured ninjas as working surreptitiously, while the need is for the web analyst to become a visible and integral part of strategy development.

This book is like dark chocolate covered pretzels on top of walnut ice cream- blissful & enlightening... Whether you're a seasoned analytics practitioner or a newbie, you'll find Web Analytics 2.0 engaging and informative. Avinash breaks down hard-to-grasp concepts into digestible nuggets by pulling in relevant examples and using real analytics tools to illustrate each point. If you're in the process of vendor selection, chapter two will be your guiding light. If you're re-visiting your initial implementation, or are still trying to figure out how to use the tool you implemented, look no further. This book will help you figure out what you're looking at, isolate actionable metrics, and tell a story with the data that will drive your online and offline strategies. It doesn't hurt that Avinash is hilarious and paints a colorful picture with every insightful anecdote. Who knew web analytics could be this simple?

Avinash's book is highly recommended by the likes of Seth Godin, Guy Kawasaki, and many other masterminds of internet marketing. He comes with the respect of his peers and thousands of fans due to the popularity of his blog [...]. He does not need this review from me, but I feel I owe him as he has been my virtual mentor for the past three years. For beginners, I would highly recommend Avinash's first book: Web Analytics: An hour a day. It will change your approach from pulling data reports that your boss tells you are important; to creating an effective analytics program built with thoughtful key performance indicators and insightful recommendations based on user data. For intermediate to advanced analysts, this book should be your primary guide for the next 2 years of so. I recommend that you buy this book, change the way you look at data, impress your boss with your new knowledge, impress your sales department with your user insights, inspire your web development team to more useful segmented reports, get a promotion, get paid more (you will become more valuable), and live happy.