Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity (Anglais) Broché – 27 octobre 2009
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Note: CD–ROM/DVD and other supplementary materials are not included as part of eBook file.
Quatrième de couverture
Seth Godin, author, Tribes
Lots of companies have spent lots of time and money collecting data and sadly do little with it. In Web Analytics 2.0, Avinash Kaushik helps us grasp the importance of this underused resource and shows us how to make the most of online data and experimentation.
Dan Ariely, Professor of Behavioral Economics, Duke University, and author of Predictably Irrational
Kaushik takes the witchcraft out of analytics. If venture capitalists read this book, they would fire half of the CEOs that they′ve funded.
Guy Kawasaki, Co–founder of Alltop & Garage Technology Ventures
When people ask, who is the smartest guy in the room when it comes to online marketing? only one name comes to mind: Avinash Kaushik. His new book Web Analytics 2.0 should be on every marketer s desk. It s powerful, awesome and actionable.
Mitch Joel, President of Twist Image & author of Six Pixels of Separation
Shift to Data–Driven Decision Making and Leverage the Complete Power of All Web Data
The Web, online marketing, and advertising have been revolutionized in the last few years, yet the approach to using data has remained largely the same as a decade ago. Web analytics thought leader Avinash Kaushik presents the next–generation framework of web analytics in this exciting book that will dramatically enhance the ability of your organization to think smart and move fast.
In this book, Avinash lays out specific strategies and execution models to evolve from simply leveraging clickstream tools to incorporating the insightful elixir of qualitative data, experimentation and testing, and competitive intelligence tools.
While expanding upon the industry–shaping lessons from his bestselling book Web Analytics: An Hour a Day, Avinash explains how to measure, analyze, and act upon today′s quickly evolving web technologies and trends including social media, video, mobile, and online user–centric design options. As he updates traditional approaches, Avinash debunks myths, identifies traps, and reveals specific, simple and advanced methodologies to transform your thinking, making this book the ultimate guide for all web professionals.
- Discover the solutions for the hardest challenges, including multichannel analytics and multitouch campaign attribution analysis
- Quantify the holistic economic value of your website and measure macro and micro conversions for ecommerce, non–ecommerce, and B2B websites
- Profit from analytical methodologies that attack the holy trinity of search: internal site search, pay–per–click marketing, and search engine optimization
- Pinpoint the most relevant Key Performance Indicators for your organization and create actionable dashboards that drive change
- Master crucial emerging analytics fields including Twitter®, YouTube®, blogs, mobile, and rich–media analytics
- Leverage experimentation and testing to create truly customer–centric websites and innovate by failing faster
- Create data–driven bosses and organizations, and cultivate the skills and background you need for a successful analytics career
- Continue learning with four hours of video, an hour of audio, and valuable presentations, templates, and models on the CD
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After leafing through the book for a while I went back to the beginning and I really enjoyed the way Avinahs Kaushik links the content; bringing basic and important concepts and very advanced techniques side by side. The book has a friendly tone, i.e., it feels like walking down the street and talking to a friend. Avinash knows when to soothe the reader and let him know that this might be frustrating or difficult, he does not pretend to give all the answers.
A central theme on Avinash philosophy also in his previous book (Web Analytics: An Hour a Day) is that people will bring change, not tools. So, even though he proposes several techniques for choosing vendors, he puts in in its place: if you don't have people, you better look for them, no tool will help you. For every $100 you have, you should invest $90 on people and $10 on tools.
This book describes a holistic approach of the Web Analytics field which he defines as "the analysis of qualitative and quantitative data from your website and the competition, to drive a continual improvement of the online experience that your customers, and potential customers have, which translates into your desired outcomes (online and offline)."
The book treats all the aspects that need to be understood in order to have a successful web strategy: clickstream data, testing, Voice of Customer, social, mobile, video, you name it. In addition, you will learn about planning and growing a web analytics career, so if you are serious about your career, this book is for you.
Concluding, 'Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity' is a landmark on the Web Analytics field and a must buy for anyone looking to grow and succeed in the Internet.
That said, a web analytics book like this is really a reference book. I personally find it a lot easier to flip the pages and find what I'm looking for. And while the author does do a good job of starting at the novice or "reporting squirrel" level and leading the reader up to the expert or "ninja" level, this is not a work of fiction. You really don't need to read it start to finish in order.
The content, as others have said, is engaging and highly readable. Even if you have been practicing in this space for awhile, you will still learn much from this book. If you are new to the space, then this book is a requirement!
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
Very Satisfied= .5
Extremely Satisfied= 1
With these weights the scale is also -100 to 100.
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.
One of the best things of this book is that it helps to clarify the heaps of data and reporting you can get from the many available analytics tools. What data should you look at? What are actionable outcomes you want to measure? What are some ways to measure success of your site?
I'd go so far as to say that every site designer should read this book - not just analytics or marketing pros. This is because it has some great sections about how you should be testing the impact of site designs and changes. The book also includes a CD and one of the items is a usability checklist that every designer should have. And if you're interested in a career in analytics, there's even a chapter at the end dedicated to this - I'm happy in my job, so I didn't read this section, but he closes out with some ideas and advice on how to find the right people for analytics jobs you may need to fill.
It's difficult to make a book about data interesting - but Avinash Kaushik has definitely done so with this book. I've already given a copy of this book to a colleague knowing that he'll find it valuable.