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.
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Top reviews from other countries
Martin
You'll get a good general idea of what's ahead
Reviewed in the United Kingdom 🇬🇧 on July 4, 2015Harry's Mum
Brilliantly written and a great reminder of measuring the few most important things!
Reviewed in the United Kingdom 🇬🇧 on January 10, 2022Marco Grossi
Good book, but it needs to be updated big time!
Reviewed in the United Kingdom 🇬🇧 on May 2, 2017Lutherking Ngolle Etugengolle
Aninash is truly an Analytic God
Reviewed in the United Kingdom 🇬🇧 on December 25, 2015Robert Collard
It provides a good background, although sone what out of date
Reviewed in the United Kingdom 🇬🇧 on June 23, 2015
