Thoughts on “Google Analytics Integrations” by Daniel Waisberg

I’ve recently read the book “Google Analytics Integrations” by Daniel Waisberg. In this blog post I share a couple of thoughts related to the topics mentioned in the book.

I don’t really review it, other people have already done that. But if you are interested in integrating data into Google Analytics, the following thoughts will be helpful.


Who should read this book? Anyone who wants to combine Google Analytics data with something else (E-Mail campaigns, CRM Data, Adwords data, etc.).

What’s this book about? Getting more data into Google Analytics (not about getting data out of Google Analytics).

What’s in the book? One (really really good) section on Google Analytics Implementation best practices. One large part about the five custom data sources one can import (Adwords, Youtube, Search Console, AdSense and Mobile Apps via SDK). A second part about four kinds of other data sources (CRM, user data,…). And one section about Offline integrations (not so good).

Each section contains a setup & configuration guide and a basic “so now, what can I do with this additional data?” part. All the setup parts & configuration parts are up to date.

Now my thoughts on some individual sections.


Integrating data into Google Analytics

Generally, this book is about getting more data into Google Analytics. Is this always useful? In my opinion, if you are already working with Google Analytics, then more data, integrated data is almost always beneficial. The custom integrations (Adwords, Search Console,…) are easy to set up and should usually be implemented.

However custom data is much harder to handle. In particular because the integrations are not that great and don’t allow historical data changes (or only of limited scope in Google Analytics Premium).

If you are interested in general in integrated digital marketing data then I’d recommend to think more broadly about your tool framework. In particular:

  1. Most marketing automation systems provide by “default” (which usually means after 100 hours of setup & customization) integrations with CRM data, cost data, social media channels etc.
  2. CRM systems are the best place to store attribution data and they are usually capable of handling that. It’s actually pretty simple to get “marketing attribution” data from Google Analytics into a CRM system (say the very first interaction point be it adwords, social media or search).

Implementation best practices

This section of the book is great. The documentation practices in particular are good. I’d recommend that approach (of using Google Docs + a predefined form) as soon as you’re working with more than 2 people on an account (that includes 1 analyst + client). You can read a short version of the practices at http://online-behavior.com/analytics/implementation.

Adsense and Google Analytics integration

The section of the book states the following:

“… enabling a data-driven approach to optimizing content for Adsense revenue.”

which is something I would wholeheartedly discourage from. I think the appropriate way for a content producer to build a healthy business is to focus on providing value. Period.

Focusing on optimizing content for Adsense revenue using the reports you get out of Google Analytics will almost always lead to worse content and readers that carry less long-term
value to the content producer. Think about it, if you optimize your blog for instance for Adsense revenue really means you’re optimizing your content to fit to people who are more likely to click away from your site.

GWT and Google Analytics integration

Some critical thoughts. The author proposes to use the CTR as an indicator on how well the search snippet is performing.

I think this analysis is utterly misleading; Content, context & the search phrase in my experience have a much higher (magnitudes higher) influence on the CTR than the snippet (which in the exemplary case are probably alright anyway).

However there is a place for trying to spot really bad performing snippets. It’s in spotting snippets that are of either one of the following kinds:

  • * “Navitem1, Navitem2, Navitem3, …”
  • * “you will be redirected in 30 secs”

But that’s it.

Custom integration with key

in the custom integrations part user data and the new universal Analytics user centered tracking methology are mentioned a couple of times. Still, if you’re looking for an answer on how to integrate custom data into Google Analytics and finally get cross-domain tracking & marketing attribution to work you will be disappointed. The premise in this book is that you need a key. And getting a user key means getting them to sign in or at least to pass their e-mail ID. For some reason the author refrains from writing more globally about how one might identify users across devices and sessions.

Some thoughts in that direction:

  1. One can use IP reversal (in a B2B situation) to get a read on the organization (and use that pretty well to get “user IDs” or better company IDs for in my experience 10-20% of the visitors)
  2. Proper incentives. Identifying users is easy, getting them to sign in is hard. We thus need more creative ideas to get people to sign in (incentives) and ease of use. (For instance pre filled forms, auto sign-in based on cookies,…)
  3. Some deep learning. On websites where people eventually sign in, there is a good base of data (behavioral, timing, IP data -> geo location) to identify people already based on those things (with a certain probability) to the correct user (subset). I don’t think anyone is doing this yet, but it seems to me to carry enormous value.

Integrating Offline Data

This section is sadly one of the weakest. It’s ok to say that offline data is currently hard to integrate. That’s the truth. There are few use cases and the integration is not as seemingly as it should be. (stuff for a future blog post)

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