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Google Analytics: For Power Users Review

My Google Analytics Experience

Google Analytics for Power Users Certification
Ted GQ’s Google Analytics for Power Users Certification

In this post I will reviewing the Google Analytics: For Power Users. This will allow me to reflect on my personal learning experience and give a objective review of its practically. Before, I begin through my first 3 months of being a digital marketing professional, and taking Google Analytics For Beginners & Advanced I utilized a lot of this material already. Whether it was out of necessity for specific business problems or me fiddling around with the software.

Unit 1: Understanding users

This is course hosted by Krista Seiden, Analytics advocate for Google. This course attempts to be specific to relevant business questions.

The first segment utilizes a very simple segment comparison where you take converters and non-converters. This are 2 built in segments that take users who have completed greater than 0 transactions or goal completions. We are prompted  to answer these questions:

  • What was the behavior of users who converted compared to those who didn’t?
  • What were the demographic characteristics of users who converted versus those who didn’t?
  • What are other characteristics of users who converted?

I enjoyed this portion because for experienced Google Analytics users they can start answering these questions in their head. This is very business specific to the user whether you can see strong correlations between converters & non-converters.

The walk through gives some more obvious signs like new & returning users. Also going into depth to look at country, language and location for insights. Something I believe they could more in depth on would be the affinity categories. They are oddly specific categories that I believe normal people would never put themselves in. Maybe google may need to take a note from Facebook with interest targeting that is not Google+. Overall, this section gives a good summary for someone that was only taking these courses and not actively solving business problems. They also give the insight that a mobile awareness campaign would be superior with a close on desktop.

What Platforms Should You Develop or Optimize For?

In this section they start to target different devices and find a correlation between their operating system and conversions. They really push the promotion of pivot tables and advanced filters to find an answer. I believe this can be extremely helpful if you know what you’re looking for. Most people won’t be looking to optimize Tablet performance on Opera because it has a 50% conversion rate on 10 users. This is more of a heads up section for users that have high level macro conversions in check, and want to get into the trenches.

Assessment 1

The assessments are relatively easy if you go through the course and actually pay attention. However, you may have to do it once or twice if you do not intuitively use analytics yet.

Unit 2: Attract High-Value traffic

Unit 2 I believe gives more of a review type of section that I believe Beginner & advanced already touched upon.

  • Which traffic sources have the highest conversion rates? Which have the lowest conversion rates?
  • Which traffic sources bring in the most revenue/highest goal value?
  • If you’re tracking  : Which sources drive the highest-value transactions?
  • And which generate the highest user engagement? 

Simply sorting traffic sources from descending order you can give the eyeball test of what is blatantly doing bad. They also dive into your non-transactional conversions such as registrations. Furthermore, a reminder to user advanced filter to sort by number of sessions.

Analysis of eCommerce performance of traffic sources suggests that the best practice to maximize revenue & average order value. Overall this section to gives a macro level look at your marketing budget and where it can be best spent.

How Can you customize your channels for increased action-ability?

I enjoyed this section thoroughly because they give you hypothetical scenarios. The examples very realistic and relatable because the goal was very vague but the problems specific! In the first case a social media team is trying to take their ‘Key Influencers’ into a separate channel to see their impact more.

Google Analytics Channel Grouping
Example of Channel Grouping in Google Analytics

You must go to admin ‘Channel Groupings’ and reorder the influences above the referral channel so that it wouldn’t be filtered together. This was something that I did not know about so it was informative.

In the 2nd scenario the email marketing team had a newsletter and a standard email campaign. They wanted to separate them to see the newsletter performance and its loyalty to consumers.

When should you send your newsletter emails?

This section was extremely helpful and useful on the perspective of custom reports. It utilized timing of marketing activities based on when your users typically visit your website. You can see this insight only on the analytics home page if you don’t make this report.

Unit 3: Improve Site Engagement

In this section there was a lot of discussion of key performance indicators  and Google Tag manager(GTM) content oddly.  It was good insight but you would need to be a JavaScript developer or GTM certified to make most of it actionable. They also discuss the various sharing method via PDF, sheets, etc. Google data studio also gets an honorable mention for its extreme helpfulness.

What pages are helping conversions?

In this section they discuss landing pages and very simple KPI metrics for its helpfulness. Also, they go into page value and what it actually means. This is helpful for anyone trying to compare apples to apples on different page values to a business. Furthermore, they go into more custom segments and reverse goal path. Reverse goal path is a good idea if you have a really long funnel and want to know where everyone is coming from.

Unit 4: Improve Product Performance

This section really goes in-depth on using enhanced e-commerce. Which can give you really great insights on user behavior on add to cart, product interaction etc. The problem is I believe 90% of analytic users don’t have this. Those that do only 1% of them can make relevant action on the data.

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