INBOX INSIGHTS, May 26, 2021: Google Analytics Goals, Marketing Technology QA, Salary Survey

Google Analytics Goals, Marketing Technology QA, Salary Survey

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How To Set Goals in Google Analytics 3 Compatible with Google Analytics 4

A common complaint we’ve heard about Google Analytics 4 is how very different it is to Google Analytics 3 (Universal Analytics, what most companies use). One of those friction points tends to be the fact that goals in GA3 and conversions in GA4 behave differently. However, there’s a way to adapt your GA3 goals to make the process of migrating to GA4 much easier; in short, stop using anything except event-based goals.

We’ve got a walkthrough of how to set up GA3 goals that are compatible with GA4, shortening the process in GA4 by 50% of the effort, in this 9-minute YouTube video:

Setting up GA3 goals to be more compatible with GA4

Click this link to watch on YouTube now »

Why does this matter? We know at some point (but not when) Google will stop supporting the older GA3 software. It won’t be overnight or even soon, but it will happen. More important, all of Google’s development efforts are focused on GA4, so new features and capabilities will appear exclusively in that platform. Taking small steps like syncing up your goals between GA3 and GA4 will make life much easier when you do migrate because you’ll already have a lot of data stored in GA4 for analysis.

And that data part is critical. Google – and every other big tech company – has been making ever greater use of artificial intelligence to drive operations. At this week’s Google I/O tech event, Google announced new machine learning models that will substantially impact marketing, including new ways of searching. It’s all but certain some of that data will be made available to us in Google Analytics 4 – but only if we have it turned on and collecting data. More important, for things like optimization of our websites, having goals set up in GA4 allows Google to start calibrating its machine learning models to make more helpful recommendations for us.

AI needs data, so the sooner you’ve got GA4 up and running, collecting the data you need, the more useful it will be to you when you do eventually make the switch.

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Binge Watch and Listen

In this episode of In-Ear Insights, Katie and Chris talk about code quality assurance, process management, and how often you should be updating your standard operating procedures. What happens if you don’t keep your processes and code in good working order? Tune in to find out!

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Last week on So What? The Marketing Analytics and Insights Live Show, we looked at competitive benchmarking using Google Analytics data.

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Data Diaries - Interesting Data We Found

This week, we look at data from the weekly Tidy Tuesday data analysis contest. For those unfamiliar, the R for Data Science community publishes weekly challenges to analyze third-party datasets. Two weeks ago, the topic was a massive compensation survey. We’ve taken the data and extracted out only the folks working in marketing, advertising, or PR. Let’s see what the numbers say:

Marketing Salary Survey

What do we see? In general, compensation increases with years of experience, though some folks in their first year do pretty well, better than their peers in the next experience bracket.

In terms of pay by currency, when converted to US Dollars at today’s exchange rate, we see that people being paid in US Dollars make the most money by a substantial margin; people who make the least are paid in Japanese Yen, followed by Swedish Kroner. People paid in US Dollars also make the most in bonuses.

Finally, when it comes to the gender gap in pay, it’s still quite sharp; people who identify as men make 17.5% more than people who identify as women, and 51.6% more than people who identify as non-binary.

What should we take away from this data? There are definitely some locations which are more favorable for higher pay; in a global economy with fewer borders thanks to technology, finding a high-paying job while living in a low-cost locale may be a great strategy for maximum benefit. We also have to pay careful attention to equitable pay to ensure that compensation isn’t biased by protected classes like gender, physical age, race, or sexual orientation.

Methodology: Trust Insights used data from the Tidy Tuesday May 18, 2021 challenge, which is data from the Ask a Manager website. Data is self-reported and the survey was originally conducted in English, so there’s a bias towards English-speaking locations. The timeframe of the data is April 27, 2021 – May 26, 2021. The date of study is May 26, 2021. Trust Insights is the sole sponsor of the study and neither gave nor received compensation for data used, beyond applicable service fees to software vendors, and declares no competing interests.

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