INBOX INSIGHTS, February 7, 2024: Building Requirements with AI, GA4 Diagnostics - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: Building Requirements with AI, GA4 Diagnostics (2/7) :: View in browser

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Using Generative AI to Build Requirements

When I was a project manager overseeing developers, our biggest struggle was agreeing on the level of detail needed for requirements. It was a constant source of contention. As a PM, my goal was to make sure we knew all the moving pieces, potential pitfalls, and unlikely (but still possible) outcomes. The development team, along with many stakeholders, wanted to push buttons, move faster, and start building.

Because the team wanted to move faster, that was usually what happened. Requirements be damned!

To make a long story short, the team often found that development took longer and was less directional. The stakeholders would get frustrated that there was nothing to show for all the money and time invested, and I was unable to quote an end date.

When I started working with Chris, I ran into many of the same issues. He wanted to move fast, I wanted to create documentation. If you’re wondering why I’m so keen on requirements and documentation it’s simple. Taking the time upfront saves time later on. It allows for faster scalability, process development, and repeatability. You have a roadmap for what you’re doing and can pinpoint where your code goes wrong more easily because you know what to expect.

Think about the home you live in. Whether it’s a condo, apartment, house, or something else, you want a solid foundation. You’d hope that the team that built it spent a little more time on the planning and structure. You’d want assurances that the support beams were level and properly secured. This upfront work takes a little longer so that the rest of the building can fall into place. Everything else about building the house should be a repeatable process. Just like software development. Once the foundation is in place, you follow the plan and know what to expect.

You can, and should, use the 5P Framework to get your basics. As a reminder, the 5Ps are Purpose, People, Process, Platform, and Performance. Depending on what you’re creating, you’ll want to go deeper into each of those. This is where you can use generative AI to build requirements.

We tend to forget about the “chat” in ChatGPT. We approach the system like we need to have all the answers to get the desired output. However, we can use the system to help us figure out what we need.

I want to encourage you to chat more with your generative AI system. You can still structure your questions like prompts. A great resource is our RACE Framework, you can get your free copy here.

Some great questions to ask are:

What am I missing?

If I’m building a (insert thing) what are the risks I’m not thinking about?

What questions haven’t I asked?

Any other considerations that people forgot about when building (insert thing)

You can even go so far as to ask your generative AI system, “What questions should I be asking?”

We all have blind spots. This is why peer reviews and paired development are essential. Other people can catch what we miss. Starting with generative AI can save you some time before you bring your business requirements to the decision-makers.

Are you using generative AI to build requirements? Reply to this email to tell me or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the complexities of data privacy in the era of generative AI. They offer practical tips on safeguarding personal and company data, emphasizing the importance of understanding terms of service and the implications of sharing information online. You’ll gain insights into how different AI models handle data and the significance of opting for secure, private AI solutions for sensitive information. Tune in to navigate the challenges and opportunities presented by generative AI in a data-driven world.

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Diagnostic Reports of GA4 Health, Part 1 of 4

As we covered recently on the Trust Insights livestream, Google Analytics 4 can be a bit cumbersome to use. So starting this week, I’ll be sharing a four part series on diagnostic analytics for GA4, to understand whether or not your GA4 is working correctly. It’s important to note that we won’t be examining GA4 for marketing purposes – this is bare bones, bare metal “is it working?” Stuff.

This week, let’s start with one of the most important visualizations: your source/medium chart. I personally extract the data from GA4 via the API but you can easily build this chart in seconds in the Explorations menu. Choose session source/medium as your dimension, and sessions as your metric, then put up the top 25 or 50 rows:

Here’s what we’re looking for.

Direct traffic: in general, direct traffic shouldn’t exceed 25% of your overall traffic for most websites. Exceptions would be a site where a login page is often bookmarked and returned to. (You can filter out that page if you want to see this chart more clearly, just add Page path and query string as a dimension and filter it in the filters section). When direct traffic exceeds 25% under normal circumstances, it’s an indicator that you may have content out there that has no tagging, like naked links in email programs. It’s a warning sign to check your UTM tagging governance.
(Not set): Anything marked (not set) is trouble. Not set means Google saw the traffic coming in and it was valid, but it had no tracking data. For combinations that look like this: Email/(not set) you have a tracking code problem. One or more parts of your UTM tracking codes are not set up properly or malformed. For (not set)/(not set), that can be both tracking code issues as well as devices and browsers with security settings turned all the way up.
Capitalized anything: generally speaking, GA4’s default channel groupings rely on a fairly strict set of guidelines, one of which is that the medium tag should always be in lower case. A source medium like Facebook/Social will NOT be categorized as Organic Social in the default channel groupings. The correct version of this is facebook/social or better, facebook.com/social to ensure the traffic is going into the right default buckets for attribution purposes.
Campaign information in source/medium: it is technically true that UTM tags are arbitrary, and as long as the query parameters are structured properly (utm_source, utm_medium, etc.), GA4 will ingest what you put in those fields. But some pieces of the software like attribution and channel groupings only work properly out of the box if they follow Google’s guidelines. That means not doing things like email-campaign-jun-23/email as a source/medium – that will make attribution reporting more complicated than it needs to be. Campaign information should always be set in the utm_campaign tracking tag.
Garbage: some bots will add garbage traffic to your GA4 instance, like things that look like this: gwefc-asxcyt-sdfy/email. This is garbage traffic from bots, and is something you’ll want to work with your implementation team to exclude entirely, as it’s worthless.

It’s important to remember that despite your best efforts, other traffic will find its way into your Google Analytics installation that will NOT adhere to best practices. You only have control over what you do – but if you’re adhering to best practices, then it becomes clear very quickly what traffic is yours and what traffic isn’t. For certain, you want to ensure any traffic you’re paying for (ads, influencers, affiliates, etc.) is tagged properly so you can do ROI calculations later – that traffic should always be prioritized for cleanliness first and foremost.

In the next post in this series, we’ll look at events and conversions.

Shameless plug: if you want these analyses done for you, rather than you doing them yourself, contact us.

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