Today, we’re talking about the future of data with Google Analytics 4.
It’s been about 6 months since we all had Universal Analytics. It’s good to talk to others who use GA4 to do their jobs, to compare notes. Although GA4 is here to stay, it still has gaps that need bridging.
That’s why I spoke with Jason Hackenberry, Head of Partnerships from web development agency Arctic Leaf. Prior to Arctic Leaf, he held Digital Marketing and operations roles at Weatherby and Save Khaki United, along with roles in Merchandising.
What you’ll hear is from a virtual event he and I did in December 2023, on topics including
How Google is migrating users of its free version differently from its 360 version
How to capitalize on the information provided by GA4
The data you actually need vs. what you THINK you need
Tips on finding insights, reporting, conversion tracking and data retention
New GA4 features that can help your lead generation or e-commerce website.
GA4 is now our de facto analytics tool. Regardless of how familiar we were with the previous tool, GA4 is here to stay so we may as well get good at using it.
I’ve got just the person to make the transition relatively painless for us.
Our guest’s love for analytics was a happy accident after she worked in marketing at a company with a sales director. They told the executive team that marketing’s budget would be put to better use hiring new salespeople. But beyond having a warm fuzzy feeling in one’s tummy, it wasn’t clear marketing’s impact could be articulated in the way that executives talked.
Not willing to watch her department disappear, she dug in and found the data that showed marketing was having an impact. She took the evidence to the next board meeting and her department was able to continue with its work.
She chose to go out on her own so she could empower marketers to do as she’d done. She now heads up The Colouring In Department, a consultancy that has completed close to 230 GA audits now, and has trained thousands of people on how to get good at their analytics.
AI won’t end up being one thing, it will be present in many little applications – hopefully that will help us in our marketing. But what kind of AIs do we want? Are we looking at the ingredients that go into them?
Those are the kinds of questions innovations our guest considers as he makes AI models for healthcare and the retail marketing sectors.
Yash Gad is a data scientist, education advocate, and foodie. Founder and CEO of RingerSciences and Chief Data Scientist of Next Practices Group. He earned his PhD from University of Illinois Urbana-Champaign in Computational Biology, Neuroscience &, Biophysics and received his undergraduate degree from Johns Hopkins. He joins me from Austin TX.
Whenever your marketing is being assessed by an analyst, they will use one of two approaches.
The first is called Multi-touch attribution, which takes a customer who’s made a purchase decision, then puts weights on the touchpoints they had on various channels (Google calls their model ‘Data-driven attribution”) on the way to that point, to say which touchpoints were most influential.
The other approach they may use is Media Mix Modeling. From what previous podcast guest Kevin Hartman told me about MMM, it’s a ‘tremendous undertaking.’ It involves collecting and analyzing historical data in different geographies at different times of the year: sales figures, both legacy and digital marketing channels, and external factors like economic indicators and even weather. It has its own jargon: Incrementality, ratios, betas, impact on objectives. Then there’s the math. It uses regression methods, both linear and non-linear, Frequentist vs Bayesian statistics.
I get so overwhelmed with these modeling solutions, it’s like the old Who’s On First skit. I needed someone who would sort this out for me.
Our guest has been a consultant in the marketing and digital analytics space for 15 years. I’m currently focusing on helping clients quantify the impact of their marketing efforts using Marketing Mix Models, experimentation, and various attribution methodologies.
He is so passionate, he started a newsletter called MMM Hub
He graduated from Carnegie Mellon with a Masters degree in Information Technology, focused on Business Intelligence & Data Analytics.
Jim is great at showcasing other people in the analytics community -He truly believes that all of us are smarter than any one of us. He, along with Simon Poulton, co-host the MeasureUp podcast.
He talked with me from his home in Pittsburgh. Let’s meet Jim Gianoglio.
Market Mix Modeling (MMM) 101 – This is a good intro-level article highlighting the important high-level concepts of MMM
A Complete Guide to Marketing Mix Modeling – although this article/site is littered with a bunch of ads, the content is actually pretty good. It touches on the concepts as well as providing some code snippets for R, Python and SAS.
Videos / Courses to help get started with modeling:
MASS Analytics – Marketing Mix Modeling Master Classes – (free) 14 courses (YouTube videos) – very well done, starts at a beginner introduction to MMM and goes all the way through advanced modeling techniques. It’s about 3 hours in total.
Marketing Mix Modeling 101 – (free) online course (YouTube videos). This is 2.5 hours over 5 courses that focuses on MMM using Robyn, so is good if you’re comfortable using R.
You took the tags the free tools gave you and installed them on your site, you configured platforms and poured over their reports, you connected the systems and even hired developers to hook everything up to a database. And yet, you have little value to show for all the work you’ve put into your company’s analytics
You feel the analytics platforms are backing away from their responsibility to streamline all this. Instead, the answer from the largest of the bunch, Google, is they’ll hold onto your data if you use their newest tool, BigQuery, and pay them money to store your data …or is it their data… on it.
The bad news is summed up in a 2023 book whose euphemistic name is “You’re data is flawed”– don’t want to get an explicit rating for using the actual name
It was written by someone who empathizes with our situation and who lays out in the book the steps needed to generate positive financial returns for our analytics investment.
Our guest Mark McKenzie started his career in London, but moved in 2014 to sunny New Zealand to work for a data-focused digital agency. That led to him founding and growing an analytics firm that served clients locally and in the UK, Australia, and the US. Following the sale of that firm in 2022, he moved with his family back to the not-so-sunny UK. where he’s consulting with on digital analytics
His focus on analytics can be seen through his volunteering at events such as ‘MeasureCamp’ and ‘Web Analytics Wednesdays.’ Let’s talk with Mark McKenzie.
With Federated IDs, a company personalizes an experience for someone using digital data that was sourced (but not shared with the company) from multiple external systems.
The DIKW Pyramid of Data, Information, Knowledge, and Wisdom
146.879456 146.879456 Admitting our data is f*%#ed 622.046944 622.046944 prior to fixing data, must treat it as an asset 2369.560249 2369.560249 Fixing data we keep internally 3287.677599 3287.677599 Book and Mark’s contact info
