If your job involves numbers, you likely spend time graphically plotting it. Whether it’s for analyzing or presenting, we usually toss our datasets into our visualization tool (mainly because it takes one button click) and start visualizing it. The problem here is that we’re making content before knowing our intent, we’re making the software master over us instead of being its master.
Today’s guest says the visualizations that come from this won’t be intelligible, won’t make them motivated to act and won’t yield good decisions. However, he does passionately believe that when people who know how to read numbers, see it presented the right way, it’ll motivate them to make the right decisions.
Lee Feinberg graduated from Cornell University with a BS and MS in Electrical Engineering. In 2012 founded a consultancy to help data leaders create armies of trustworthy decision makers. He has worked in the analytics and data visualization fields for 20 years.
He is associated with Data Science programs at both NYU and the University of Chicago. When he’s not talking about visualization, Lee likes experiencing concerts – from the front row, and also hanging out with his wife and kids.
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.
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.
Johan van de Werken thrives best at the sweet spot between data, business & technology.
Graduating with a philosophy degree from the University of Utrect, my guest started his career as a journalist for several Dutch publications, writing about everything from events and pop culture to media, politics and economics. Around 2014 he switched from letters to numbers, working in CRO for several European e-commerce businesses. That led him to building dashboards and leveraging cloud platforms to turn raw data into usable marketing insights.
Working at an analytics firm that exposed him to BigQuery, he thought about sharing what he was learning. Seeing that the domain GA4BigQuery.com was available, he registered it and started posting there as a side gig. It got noticed by Simo Ahava, the founder of Simmer. That led Johan to release the GA4 and BigQuery course on their training platform. As we fast forward to 2023, GA4BigQuery is now a well-known resource for marketers. And its creator is now consulting full-time on data analytics under his own brand, Select Star. Except for when he’s having fun playing in a punk rock cover band.
