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.
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.
Data warehouses are amazing things: you can toss all kinds of information into them then pull mind-blowing insights out the other end. This feat can happen because you’re connected to outside systems holding their own database tables. A copy of whatever has recently gone into the table is taken out and shot through a data pipeline and pushed into your data warehouse. But today’s data stacks contain Multiple clouds, hybrid environments, and so many data pipelines the programs in charge of monitoring and logging the flows almost can’t manage them. It becomes overwhelming to manually check and ensure the quality and integrity of the data. The more sophisticated the systems, the more errors creep into the data. If we rely on flawed data, the outcomes and insights we generate will be equally flawed. This is where data observability comes in.
In this episode you will hear about something called an observability platform. It identifies real-time data anomalies and pipeline errors in data warehouses. Now there’s a twist here because we’re in a cloud computing environment that charges by number of computing cycles. You don’t want an observability tool that’s another pipe accessing client data and running up the meter. The good news is there’s an easier way to detect when data has gone awry, by comparing log files – basically metadata – they are just as effective at alerting you to problems.
If you’d like what this is doing described in a completely non-technical way, think of Hans Christian Andersen’s Princess and the Pea. There is a girl who comes to a castle seeking shelter from the rain claiming to be a princess. The queen doubts whether she is truly of noble blood, and offers her a bed, but this bed has twenty mattresses and twenty down-filled comforters on it. A pea is placed underneath the bottom mattress to test if this girl detects anything. The next morning, the princess says that she endured a sleepless night; there must have been something hard in the bed. They realize then and there that she must be a princess, since no one but a real princess could be so delicate.
I spoke with Yuliia Tkachova, the co-founder and CEO of Masthead Data, a company which recently received $1.3M in a pre-seed round. Originally from Ukraine, Yuliia came to found Masthead after work that convinced her of the need for an observability solution. She had roles as a Product Manager roles at OWOX BI and Boosta, where their data solutions encountered problems. Prior to that, she did marketing for RAGT. She has Bachelors and Masters degrees from Suma State University, specializing in MIS & Statistics. She also serves as an Organizer at MeasureCamp, a volunteer community where analytics professionals come together to learn.
Analytics is something that everyone says they want, and some brag that they can analyze very well. But few people know what investment’s required to build a quality analytics function, and even fewer are good at justifying its value.
Our guest Martin McGarry is so passionate about analytics, as you’ll see from his backstory, if anyone can articulate the business value of analytics, it’s him.
After completing a Bachelor of Science from The University of Manchester and studying at the University of Cambridge as a Doctoral Candidate, our guest worked in the UK analytics practice of a global Management Consultancy. Due for a change after 6 years of that, he moved to Ottawa Canada and founded his own consultancy so he could offer a more independent approach. A while later started the firm he’s been leading for nearly 15 years, Bronson Analytics.
In 2018 he began a recurring event in Ottawa called Beer & Analytics, which draws hundreds from the field together for learning and socializing. In 2022, the event went outside of Ottawa for the first time, being held in Toronto.
It’s fitting, given how this is Funnel Reboot’s 150th episode, that we veer off of the standard format and dig into a niche within marketing that’s becoming a de facto part of every marketing function and is dictating new skills that every marketer must learn. I’m talking about marketing analytics.
The winning analogy of the conference made fun of how loosely people add the term AI to everything. Kenya Gillette used soccer to characterize this. Imagine you were the person who designed the game, documented all the rules and scouted the earth for people to play it.
Then someone says they can make soccer better…by simply playing the game ON THE MOON. That is the equivalent of saying that any business activity can be made better by adding AI to it.
We’re ending our series on advancing analytics today. We’ll focus on the software that, for many marketers, is at the core of this field: Google Analytics. It was introduced in 2005 as a read-only tool that tracked basic info on websites. The name has stayed the same over these last 18 years, but so much else of Google’s technology landscape has changed: they have released many other tools: Tag Manager, Google Data Studio, now Looker Studio, ways for API’ing between systems in and outside of Google, and most importantly a place where it can all be managed – Google cloud.
The new Google Analytics GA4 was born of this environment.It’s been criticized as being immature since it lacks features that were in the old UA interface. However, if judged by how well Google integrated it into their stack and how much those with technical skills can do with it, we would rate it as ready for prime time. Add to that the fact – in a matter of weeks Google teams won’t have to maintain two analytics tools, and they’ll get to focus exclusively on just one – GA4.
We can debate Google’s motives for tightly integrating GA4 with the whole Google cloud. I’m not wading into how good or evil it is to give away a product and hope users try the paid cloud platform that comes with it. But I’ll say that using Google Analytics with these other pieces lets you do much more with your data that you couldn’t do with the old GA. And when tools directly or indirectly make money for Google, that incents them to keep those tools and keep improving them. I’ll leave it at that.
Our real question is how do we economically benefit from the available tools. And that’s what our guest is going to tell us. His book which came out in early 2023 is called “Learning Google Analytics: Creating Business Impact and Driving Insights” The business impact spoken of there doesn’t mean using GA4 as a standalone lookup tool. Using it like that and ignoring what’s possible, would make the rest of the whole Google stack seem (to quote from the Movie ‘Contact’, “like an awful waste of space”
Our guest knows the value of integrating Google’s tools for many world-wide brands, as he’s done through digital agencies on his own as a GA consultant since 2008. Mark Edmondson has helped turn the out-of-the-box Google Analytics into a package that automatically describes, predicts and activates better marketing outcomes. He currently works at Devoteam as their Principal Data Engineer.
Mark grew up in Cornwall, UK before gaining his Masters in Physics at Kings College London. He now lives in Copenhagen with his wife and two children, enjoying playing music and cycling around the many lakes.
Today we’ll talk about automated marketing data pipelines for reporting and even activation. If that last sentence didn’t make total sense to you, don’t worry, our guest is going to tell us why we need it and how we can set out implementing it.
The place where Noah Learner got his start was on the island of Nantucket. It’s around 20km (15 mi) long, which is small enough that most visitors leave their cars on the mainland, come across on the ferry and rent a bike.
Noah’s journey started with a job at one of the island’s bike rental shops. Over the next decade as he rose to become the company’s GM, he became convinced of the power of SEO for driving traffic.
He relocated to Colorado, at various points he worked in corporate SEO roles and worked out on his own. He even mashed up skills from his past to serve businesses in the pedal-powered rental market, calling it ‘bike shop SEO.’
In the past 4-5 years he’s built cutting edge SEO tools using Google Cloud technology and has shared how to deploy them by speaking at MozCon, SearchLove and LocalU and in the agencyautomators.com community which he cofounded. he’s just taken on a new role that has tool-building baked into his mandate, as Director of Innovation for Sterling Sky.
When not at work, he loves doing typical Colorado things like fly fishing and skiing, along with family-friendly activities like hiking and camping where their two dogs can tag along.
People/Products/Concepts Mentioned in Show
DBT, or Data Build Tool is a set of scripts or programs that, when deployed, cleans and transforms a data source’s original tables and fields into a usable format for analysis.
You have strategized and run a program with positive results, you would think that you simply show leadership your data and then you can sit back while they lavish you with praise.
Not quite.
If it isn’t packaged right, it won’t have that hoped-for impact. Remember Maya Angelou said ‘people will forget what you said…what you did….but will never forget how you made them feel.’ That feeling is conveyed through stories. To find and tell those stories, you need business intelligence and data visualization tools. I sought out a Googler who’s an expert in their tool for doing this: Looker Studio.
Sireesha Pulipati is an experienced data analytics and data management professional. She has spent the last decade building and managing data platforms and solutions, and she is passionate about enabling users to leverage data to solve business problems.
Sireesha holds a master’s degree in business administration and a bachelor’s degree in electrical engineering. Her work history spans multiple industries – healthcare, media, travel, hospitality and high-tech
She is currently at Google as a technical lead, helping with the business intelligence and analytics strategy for internal teams.
John Thompson is the author of the 2020 book Building Analytics Teams, and a 35-year technology executive in the fields of data, advanced analytics and artificial intelligence (AI).
He is Global Head, Artificial Intelligence (AI) at EY. John was an Executive Partner at Gartner. He was the global advanced analytics and AI function at biopharmaceutical company CSL Behring, where he led an analytical applications team.
John has built start-up organizations from the ground up and he has reengineered business units of Fortune 500 firms to reach their potential. He has directly managed and run – sales, marketing, consulting, support and product development organizations.
He has been a technology leader with expertise and experience spanning all operational areas with a focus on strategy, product innovation, growth and efficient execution.
Thompson holds a Bachelor of Science degree in Computer Science from Ferris State University and a MBA in Marketing from DePaul University.