Category: Analytics/AI

Analytics Advice

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Episode 136: Building Analytics Teams, with John Thompson

Building Analytics 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.

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Tom Davenport

Data Science modules on Coursera by U Michigan’s Dr Charles “Chuck” Severance

Shawn Rogers

Diagram showing work of analytics being approached in either an artisanal or a factory style; used with permission

Episode Reboot. John’s book  “Data for All” published in 2023

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Episode 135: Data Clean Rooms, with Puneet Gangrade

Data Clean Rooms

Disclaimer:When I bring technology vendors on the show, you should know that they are not sponsors or affiliates. They’re simply here to give you a broader perspective.

Puneet Gangrade is a Customer Success Engineer, an active amateur soccer and badminton player and a resident of New York City who originally hails from Indore, India

He works at Habu, a data clean room software company, with the goal of creating reliable data and media strategies for all of his clients. He is responsible to work with Habu’s Engineering, Product teams, and clients on end-to-end workflows including clean room adoption, onboarding, planning, implementation, and growth. Habu works across distributed data platforms that allow agencies, publishers, advertisers, and their partners to truly unlock the potential of data and collaborate in a privacy-safe manner. 

He has been a data clean room enthusiast since the beginning of 2018. Previously, he worked as a Digital Analytics Specialist at Time Inc till Oct 2017 and as a Data Science Team Lead at MightyHive, an adtech agency from early 2018 to mid-2022. He has a BS in Computer Science and earned his MS in Marketing Intelligence from Fordham University Gabelli School of Business, with a concentration in Marketing and Analytics.

Amid the rise of advanced measurement techniques in media and other industries, he likes to keep learning about various data clean room technologies and constantly think of use cases in media, finance, CPG, pharma, etc. where data clean rooms could be leveraged. He continues his passion for everything related to data clean rooms and data collaboration at Habu.

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Puneet’s LinkedIn Profile

Puneet’s Twitter Profile

Company where Puneet works: Habu

Description of Walled Gardens

Concept of an Escrow (example given with finance can also be applied to data)

Episode Reboot: Read this background article: Data clean room: A silver bullet to a post-cookie transition?

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Episode 121: Looker Studio 101, with JJ Reynolds

No about analytics is complete without talking about how to visualize data. A picture’s worth a thousand words, right? In the past, when data was in a spreadsheet, it only took hitting that ‘chart’ button to render some numbers visually. But for many of us, this experience has moved to a browser where we build our own report, either in an interface like GA or in a standalone visualization tool. 

I’m talking with someone who’s really good at a visualization tool, but came by his power-user status in a roundabout way. My guest was born and raised in Hawaii.  After he got his Marketing degree, he worked at an ad agency where he did everything from videography to FB and G-ads writing. That, and also building a few websites, stoked his curiosity for how the tagging and the analytics behind all these things worked. He didn’t just want to get at raw data, he wanted actionable data.  He felt that to optimize his marketing, if he only knew how to present visitor behaviour data visually, the answer would be apparent – even obvious.  

He went down YouTube rabbit holes, asked around at conferences, and eventually landed on the beta of Google Data Studio, now called Looker Studio.  And that’s the Data Visualization tool we’re talking about with JJ Reynolds, who joins us from Reno NV.

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You may want to check out these related episodes:

Episode 9: How Dashboards Make the Whole Funnel Work
Episode 93: Visualizing & Making Data Valuable, with Eric Boissonneault
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Episode 120: The Analyst’s Role in Marketing, with Tim Wilson

We had to see it coming. We marketers have been getting more and more data. From on-premise CRMs and site logs in the early days, then marketing SaaS products and API calls that pipe data in all directions, there’s data everywhere. It goes without saying that we need help making sense of all this data. Most marketers wouldn’t consider themselves natural statisticians. Enter the analyst, who knows how to wrangle, normalize and visualize those data points, and maybe even get it cleaned and dressed for dinner. 

There are marketing teams who’ve got analysts onboard, but it isn’t an industry-standard practice just yet. Some leaders in the analytics community make the case elegantly of how this role helps marketers. And I’d count my guest today as being a vocal advocate for why we need analysts.  

In his day job, he is Senior Director of Analytics at Search Discovery. But that only scratches the surface of all that he does. He’s also a perennial  conference speaker and writer on many topics in analytics.  To me, he typifies how one can be a digital analyst despite having a non-analytics background. In his case, he obtained an Architecture degree before entering the field.

Joining me from Columbus, Ohio, let’s hear from a man who some call the quintessential analyst, Tim Wilson

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Episode Reboot:

Check out the podcast which Tim co-hosts, Analytics Power Hour

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Episode 119: Digital Marketing Analytics, with Kevin Hartman

As Google’s Chief Analytics Evangelist, Kevin Hartman is responsible for leading the design, implementation, and evolution of programs and approaches that help businesses around the world realize the opportunities presented by data. 

Kevin has a proven track record of building large, global, high-functioning analytics organizations from scratch and deep experience in leading large profit & loss centers and cross-functional teams, identifying business opportunities, and creating effective marketing programs. He has also written “Digital Marketing Analytics: In Theory And In Practice” which is now in its second edition.

Kevin’s decades of work in the digital analytics space, with most of that time spent leading large analytics teams at a major global advertising agency and Google. He has taught analytics for nearly 10 years at Universities near to his home, such as The University of Chicago, The University of Notre Dame, and The University of Illinois.

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Episode Reboot:

look into Kevin’s course on ELVTR

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Episode 118: Converted, with Neil Hoyne

Converted, Neil Hoyne

In digital marketing, we’re all striving to do what works. Yet whether we’re in-house or at an agency, we’re basing our definition of what works on a small sample size. Honestly, none of us can zoom out far enough to the general traits of successful marketing. That is, unless you’re someone who’s tasked with measuring marketing data at the organization with the single-largest quantity of it on the planet. 

My guest has gained a lot of insight on successful sellers in his role as Google’s Chief Measurement Strategist, where he has led over 2,500 engagements with the world’s biggest advertisers. He is a Senior Fellow at Wharton and holds degrees from Purdue University and UCLA. And in his book “Converted: The Data-Driven Way to Win Customers’ Hearts” the difference (I’m simplifying here) is that the  best ones humanize their funnels for their buyers. 

“Wait,” you say, “we already know  how to treat people nicely, we’ve known how to do that since humans have been around. You’re right, yet it’s surprising how we lose the human element is when we move commercial interactions online. My guest wants us to learn – or more correctly, relearn how to make our marketing more human. 

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Episode Reboot

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Episode 117: Marketing Artificial Intelligence, with Paul Roetzer

Marketing Artificial Intelligence

Paul Roetzer graduated with a journalism degree from the E.W. Scripps School at Ohio University and a few years afterwards he founded Ready North (formerly PR 20/20). In 2016 he founded the Marketing AI Institute. The idea for such an organization came from what Paul saw when AI began impacting his agency. He thought the only way marketers like him could work alongside AI would be by better understanding its capabilities. 

Part of their vision of educating marketers is through an annual event, and in 2019 they held their inaugural Marketing AI Conference. MAICON was on pause during lockdowns, but it came back in 2022.

In 2022, He and co-author Mike Kaput published the book we’re talking about, Marketing Artificial Intelligence. The book draws on years of research and dozens of interviews with AI marketers, executives, engineers, and entrepreneurs. He has also authored The Marketing Performance Blueprint (2014) and The Marketing Agency Blueprint (2012). Through his podcast and as a conference speaker, Paul makes AI approachable and actionable for marketers. 

He and his family live in Cleveland, Ohio. 

People, Products and Concepts in the Show:

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Episode 103: Data First Marketing, with Janet Driscoll Miller

If you’ve listened to this show, you know that I believe we can base all the marketing decisions we make on data. This fourth book in our Marketing Books summer series talks with an author who’s extensively described how we get data in a form that helps us make decisions.

Janet Driscoll-Miller brings over twenty years of search engine marketing experience to Marketing Mojo and is considered a leading expert in her field. Janet has spoken at search engine and marketing conferences including Digital Summit, SMX Advanced, MarketingProfs B2B and Pubcon. Janet is also a frequent guest lecturer at colleges and universities including the University of Virginia and James Madison University.

In 2020, she co-authored Data-First Marketing with Julia Lim.

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Episode 101: Age of Customer Equity, with Allison Hartsoe

Think of the data you have on your customers as having value. It does, by the fact that the more you know your clients, the better you can serve them. This “unlocked potential revenue” of all your current customers can be quantified as your whole customer’s lifetime value (CLV) added together. 

This number is known by finance people as Customer Equity, but it’s much more than a mathematical formula. The value that VCs and public markets have put on assets such as loyalty programs and subscription lists is often greater than the value of a company’s capital assets!

While it might sound like it has to do with finance, this is all highly related to marketing. This is because each tactical decision gets vetted by whether it will optimize CLV; it becomes your company’s North Star.  

Allison Hartsoe has strategize d the digital customer analytics for dozens of Fortune 500 customers throughout her career. She now leads an analytics consultancy in Portland OR, Ambition Data, and published the book, “The Age of Customer Equity”,  in 2021. She has been published in Forbes.com, MIT Technology Review, and Fast Company and somewhere in between all this writing, she found time to cycle across the USA. 

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Episode 99: Tying Revenue back to Traffic, with Steffen Hedenbrandt

Disclaimer: When I bring technology vendors on the show, you should know that they are not sponsors or affiliates. They’re simply here to give you a broader perspective.

If you have been to the eye doctor for near or far sightedness, the equipment that’s likely been used to assess you is a phoropter. The part that’s put in front of your eyes looks somewhat like a pair of glasses, but it branches out from that with an imposing array of lenses, dials and machinery. You are shown an eye chart and the doctor flicks through alternate lenses, asking you to say whether the image is clearer with lens 1 or lens 2. When tests on the phoropter & other equipment is done, you end up with lens prescriptions that are right for you. 

This process isn’t unlike what’s behind marketing’s use of attribution models. They serve to show what impact advertising channels have on a company’s revenue, with pre-set models, each one weighing the impact of digital touchpoints differently. By attributing revenue back to the channels and campaigns that helped acquire it, you get a clearer view of what you are getting for your marketing dollar. 

Of course, marketers don’t use phoropters, but doing attribution analysis does take specific tools, and that’s what this episode takes us through. 

My guest is Steffen Hedenbrandt, who’s growth-oriented, data-driven and loves all parts of scaling a business.  He worked at places like Upwork and Airtame before cofounding DreamData, where he serves as the Chief Marketing Officer.He has a bachelor’s degree from Aalborg University and a Masters from Copenhagen Business School. 

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