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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Episode 93: Visualizing & Making Data Valuable, with Eric Boissonneault

In its raw form, data’s not worth much. If refined and put together with other data, it can be worth a lot. Here are well-known brands that built their value by creating a useful visual experience out of user-generated data:

  • Notable Examples:
    • Glassdoor
    • Nest
    • Zapier
    • Mint
    • Robinhood
    • Flipboard
    • Ancestry
    • GoodReads

This episode’s guest will help us see what is possible once you have data in your hands. Eric Boissonneault grew up loving numbers, but it wasn’t until he saw a Hollywood movie about card players at age 16, that he knew how he would apply his skill. He taught himself poker and methodically played this ‘game of chance’ so well that He became a professional player through his years at University du Quebec à Montreal and beyond. 

After cashing his poker chips in, he wanted to show the business world how they could look at the data they have on-hand as the basis for decisions. In 2020 he founded data consulting company Systematik to help businesses untangle, collect, visualize and understand their data.

Listen in this episode for Eric’s explanation of how you can put a unique transformation or twist on the data you already have, and even make an application that monetizes the data. 

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How fast will you hit Google Sheets 5-million cell limit? If you have a spreadsheet with 5 tabs and each tab fills columns A to CW, and there is 10000 rows of data in each tab. It happens faster than you think.

Episode 84: Data Science Wizardry with Richard Fergie & Brett Serjeantson

Do you own a crystal ball to predict the future? No? Well, I have good news for you. You don’t need one. 

When you feed lots of historicals into a machine, it doesn’t have to know what those numbers mean in business terms. Thanks to sophisticated data models, machines can determine the trends behind your numbers, including a pretty accurate prediction of what the next numbers in the series will be. 

That’s not all there is to data science though. Our guests believe it’s what takes our analytical abilities up the evolutionary ladder, from what happened, to how it happened, to how it might happen.

This episode talks about how cloud computing, which  crunches these numbers, has changed data science, doing everything that high-end servers used to do, at a fraction of the cost. It also covers the skillsets of the people needed to help machines work. To learn about how you can start implementing data science in your marketing, I’m joined by two people who are well versed in the field.  

Richard Fergie lives in the UK. He showed a penchant for math, which led him to Oxford University where he took his bachelors in mathematics. After spending time living in South America, he moved home and took a liking to the quant-heavy world of pay per click marketing. He has taken the 10 years of experience he got consulting on digital marketing and web analytics projects to found Forecast Forge, a quick and accurate forecasting tool that runs on Machine Learning.

Brett was an early SaaS pioneer and helped change the PR industry by creating one of the first companies to develop a real-time media monitoring and analysis platform that was able to successfully generate business intelligence from both traditional and social media.

Brett would lead the company as both the CEO and chief architect and developer of the platform as well as being awarded 2 patents.

Brett led his company through several significant transitions, including recognizing the need for big data and machine learning capabilities very early on as well as recognizing the importance of social media. 

Brett also identified the need for both journalist and publication data from the aspect of creating new markets as well as leveraging the data to further improve the analytics.

Brett’s company, MediaMiser, would be acquired in 2014.

Brett would later go on to improve his own analytical capabilities by achieving certifications in both data science and AI from the University of Waterloo and the University of Toronto.

Brett also has a bachelor’s degree from Western University and a diploma in PR and communications from Algonquin College.

Let’s go talk to Richard Fergie & Brett Serjeantson.

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What you need to doTools that can do it
Visualize large datasetsGoogle Colab/JupyterMatplotlib 
Find trends, use predictions to decide next actionPomegranate
Do regression analysisPandas (built on NumPy)
unclassifiedProphetPyTorchTensorFlow

Episode Reboot. 

Check out https://www.forecastforge.com/learning/

Episode 83: Quantifying Your Marketing Funnel’s Revenue with Keith Perhac

Disclaimer: The company featured here is not a sponsor of the show, nor have I affiliated with them. They simply bring a perspective that I think you’ll get some use from.

“It’s not working.” That’s the gist of every complaint made about marketing funnels. Marketers painstakingly build a series of offers and pay for traffic to see them, but the conversion rates drop off somewhere between there and the point where sales close.

Can funnels be fixed? Absolutely, but not without knowing a critical piece of data. Getting that data that helps fix the suboptimal parts of the funnel is our focus today. 

To go through this I’m joined by Keith Perhac, a digital marketing expert and software entrepreneur. After growing up in the states, he headed to Japan to become what’s known there as a salaryman. He moved back In 2010 to work with startups and digital marketers looking to grow quickly. He founded SegMetrics, a tool that lets you see revenue from the perspective of each touchpoint in your marketing funnel. Since then, he’s appeared on over 35 podcasts & in 2020 published the book we’re here to discuss, “Building Marketing Funnels that Convert, a 90 minute guide”

When he’s not working on SegMetrics, Keith draws and attempts (futilely) to spend more time outdoors. He lives in Portland, Oregon with his wife and two daughters.

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Episode 77: Stop arguing over leads; start scoring them, with Gary Amaral

Disclaimer: The company featured here is not a sponsor of the show, nor have I affiliated with them. They simply bring a perspective that I think you’ll get some use from.

Two things are required to get a clear view of revenue growth. First, sales and marketing must come together to jointly-define the thresholds at each stage of a lead’s lifecycle. Second, they must apply points to a lead’s every action, either manually or by layering automation on this process. 

My guest believes that lead scoring systems not only bring pipeline visibility, they improve the collaboration between Sales & Marketing. In fact, he claims that by pooling their information on leads and letting AI find the patterns, they can tell when a lead is ready to buy, upsell, or churn. 

Gary Amaral held several positions at places like at BlackBerry & Hootsuite, always at the intersection of marketing and sales. Seeing how poor scoring led to frustration for all involved, he joined forces with two other serial startup entrepreneurs. 

In 2020 they co-founded Breadcrumbs, which is a revenue acceleration platform based on a co-dynamic lead scoring and routing engine. Listen for Gary’s advice on what you need to do to get scoring right. Just as good communication helps keep couples together, the Sales & marketing relationship needs good communication on the status of leads. Lead scoring could very well be the glue in this marriage. 

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You can also check out these episodes involving lead scoring:

Episode Reboot.

Download Breadcrumb.io’s lead scoring template

Episode 61: Tools for wrangling marketing data, with JD Prater

Disclaimer: The company featured here is not a sponsor of the show, nor have I affiliated with them. They simply bring a perspective that I think you’ll get some use from.

What needs to be done with marketing data to make it usable?

Essentially, it must be taken from its original source, formatted cleanly, and put into your database to be analyzed. This is handled by a process called ETL, Extract, Transform & Load. This process was done manually in olden days, but AI is now facilitating this task to be almost entirely done by technology. 

Our guest can help us get familiar with how this works because he approaches it more from a marketer’s perspective than a technical one. JD Prater has a background in the world of paid media marketing, probably the niche that’s most famous for doing detailed analysis on large amounts of data. He has recently become Marketing Lead at Osmos, the maker of a tool that uses AI to help companies with ETL work. Besides that, JD has done marketing in-house at Amazon and Quora, and worked on brands while with a PPC agency. Besides that, he’s well known for speaking on digital marketing and being involved with several podcasts, and when he’s not on dad duty, you’ll catch him somewhere in his home state of Oklahoma, out cycling on an open stretch of road.

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Episode Reboot

Go take a product demo of some tool you might use.

Episode 57: How AI Levels the Marketing Playing Field

If every part of your customer acquisition can be measured, you’ll figure out how to do it profitably. That premise has driven why digital marketing, and especially Pay-per-click (PPC) is managed by experienced humans. These professionals scour through data for the relationship between a company’s ads and the buyers actions; once found, budgets get shifted to achieve that optimal effect.  

A wrench has been thrown into our acquisition dreams by the ad platform titans: Google, Facebook and Microsoft (who own LinkedIn). Thanks to major AI investments they have made in the last five years, they’ve been able to automate much of the work that marketing professionals have done. In tandem with implementing their ‘smart’ software that runs autonomously, they have been restricting a marketer’s ability to manually control campaigns. 

The platforms believe their AI is smart enough to run marketing, so we can either be passive, letting them spend our money as they see fit, or we can choose to give them navigational assistance while they drive. The point is, you should have a game plan that works with the platforms’ AI. One that, over time, will generate the leads you need at the best possible acquisition cost.

I believe listening to this episode will give you that plan. It covers:

  • How marketing has become more computationally complex than humans can handle
  • What was in it for the platforms to automate PPC marketing
  • Stages of maturity for dealing with data, ending with predictive analytics
  • Why you shouldn’t fight ad platform automation, but instead use your business data to train algorithms how to market you more effectively
  • How you should integrate your in-house systems and apply data science to uncover insights

People/Products/Concepts Mentioned in Show

Paper estimating how much data optimized advertising requires, authored by Randall A. Lewis of Google; Justin M. Rao of Microsoft: “A calibrated statistical argument shows that the required sample size for an experiment to generate informative confidence intervals is typically in excess of ten million person-weeks”

Quote by Chuck Heamann & Ken Burbary in “Digital Marketing Analytics”:  “If you think about all the tools we have talked about…you see that there is one common denominator: You do not own any of the data. Herein lies what we think is the biggest revolution coming to digital analytics..companies will be building internal repositories for this data.”

Episode Reboot 

Go talk to a coworker who uses statistical measurement, to understand how the efficiency it achieves in other fields can be applied to marketing.

Episode 53: Digital Marketing in an AI World with Fred Vallaeys – Summer Books

Disclaimer: The company featured here is not a sponsor of the show, nor have I affiliated with them. They simply bring a perspective that I think you’ll get some use from.

Chess Grandmaster Garry Kasparov was famously beaten in 1997 by a supercomputer built by dozens of IBM technologists. A Slate article looking at how Deep Blue changed chess said “The change here wasn’t just that a computer could win, but that a computer could help human players win if incorporated into their training regimes effectively.”

The same thing is happening with PPC Platforms. Since 2011, Google has been integrating AI into many of their products, and every campaign feature Google Ads rolls out seems to take away control from us humans and give it to their machines. So if we’re going to follow Kasparov’ lead and get better at this game with the AI, the question becomes, what’s the process for training an ad platform’s AI, when it’s writing programming that only it knows, and even the technologists running it don’t know?

Some answers are contained in the book Digital Marketing in an AI World. Fred Vallaeys was one of the first 500 employees at Google where he spent 10 years building AdWords and teaching advertisers how to get the most out of it as Google’s AdWords Evangelist. Today he serves as Co-Founding CEO of Optmyzr, a PPC management software system. Fred is a fixture on the marketing conference circuit and blazed new trails with online industry learning through Optmyz’s PPC Town Halls. 

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Episode Reboot: 

Remember, computers have a different kind of smarts than us.