The Marketing Science of Data Science of Marketing Science

Posted on Posted in Artificial Intelligence, Business, Machine Learning, Marketing Science

Earlier in the month, I dined under the space shuttle Endeavour with some of the best minds in marketing science. One mind remarked: “That’s why I bring a glossary with me, oh, you want to do supervised learning? Oh you mean regression? Oh, okay, now we can talk… We’ve been talking to managers about these […]

What if Total Addressable Market can’t be estimated accurately?

Posted on Posted in Data Science, Economics, Machine Intelligence, Marketing Science

What if Total Addressable Market can’t be estimated accurately? What then? What is Total Addressable Market (TAM)? Total Addressable Market, or TAM, is the number of buyers who are Willing To Pay (WTP) for a solution to a problem they have now, or are Willing To Pay (WTP) your firm instead of the firm they’re currently […]

Predicting technical change in three variables

Posted on Posted in Data Driven Culture, Machine Intelligence, Machine Learning, Marketing Science, Predictive Analytics, Technology

A great mind in public policy told me, just this last September, that people are really bad at judging the rate of technological change and when it’ll affect them. It’s like standing on a railway. You can see the train out there. Some people assume that the train is going to hit them very soon. […]

Why the unimportant, urgent stuff gets done first

Posted on Posted in Analytics Strategy, Data Driven Culture, Marketing Science, Technology

Why does it seem like all the unimportant, easy stuff gets done first? Look up The Urgency Bias. Employing simplified games and real-life consequential choices, we provide evidence for “urgency bias”, showing that people prefer working on urgent (vs. important) tasks that have shorter (vs. longer) completion window however involving smaller (vs. bigger) outcomes, even […]

How Data Driven Cultures Tackle CAC

Posted on Posted in Analytics Strategy, Business, Data Driven Culture, Marketing Science, SaaS

Assume that you’re a founder of a tech startup. Assume that you’ve achieved product-market-solution fit. You’ve nailed it. Time to scale. Many founders are great at sales. But not all founders are great at marketing. And that’s a bit of a problem because of three letters: CAC. The Customer Acquisition Cost CAC is the ratio […]

Why did it come to this: adblocking and the old deal

Posted on Posted in Data Science, Marketing Science, Technology

Some reports have adblocking penetration at anywhere between 10% and 40%. Some publishers are blocking content from the adblockers. Others are making the ads unskippable with ad block. Broken systems are interesting, aren’t they? The system of advertising is broken. Here’s the best that I can explain it from as many perspectives as I can […]

How to think about Content Scoring and Audience Scoring

Posted on Posted in Data Science, Machine Learning, Marketing Science, Predictive Analytics

A score serves as an ultimate abstraction or summary. That’s especially true in sport. “Who won?” “The Blue Jays. 11 to 5.” The Blue Jays won because they moved men more often across one specific plate more often than the other team. This is all very American. A brief period of action. Collect statistics about […]

The Quantitative, The Qualitative, The Total Consumer Experience

Posted on Posted in Marketing Science, Strategic Analytics, Technology

A tier one MSI topic focuses on how should quantitative methods and qualitative methods be combined to understand the total consumer experience. It’s an excellent topic. The two worlds aren’t natural complements. They have radically different systems of activities, tools, and methods, which in turn affects their own experiences, and how they see the world. However, if […]

Who’s Downvoting You On Reddit?

Posted on Posted in Analytics, Marketing Science, Social Analytics, Social Media Analytics, Social Media Measurement

So who keeps on downvoting you on Reddit? We’ll find out. But first – three notes: You may be familiar with Reddit. If you’re not – you can read this explanation about what Reddit is. To answer that question, I downloaded a dataset that was built in early 2011 or very late 2010. The dataset […]