Goal Formation

Posted on Posted in Business, Data Driven Culture, Data Science, Decision Neuroscience, Design Thinking, Economics, Machine Intelligence, Technology

Bart Gajderowicz delivered a great talk at Machine Intelligence Toronto about how people go through stages in accomplishing a goal [1]. The talk was about homelessness and AI approaches to public policy. I instantly saw a connection to all sorts of tensions that people endure when they set out on a goal. To distill the […]

Into the Trough: BitCoin, Machine Learning, Social Analytics

Posted on Posted in Big Data Science, Business, Data Science, Machine Learning, Technology

Into the trough of disillusionment with the hyped technologies! The canary in the coal mine for me, with respect to BitCoin, is this post. Look, nobody has enjoyed more popcorn around BitCoin than I have. From Coinye to Dogecoin, crypto-currencies have delivered the lulz. Do I believe there’s a slope of enlightenment for crypto-currency? Absolutely. Do […]

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 […]

The Creativity of Data Science for Creative Content

Posted on Posted in Business, Data Science, Design Thinking, Machine Learning, RecSys

Let’s start with a story. Daan did a traditional fast follow. He calls it Netherflix. His story was: “It’s like Netflix…for The Netherlands!”. At first, he buys rights on the cheap, pays for digital subtitling, and has a successful kickoff. He gets through to 10% household penetration, or roughly 700,000 subscribers, with an annualized gross revenue of […]

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 […]

Morphing the Lean Startup

Posted on Posted in Business, Data Science

That title, ‘morphing the lean startup’, may be technical jargon. But it is literal. And brief. I have a few thoughts to share about them both. Morphing There’s a very small sliver of research in the Marketing Science on morphing. Two papers, ‘website morphing‘, and its adtech successor, ‘morphing banner advertising‘, stand out as giants. This technology […]

Communication overhead (II)

Posted on Posted in Business, Data Science

Previously, I wrote about communication overhead in tech and the two cultures around it. Broadly, I perceive two broad camps: there are the shippers and there are the talkers. Shippers ship. Talkers talk, then ship. In this post I’ll describe three forms of written communication and how they link up with current cultural megatrends. There are […]

How to build an Experimentation and Testing Bench

Posted on Posted in Analytics Strategy, Data Science, RecSys, Technology

How do you, and those around you, deal with failure? Because if the answer is anything but “well” or “every fail has a lesson”, then progressive, iterative, experimentation and AB testing really, really isn’t for you. Go away. Stop reading. It’s not everyone. The way to build an experimentation and testing bench is entirely by […]