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 makes snap changes to a digital user experience. The ultimate reason why you’re not hearing more about morphing in adtech is because paid agencies can’t figure out how to scale the creative necessary to drive it. I’m convinced that morphing is the ultimate promise I bought into back in the mid-nineties – the perfect intersection of recsys and experience. It requires an extreme[…]
Category: 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 those that can write instructions that a human can reliably compile and execute (management). There are those that can write instructions that an organization can reliably compile and execute (governance/policy). There are those that can write instructions that a computer can reliably compile and execute (development). There are instructions that can be typed that cause performance in people. Such parameters include the outcome, the instruction,[…]
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 setting sail for fail. How to build your Experimentation and Testing Bench Your bench will be built: 95% with culture 5% with technology The technology is well thought out and several vendors are excellent in this space. The technology isn’t a problem. Sure, there are a few pretty shockingly bad systems out there. Every industry has a phony. Building a testing bench begins with policy.[…]
Two big announcements – HBO and CBS, two major media companies that create original content, will both offer OTT streaming services. Consumers won’t need a cable subscription to get either of them. Sports are excluded of the service. More on that below. As a Canadian, it’s even more interesting because the CRTC has been holding hearings on another consumer friendly initiative, Pick-And-Pay. It’s pleasing to see HBO and CBS work at offering audiences the entertainment they want, and how they want it. It’s the beginning of the flip from a content-centric to a consumer-centric paradigm. And that’s a lot deeper than just a set of buzzwords. It manifests itself in the activities at the media company. I was impressed with[…]
Data Civilians. Monica Rigoti used that terrific term in a New York Times Big Data piece. And the term resonated. It’s common to think of Big Data in much the same ways as nuclear research. Everybody wants the bomb. Yet, data comes out of the ground in a raw ore. The ore has to be mixed different chemicals to create various salts. Then it has to be shoved into huge centrifuges. These enormous processes are used to separate the slightly heavier bits of data from the slightly lighter ones – a process that’s important if you don’t want to contaminate the earth with dirty bias. It has to be milled into a sphere or sometimes an ingot. And then surrounded[…]
Consider a list of metrics. Now pick the Y, the dependent variable, from that list. A human would use their judgement. A machine would use an algorithm. It’s clear that human judgement varies. There’s evidence that it does. Causing a machine to make accurate predictions about human judgement is an interesting problem because of the inherent variation of judgement within human populations. This is a polite way of saying that some people really diverge from the median in their application of judgement. Consider the following question: If given a table of Gross Profit, Sales, COGS, Marketing Spend, Working Dollars, Non-Working dollars, Discount Cost, Impressions, Paid Media Impressions, Paid Media CTR, UV, V, and PV, which is the dependent variable? The[…]
This is a lot of inside baseball. The motivation is to share information while acknowledging that it’s wildly anecdotal. It’s directed at data scientists thinking about business. The Facts Andrew and I founded Authintic in late 2012. We landed three great customers. We met between 1,600 and 1,900 well wishers, competitors and prospective customers. Five major market hypotheses were tested. Revenue was earned and value was generated. Authintic was acquired by 500px in early 2014. The Feels Thrilled. Very excited. And a tad skeptical about the lessons learned. People are terrible about extracting causal factors from an experience. I’m people. So I reckon that applies to me too. A sample size of 1 isn’t authoritative. It doesn’t constitute proof, or evidence[…]
Technical debt builds up in software over time. It is the summation of all the liabilities built into the technology over time. It impedes the ease of adding new features and increases the cost of keeping the product functioning. For those that do not understand technical debt, it is enraging. Why Technical Debt is Important Assume a software product that solves a problem that a self-referential group of people (a market segment) is willing to pay for that product. Assume that the product has just enough features (m) that results in more customers (n), consumer retention (r) and market penetration (p) that all feed directly into the recovery time of an investment (I). These variables are at the core of[…]
It’s the results, genius! It’s the results. The purpose of any sort of data analytics or data science is to get results. It isn’t about the spreadsheet that comes three weeks after the campaign. It isn’t about sandbagging numbers. It isn’t the few slides in the Quarterly Business Review. It isn’t even data entertainment. It’s the results. Great! So what’s the deal? Why is so much time expended on activities that don’t directly tie to getting results? Analytics Maturity It’s because of maturity, or the sum of experiences that an organization/culture chooses to remember. Very good models of analytics maturity exist. Stephane Hamel has a great one. Stances inform tools and tools cause experiences. Where you stand affects which, if[…]
This is Big Data Week in Toronto. I’ll be delivering a case study on the business value of that data, but on a rather small, but beautifully complex, dataset on Monday. Big Data has now just become a marketing term. Those who have put in the effort, and read the three or four HBR articles on the subject, know more than 80% of the population. If you’ve read up on some of the applications involved, you’re ahead of 95%. If you read this, you’re ahead of 99.99% of the population. So, there’s an incentive to read on. What is Big Data? A good definition of Big Data is anything that is generally too big to fit in the memory of[…]