Canadians are a people shaped by physical and social geography. Both explain a portion of why we are who we are, and how we relate to each other. Covid-19, an executable snippet of code wrapped in protein with the sole goal of persistence, is shaping us. It has already affected our social demography. Will it change Canada’s social geography? What It Is Population density is a pretty good indicator of attitude. I can’t make the claim that it’s always causal for all people, After all, did living with 25,000 others in a square kilometre in downtown Toronto make you more conscious of mental health challenges facing the population, or were you always conscious and chose to live with others who[…]

Is what is happening in analytics, in industry, an evolution or a revolution? What is Analytics is the science of data analysis. Those who practice analytics self-identify as analyst, digital analyst, marketing scientist, data engineer, researcher, among many others. Tukey (1962, The Future of Data Analysis, The Annals of Mathematical Statistics, (33), 1) called them all practitioners. The goal of the practitioner depends on their context. That context largely, but not always, depends on the state of knowledge, state of the culture, or sometimes, normatively, the state of maturity, of the group they belong to. Large organizations can have a large amount of difference within them. It’s not uncommon for an operations department to be extremely mature and for its[…]

I used to conjure Louis Del Grande to appear on my television. I used steel wool on the antenna of a black and white set, Tuesday’s at 7 or 8pm, on CBC. Louis played a tabloid journalist that fought crime, fought his wife, fought the Crown, cracked jokes, and in the end would solve the murder mystery with a fuzzy psychic flashback. The show was called, wait for it, Seeing Things. I thought it was neat how he could see the past so clearly, with psychic flashbacks, often at the most inconvenient time. I remember wanting to see the future like that. It was unlike anything I remember watching on television. Last night I scrolled through over a hundred titles[…]

There are at least two systems of achieving productivity growth: path dependence and disruption. What if there is a third way? This post unpacks that paragraph and explores ways through. It will start with explaining lock in and path dependence. We’ll cover the application narrow machine intelligence in a very narrow industry. It will end with a small scenario and a few what ifs. Lock In Consider banner advertising. This is a relatively old industry. Its roots predate the Internet by at least a couple hundred years. It may have started thousands of years ago. It starts out with a person with a problem. They need to get the word out about their product or service. Reframed, they need to[…]

Torben Iversen and Anne Wren wrote (1998) “Equality, Employment, and Budgetary Restraint: The Trilemma of the Service Economy” and published it in World Politics, (50), 4, pp. 507-546. And it’s a good read. And you could read it for yourself right here. Here’s a summary in one image: What It Means What causes the Trilemma itself? It’s the idea that productivity doesn’t really grow in a pure local services economy. A restaurant can only serve so many meals, barber cut so many heads, a teacher so many students, a surgeon so many people, a police officer so many arrests. It’s far harder to get compounded year on year growth in productivity in services. As I’ll argue below, it isn’t impossible.[…]

What do you think causes the demand curve? Mechanically, it’s pretty easy to describe the laws of demand. The way pretty lines shift to the right or the left from shocks. It’s possible to deduce the real, rough, shape of the demand curve for a product (It just takes a lot of courage!). We can import all the knowledge about demand, segmentation and price discrimination. We can describe a demand curve just fine. Why does it exist? What causes it to exist? If intelligence didn’t exist, demand wouldn’t exist. It’s fun to think of a machine generating it’s own preferences, independent any human input. Most of human trainers of such machines seem to keep them on a short leash. Monkeys,[…]

What causes conversion? Demand. It’s a simple answer and worthy of unpacking.  You could thank Claude C. Hopkins for the simple answer. Hopkins wrote two books towards the end of his life – Scientific Advertising and My Life In Advertising. He seemed to regret his experiences as an agency president, and left some direct advice on how master marketers should think of their choices. In his last decade of life, Hopkins marketed his marketing expertise. Instead of continuing to take on all the risk of marketing product on behalf of somebody else (and maybe getting paid if the product sold), he set up a system where products would be pitched to him. If the product was good, he’d take a[…]

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 paying to solve a problem. Why is TAM important? TAM determines the life and death of a firm. The leading cause of startup failure, and perhaps all business failure in general, is the failure to penetrate and/or retain TAM (Including bureaucratic capture and rent-seeking). In this context, I’m concerned about the introduction of a new product into the market in an effort to generate both[…]

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 concept, let’s start off with the idea that people have goals, people have emotions, and that time moves forward. As people make progress towards their goals, their emotions change over time. They start off in a good mood, in a state of uninformed optimism. Then, as negative information overwhelms their ignorance, they enter into a state of informed pessimism. So much negative information builds up[…]

“A study at Ball State University’s Center for Business and Economic Research last year found that trade accounted for just 13 percent of America’s lost factory jobs. The vast majority of the lost jobs — 88 percent — were taken by robots and other homegrown factors that reduce factories’ need for human labor.” – AP Canada’s labour force is around 19.6 million people, of which 18.2 million people are employed. Together, they worked something like 2.4 billion hours that month. In December 2016, something like 1.7 million Canadians worked about 240 million hours in manufacturing.  Roughly. Because of seasonal adjustments and different data at different times. And error. In terms of our working lives in Canada, collectively, manufacturing is about 10% of[…]