Backcasting is a fantastic technique. It was invented in Canada. You’re welcome to use it. If it sounds like forecasting – well – that’s because it’s kind of like forecasting. With an important difference. That wikipedia page says: Whereas forecasting is predicting the future (unknown) values of the dependent variables based on known values of the independent variable, backcasting can be considered the prediction of the unknown values of the independent variables that might have existed to explain the known values of the dependent variable. I had to re-read it a few times to really get it. Once you get it, it’s just elegant. What’s beautiful is that it can silence the reactive-pure-statistician brain long enough for the prospective centre of the creative brain to imagine several futures. What I like about backcasting[…]
Author: Christopher Berry
I was 28 and sleepless when I encountered a marketing version of the logistic function. It was beautiful. It’s one of those things you’re taught about in one context, and when you’re shown it from another angle, it expands your mind. It was like discovering Pi for the first time. I could use it to check the assumptions of a market penetration forecast, and substitute my own estimates for others. I felt empowered and delirious from being able to produce a solid forecast. It became a tool as useful as btau or the crosstab. There’s a part of that math, a variable called saturation, that worried me from the outset. Saturation is the maximum percentage of adoption that a market[…]
Jon Evans wrote a piece for Techcrunch entitled: After the end of the startup era. In it, Evans writes: We live in a new world now, and it favors the big, not the small. The pendulum has already begun to swing back. Big businesses and executives, rather than startups and entrepreneurs, will own the next decade; today’s graduates are much more likely to work for Mark Zuckerberg than follow in his footsteps. And, Because we’ve all lived through back-to-back massive worldwide hardware revolutions — the growth of the Internet, and the adoption of smartphones — we erroneously assume another one is around the corner, and once again, a few kids in a garage can write a little software to take[…]
It was a treat to see these three – Yoshua Bengio, Yann Lecun, and Geoffrey Hinton – for an afternoon. Easily the best three consecutive hours I’ve ever seen at a conference. They remarked that Canada continues to invest in primary research. And this is a strength. Much of the exploratory work these three executed in the 80’s, 90’s and naughties was foundational to industrial applications which came after. Much of reinforcement and deep learning has moved on into industrial application. For the three grandfathers of deep learning, all of these algorithms and methods move into the realm of solved problems. For those of us in industry, there remains a lot of work to realize the benefits of deep learning.[…]
Some people want just one number. Some people want all the numbers. For best results, seek balance. One Number It is very possible to summarize the performance of a business or an organization in a single number. There are two main ways to do so. One is selection. One is indexing. In selection, you pick the most important metric, and you focus on it. It requires discipline and comes at the cost of myopia. In indexing, you pick the most salient metrics and you combine them into a single number. It requires no discipline and comes at the cost of boiling the ocean to the point that all the rocks bleed their salts into the atmosphere. When it comes to[…]
Hinton is quoted as saying, with respect to back propagation, “I don’t think it’s how the brain works”. You can read the full article here. Back Propagation To oversimplify, in Back Propagation, the influence of each neuron is rewarded based on how well it predicts something. Accurate predictions are rewarded with more influence. Bad predictions are punished with less. This is how the machine learns. And there’s a lot of optimism about Back Propagation. It’s really useful and generates fairly predictable machines. As data scientists, we like this. And as data scientists, we should also like what Hinton is hinting at. Kuhn It’s much more likely than not that we’re approaching a local maxima on this thread of research. I’m[…]
An orthodox Software as a Service (SaaS) business is, in part, math that an organization tries its best to manage. Think about all the math that goes into the construction of a typical SaaS firm. At the core there’s some customer with a job: a goal against which the customer wants to make progress. They can have a mathematical representation in a database somewhere. A bunch of technologists write some code, which is all math, and a bunch of creatives take a few photographs, which expresses itself a mathematical representation, and some data is Created Read Updated and Destroyed in a database somewhere, which is all just more math. And it’s all abstracted by yet more math at the processor[…]
The other I likened the process for taking apart a Job To Be Done to taking a part a lobster. There’s a very effective way to decompose any problem with enough energy. And then I watched The Founder on Netflix and admired the McDonald brothers using a classic technique in management science to refine a system on a tennis court. And I loved it. They really refined hamburger and frenched fry delivery. And then this morning I read that Andrew Ng in working on a new coursera course for AI. And I’m thankful for his initiative and optimism. Out of those three threads, this one post. The Assembly Line The assembly line was an American invention for Americans. It could[…]
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 methods for decades, but it’s just suddenly sexy because it’s all machine learning and deep learning and reinforcement learning.” A lot of the math that underlies much of machine intelligence and artificial intelligence is indeed remarketed marketing science. And, hipsterism aside, the annoyance is understandable. Marketing science started out a bit of a revolt against the Mad Men. Some of the early stories feature post-war[…]
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[…]