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[…]

The data is imperfect. Judgement is imperfect. Decisions are imperfect. The question isn’t about perfection. It’s about progression. What becomes true if we were to focus on progression? Credit goes to Matt Gershoff for inspiring this post. A remark he made at a recent #miToronto grabbed me. To paraphrase, he said that when you stop obsessing over which model is right or wrong, because all models are wrong by definition, and start focusing on just making it better, you get a lot further. He used the term liberating. And it is. The Data Reality is flawed, and as a direct result, it generates data that is flawed. The machine housed in your skull is a serious piece of technology, but[…]

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[…]

Previously, I argued that you should look at the Q4-2016 VR sales figures closely and then make decisions about whether to jump in. Some figures are in. SuperData Research, a technology research firm, estimated that Oculus had sold 360,000 headsets and HTC 450,000 since their products went on sale in March and June, respectively. Both of those headsets require high-end PCs with powerful processors. The firm estimated that Sony, which began selling a virtual reality headset in October, has sold about 750,000. — NYtimes Jan 8/2017 Those aren’t encouraging install bases. Obligatory Gartner Hype Cycle image: Consolidation is a long ways off. Facebook, has deep pockets and can sustain a long chasm crossing. The legal issues with Zenimax are a distraction. This is[…]

Some work is very clearly product work. It’s work on things where the success and failure is dependent on the users of the thing. Your users pay you. Their satisfaction matters above all else. Optimizing for the satisfaction of end users is a distinct activity. Hypotheses have to be assessed and then tested – because it’s very likely that you’re going to be wrong. There’s technology that has to be set up such that it’s reliable and robust for the intermediate to long run. It’s designed to be effective and persistent, with all of the instrumentation that goes along with that. That might include manual A/B testing, user-focused analytics, and extra special attention on the optimization objective. Clear product work is[…]

Consider the chart below: There are two series – the total number of cumulative customers (top curve) and the number of new customers added each month (bottom curve). The top curve is shaped like an ‘s’ and the bottom one is shaped like a bell. Each month that goes by, the rate of new customer acquisitions increases up to a point, and then declines. You can see the impact that the bottom curve has on the top, because adding up all the incremental customers yields a cumulative penetration curve. Pop-literature (Moore, Crossing The Chasm) focused on the bell shape of the new customers added curve. Strictly speaking, it’s not a distribution, but the shape causes a degree of comfort with[…]

In this post: Data Driven Cultures in startups should discover product-solution-market fit more reliably than Ego Driven Cultures Data Driven Cultures Carl Anderson, 2015 (Data Scientist at Warby Parker) defines a data driven culture as: Is continuously testing; Has a continuous improvement mindset; Is involved in predictive modeling and model improvement; Chooses among actions using a suite of weighted variables; Has a culture where decision makers take notice of key findings, trust them, and act upon them; Uses data to help inform and influence strategy. Startups A startup is defined as an experiment looking for a problem-solution-market fit. The goal of a startup is to become a business. To do that, it must discover a market, a subset of people[…]