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[…]

The main point is that it’s worth trying to predict technology triggers and asking what those triggers mean. There is value in answering the question so what? A secondary argument is that questions beginning with what if? can be very interesting, but far less reliable than so what? What is a Technology Trigger? The term Technology Trigger is from Gartner’s Hype Cycle. They defined it as: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven. Gartner Research The term has been deprecated in favour of the term innovation trigger. However, as an owner of the hardcover book Managing The Hype Cycle (2008), I[…]

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[…]

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 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[…]

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[…]

Why does it seem like all the unimportant, easy stuff gets done first? Look up The Urgency Bias. Employing simplified games and real-life consequential choices, we provide evidence for “urgency bias”, showing that people prefer working on urgent (vs. important) tasks that have shorter (vs. longer) completion window however involving smaller (vs. bigger) outcomes, even when task difficulty, goal gradient, outcome scarcity and task interdependence are held constant.- Zhu, Yeng, Hsee (2014) Even when task difficulty, goal gradient, outcome scarcity AND task interdependence is held constant, urgency wins. Even when it would be more beneficial to do something important instead of something urgent, even when you’re painfully made aware of those incentives, you still gravitate towards doing the urgent. There’s[…]

Assume that you’re a founder of a tech startup. Assume that you’ve achieved product-market-solution fit. You’ve nailed it. Time to scale. Many founders are great at sales. But not all founders are great at marketing. And that’s a bit of a problem because of three letters: CAC. The Customer Acquisition Cost CAC is the ratio between dollars spent on marketing, and new customers acquired. And it is related to valuation in a very important way. Let me explain. Take a look at the chart below. This is an output from a standard model of SaaS market penetration. Market size is 333,333 customers, the product will approach saturation at 51% of that target, with a monthly churn rate of 0.20% held[…]

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[…]