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[…]

Consider the statement: The strategies generated by data driven cultures are likely to produce sustainable competitive advantages. Data Driven Cultures Carl Anderson, 2015 (Data Scientist at Warby Parker) defines a data driven culture: 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. Strategy For the purposes of brevity, I’ll define a strategy as: An artifact; That enunciates choices selected from acknowledged tradeoffs; Which is rooted in a paradigm; That is actionable; With the intent of causing a sustainable competitive advantage in[…]

There’s a tension between two modes of communication – comms by storytelling and comms by bullet point. They each have pros and cons. In this post, I’ll summarize the differences. There is no verdict. The bullet point Some speak in bullet points. They’re being the golden threes be’s: Be Brief. Be Brilliant. Be Gone. Their talks might as well be written in nouveau-valley font, with Serif. Brevity is valued; where hard problems command easy heuristics, and where ‘don’t make me think’ is the l’ordre du jour. If I had more time, I would have written less. That’s especially true in the Valley. It’s true of politicians in certain settings. It’s true on Adelaide and on King. Elevator pitches are a[…]

Analytics in 2014. What a year. We hit peak Data Science hype in October. We hit peak Data Science sometime over the summer. This has a few important impacts for 2015. The end of that hype will make it harder for the majors to sell binders of plans. It’ll be tougher to find optimistic customers. It’ll be rough going for some of the weaker offers on the market to fake it long enough to make it. It’ll sort out the ‘transformational change’ shops from the technical shops far more slowly. Markets aren’t nearly as efficient as they should be. It usually takes 180 days for the peak to bite and 270 days for the money to run out. It’s really[…]