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[…]

In general, information retrieval from analytics systems becomes harder with the degree of customization (It gets harder to find things over time). That customization is frequently an expression of the values of a culture over time. The inertia of the technical debt caused by early customization is greater than the inertia of a data driven culture. There are no silver bullets. The rest of this post unpacks that paragraph. Information retrieval from analytics systems becomes harder with the degree of customization Assume a vanilla implementation of Open Web Analytics. Or Google Analytics. Or Adobe Analytics. It’ll tell you a lot about a web system on its own. The optimization objective that is at the core of the business will typically[…]

A data driven culture isn’t necessarily devoid of creativity or imagination. Just the opposite. They’ll have to be especially patient around brand formation. Brands A brand exists in the mind of a person. It usually costs a lot of money for a brand to be impressed upon the cortex of a person. There are certain economies of scale that kick at scale, but still at a considerable cost. If that feels fuzzy, despair not. The framework below is fantastic:   Brand and CAC The optimization objective of a startup is valuation. To maximize valuation, Customer Acquisition Cost has to be minimized. As previously explored, nature doesn’t cooperate to keep CAC low. The point of the brand is to reduce CAC[…]

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[…]

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[…]

A digital product will go unloved for years. Somebody new comes into the organization and is tasked with the redesign. Two years and a lot of money, tears, and bruises later, a totally changed product is launched. People hate it. Sometimes the creative force behind the redesign, expecting an avalanche of applause and a Lion, can’t believe that people hate it. Traffic falls. And sometimes people get fired. Usually a few just go away. People are afraid to touch the site. A site goes unloved for years. There’s a binge and purge cycle. It may be the case that not too many people are even that good at managing change at all. Maybe that’s a skill that isn’t too common,[…]

In this post: Data Driven Cultures in startups should be better at prospection than other 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. Prospection There is tremendous variation in how people think about the future. There’s a lot of variation in how people think about how people think about the future. If I were to use a very strong magnet[…]

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[…]

What is a data driven culture? Data Driven Cultures Carl Anderson, 2015 (Data Scientist at Warby Parker) describes a data driven culture as on that: 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. This is a fine summary and worthy of unpacking. The Big Why Data driven cultures are likely to produce better, sustained, performance over time than the alternatives. It’s not even worth considering the alternatives to a data driven culture. How do you know if a[…]