A lot of what seems to be alarming news on the Virtual Reality front. Steam is reporting a 0.18% penetration on VR headsets. The gadget press is calling it a crashing halt. Or a nose dive. It’s interesting. Let’s reserve judgement. Google Trends suggests a summer lull in interest in VR. What happens in 2017/18 for VR really depends on what happens on the weekend of November 24 – Black Friday. If the demand from early adopters responds the way the supply side wants, we’re in for a roaring year of consolidation in the space. And we’ll be feeling the effects of that consolidation for a very long time. If you’re looking to make a long term bet on a[…]
Author: Christopher Berry
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,[…]
You’re going to hear a lot more about Artificial Intelligence (AI) more generally, and Machine Intelligence more specifically. Valuation is the core causal factor. Here’s why: We’ve gotten pretty good at training a machine on niche problems. They can be trained to a point to replace a median-skilled/low-motivated human in many industries. Sometimes they can make predictions that agree with a human’s judgement 85 to 90% of the time, and sometimes, it’s the human that’s causing the bulk of the error to disagree with the machine. We’re confident that we can train a machine to learn a very specific domain. And these days we’re in the midst of that great automation revolution. Most of the organization that build those machines can[…]
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[…]
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[…]
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[…]