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Peter Drucker famously observed, “If you can’t measure it, you can’t manage it.”
The Minimum Viable Product is the smallest thing you can build that will create the value you’ve promised to your market. But nowhere in that definition does it say how much of that offering has to be real. If you’re considering building a ride-sharing service, for example, you can try to connect drivers and passengers the old-fashioned way: by hand. This is a concierge approach. It recognizes that sometimes, building a product — even a minimal one — isn’t worth the investment. The risk you’re investigating is, “Will people accept rides from others?” It’s emphatically not, “Can I build
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Concierge MVP for happaround, kaavadia makes a lot of sense and need to figure that out very quickly.
It will a cheap, quick test but it doesn't all have to be completely real. Only the core essential parts of it have to be real.
Now that it’s cheap, even free, to launch a startup, the really scarce resource is attention. A concierge approach in which you run things behind the scenes for the first few customers lets you check whether the need is real; it also helps you understand which things people really use and refine your process before writing a line of code or hiring a single employee.
Sometimes, growth comes from an aspect of your business you don’t expect. When you think you’ve found a worthwhile idea, decide how to test it quickly, with minimal investment. Define what success looks like beforehand, and know what you’re going to do if your hunch is right.
“Without productivity objectives, a business does not have direction. Without productivity measurements, it does not have control.”
In a startup, the purpose of analytics is to find your way to the right product and market before the money runs out.
Of course, customer satisfaction or pipeline flow is vital to a successful business. But if you want to change behavior, your metric must be tied to the behavioral change you want. If you measure something and it’s not attached to a goal, in turn changing your behavior, you’re wasting your time. Worse, you may be lying to yourself and fooling yourself into believing that everything is OK. That’s no way to succeed.
If quantitative data answers “what” and “how much,” qualitative data answers “why.” Quantitative data abhors emotion; qualitative data marinates in it.
Whenever you look at a metric, ask yourself, “What will I do differently based on this information?” If you can’t answer that question, you probably shouldn’t worry about the metric too much. And if you don’t know which metrics would change your organization’s behavior, you aren’t being data-driven. You’re floundering in data quicksand.
Analytics has a role to play in all four of Rumsfeld’s quadrants: It can check our facts and assumptions — such as open rates or conversion rates — to be sure we’re not kidding ourselves, and check that our business plans are accurate. It can test our intuitions, turning hypotheses into evidence. It can provide the data for our spreadsheets, waterfall charts, and board meetings. It can help us find the nugget of opportunity on which to build a business.
There’s a “critical mass” of engagement necessary for any community to take off. Mild success may not give you escape velocity.
This is a very important note for community!
Virality requires focus.
Finding a correlation between two metrics is a good thing. Correlations can help you predict what will happen. But finding the cause of something means you can change it. Usually, causations aren’t simple one-to-one relationships. Many factors conspire to cause something.
There’s no substitute for engaging with customers and users directly. All the numbers in the world can’t explain why something is happening. Pick up the phone right now and call a customer, even one who’s disengaged. Second, make early assumptions and set targets for what you think success looks like, but don’t experiment yourself into oblivion. Lower the bar if necessary, but not for the sake of getting over it: that’s just cheating. Use qualitative data to understand what value you’re creating and adjust only if the new line in the sand reflects how customers (in specific segments) are using
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Showing half of the visitors a blue link and half of them a green link in order to see which group is more likely to click that link is a cross-sectional study.
Bud Caddell has three clear criteria for deciding what to spend your time on: something that you’re good at, that you want to do, and that you can make money doing.
If you’re going to survive as a founder, you have to find the intersection of demand (for your product), ability (for you to make it), and desire (for you to care about it).
Don’t start a business you’re going to hate. Life is too short, and your weariness will show.
Never start a company on a level playing field — that’s where everyone else is standing.
figure out whether you like doing this thing. Startups will consume your life, and they’ll be a constant source of aggravation. Your business will compete with your friends, your partner, your children, and your hobbies. You need to believe in what you’re doing so that you’ll keep at it and ride through the good times and the bad.
For an intrapreneur, this question needs to be answered simply to get approval for the project, but remember that you’re fighting the opportunity cost — whatever the organization could be doing instead, or the profitability of the existing business. If what you’re doing isn’t likely to have a material impact on the bottom line, maybe you should look elsewhere.
Humans do inspiration; machines do validation.
In mathematics, a local maximum is the largest value of a function within a given neighborhood.[15] That doesn’t mean it’s the largest possible value, just the largest one in a particular range. As an analogy, consider a lake on a mountainside. The water isn’t at its lowest possible level — that would be sea level — but it’s at the lowest possible level in the area surrounding the lake.
Optimization is all about finding the lowest or highest values of a particular function. A machine can find the optimal settings for something, but only within the constraints and problem space of which it’s aware, in much the same way that the water in a mountainside lake can’t find the ...
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Math is good at optimizing a known system; humans are good at finding a new one. Put another way, change favors local maxima; innovation favors global disruption.
This is very important to note.
Math will optimise withing the constriants, won't think outside of it.
Humans can find a new system to work with.
-- If someone asks me what is it that you can do?
The answer is simple - I am not going to optimise, machines can do that, I can find and build new systems and opportunities that a machine can't.
In his book River Out Of Eden (Basic Books), Richard Dawkins uses the analogy of a flowing river to describe evolution. Evolution, he explains, can create the eye. In fact, it can create dozens of versions of it, for wasps, octopods, humans, eagles, and whales. What it can’t do well is go backward: once you have an eye that’s useful, slight mutations don’t usually yield improvements. A human won’t evolve an eagle’s eye, because the intermediate steps all result in bad eyesight. Machine-only optimization suffers from similar limitations as evolution. If you’re optimizing for local maxima, you
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This is a very strong analogy.
Ultimately, quantitative data is great for testing hypotheses, but it’s lousy for generating new ones unless combined with human introspection.
Need a healthy mix of quanitative and qualitative data to make the best decisions.
Plenty of founders use Lean Startup as an excuse to start a company without a vision. “It’s so easy to start a company these days.” They reason, “the barriers are so low that everyone can do it, right?” Yet having a big vision is important: starting a company without one makes you susceptible to outside influences, be they from customers, investors, competition, press, or anything else. Without a big vision, you’ll lack purpose, and over time you’ll find yourself wandering aimlessly.
This is why it is important to take a step back from the trend wave and see if you will actually stick to this for a long time.
But getting paid, on its own, isn’t an engine of growth. It’s just a way to put money in the bank. Revenue helps growth only when you funnel some of the money generated from revenue back into acquisition. Then you have a machine that you can tune to grow the business over time.
The question this poses a of course, is how do you know if you’ve achieved product/market fit? Sean devised a simple survey that you can send customers (available at survey.io) to determine if you’re ready for accelerated growth. The most important question in the survey is “How would you feel if you could no longer use this product or service?” In Sean’s experience, if 40% of people (or more) say they’d be very disappointed to lose the service, you’ve found a fit, and now it’s time to scale.
Must try this.
We call this the Long Funnel. It’s a way of understanding how you first come to someone’s attention, and the journey she takes from that initial awareness through to a goal you want her to complete (such as making a purchase, creating content, or sharing a message).
Founders are magpies, chasing the shiniest new thing they see. They often use the pivot as an enabler for chronic ADD, rather than as a way to iterate through ideas in a methodical fashion.
At Year One Labs, one of the litmus tests for us as advisors and investors was the clarity with which a team understood, and tracked, their OMTM. If it was on the tip of their tongues, and aligned with their current stage, that was a good thing. If they didn’t know what it was, if it was the wrong metric for their stage, if they had several metrics, or if they didn’t know what the current value was, we knew something was wrong.
The ratio works because it’s: Simple: It’s a single number. Immediate: You can generate it every night. Actionable: You can change staffing, or encourage upselling, the very next day, whereas ingredient costs, menus, or leasing take longer to modify. Comparable: You can track it over time, and compare it to other restaurants in your category. Fundamental: It reflects two basic facets of the restaurant business model.
One thing we’ve noticed about almost all successful founders we’ve met is their ability to work at both a very detailed, and a very abstracted, level within their business. They can worry about the layout of a page or the wording of an email subject one day, and consider the impact of one-time versus monthly recurring sales the next. That’s partly because they’re not only trying to run a business, they’re also trying to discover the best business model.
How do I get to a place where I can confidently do this?
As a startup, you have a wide range of payment and incentive models from which to choose: freemium, free trial, pay up-front, discount, ad-funded, and so on. Your choice needs to match the kind of segmentation you’re doing, the time it takes for a user to become a paying customer, how easy it is to use your service, and how costly an additional drive-by user is to the business.
The team at Startup Compass, a startup dedicated to helping companies make better business decisions with data, identifies 12 revenue models: advertising, consulting, data, lead generation, licensing fee, listing fee, ownership/hardware, rental, sponsorship, subscription, transaction fee, and virtual goods.
Unpaid users “churn” by cancelling their accounts or simply not coming back; paid users churn by cancelling their accounts, stopping their payments, or reverting to an unpaid version. We recommend defining an inactive user as someone who hasn’t logged in within 90 days (or less). At that point, they’ve churned out; in an always-connected world, 90 days is an eternity.
In a startup, your business model — and proof that your assumptions are reasonably accurate — is far more important than your business plan. Business plans are for bankers; business models are for founders.
Keep coming back to this.
Discovery is the muse that launches startups.
