Four types of good luck

Play a lot of poker and you’ll come to realize there are several different forms of good luck involved in both cards and in life:

1. Something positive happens due to chance

This what we tend to think of when we consider good luck: there’s a small chance of something positive happening and it does.

Poker: You get all in preflop with nines vs someone else’s kings, hit a nine on the river, and win the pot despite being a 4-1 dog when you got all in.

Life: You win the lottery for a few million dollars.

2. You avoid bad luck

I’ve written about how if you expose yourself to small probabilities repeatedly, the odds of that thing happening rise dramatically. For example, a rock climber who estimates his odds of dying on a climb are 1 in 1,000 has a 63% chance of dying after 1,000 climbs.

Poker: You win a tournament after getting all in with AK vs Ax (like A9, etc) multiple times. You’re roughly a 3-1 favorite each time you get all in, but were lucky that your opponents never hit one of their outs and knocked you out.

Life: You’ve never been rear-ended while waiting at a traffic light.

3. A random situation favors a positive outcome for you

Poker: It’s late in the tournament and it folds around to the small blind who raises with kings. You look down and find aces in the big blind, re-raise, get it all in, and your aces hold up. You were lucky because had the small blind gotten aces and you gotten kings, you would have also wound up all in, with you likely losing. There was no skill involved in the outcome.

Life: You’re born in America and not a third world country.

4. You get to experience lucky situations at all

Poker: When you first started playing, you ran well, which encouraged you to play more, which led you to improving and playing long term.

Life: Bill Bryson says it best in a one of my favorite books, A Short History of Nearly Everything:

Not only have you been lucky enough to be attached since time immemorial to a favored evolutionary line, but you have also been extremely-make that miraculously-fortunate in your personal ancestry. Consider the fact that for 3.8 billion years, a period of time older than the Earth’s mountains and rivers and oceans, every one of your forebears on both sides has been attractive enough to find a mate, healthy enough to reproduce, and sufficiently blessed by fate and circumstances to live long enough to do so. Not one of your pertinent ancestors was squashed, devoured, drowned, starved, stranded, stuck fast, untimely wounded, or otherwise deflected from its life’s quest of delivering a tiny charge of genetic material to the right partner at the right moment in order to perpetuate the only possible sequence of hereditary combinations that could result-eventually, astoundingly, and all too briefly-in you.

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I’ve found that knowing about these different forms of luck has made it easier for me to recognize and appreciate when chance is playing a role in a situation, good or bad, and not to overstate the role of my decisions in the outcomes.

The Meta Funnel: From User Activity to Product Changes

When we think about funnels, we tend to think about how users move through our product: what percentage of people who visit our homepage sign up, what percentage of those users pay, etc.

If we zoom out, there’s another even more important funnel that we can use to measure how sophisticated an organization is with its data:

User Activity → Data → Analysis → Insights → Product Changes

User Activity → Data: When users interact with your product, are you capturing the relevant data related to their activity? For example, you might track how many people visit your homepage, but how about what percentage scroll below the fold, where they click on the page that they shouldn’t be, the bounce rate, how they’re getting to your site and how that’s changing over time, etc. What’s important to track will vary by product and not everything you track will be important, but if you’re not recording the data, it will be impossible to analyze it.

Data → Analysis: You have data, but is anyone looking at that data regularly? All of the data in the world doesn’t matter if no one ever analyzes it. For some types of analysis your analytics tools will make this step easy, but it also might require complex queries or scripts depending on what questions you’re trying to answer.

Analysis → Insights: One of the hardest things about analytics is that it’s often difficult to look at all of the numbers and draw actionable insights from them. You may discover that your conversion rate is 15%, but is that good or bad? If it goes up to 20% is that because your product has improved or just because the quality of your traffic has changed. If the top term your users are searching for on your support page is “domains”, is that an indication that you need to improve the instructions you provide users in your product, or just an inevitable result of domains being very complex?

Insights → Product Changes: And finally, once you have insights from the analysis you’ve done, are you making any changes to your product as a result? Maybe insights into your product’s support search terms indicate that you do need to improve the guidance you provide to users within your product. Does your team then execute on that by actually improving the guidance within your product?

In my experience with both many years of side projects and at work, the conversion rate across this entire funnel is typically very low. Part of it is just the nature of the beast: it’s hard to set up tracking to collect everything that’s important, it’s hard to analyze the data you do collect, it’s hard to come up with insights from that analysis, and it’s hard to make changes to your product when you do have those insights.

But just because it’s hard doesn’t mean it’s not worth optimizing. If you can double your organization’s conversion rate between any of these steps, it should double the number of improvements you wind up making to your product as a result.

One thing that can help is to discuss with your team and document your organization’s processes for each of these steps. Things like:

Who is responsible for implementing analytics on your team?
If they don’t have experience setting it up, where can they go to learn?
Where can they go to learn what data is important to collect?
How do they analyze the data?
How do you ensure people are looking at the data often enough?
Can you automate the reporting? Should you?
Who on the team needs to be involved to maximize the number of insights you’re discovering from your analysis?
What does your process look like for turning those insights into actual product changes?

There’s probably a lot of low hanging fruit here for your team to work on. The better your team gets at moving down this funnel, the more improvements you’ll make to your product leading to happier users and more impact on your company’s bottom line.

Teach everything you know

When I was a lieutenant in the Air Force I had the privilege of serving as an Executive Officer to Major (now Colonel) Heather Blackwell while she commanded the 87th Communications Squadron at McGuire Air Force Base, New Jersey.

One of my many takeaways from the experience stems from a conversation we had about her taking leave and who would take over her various responsibilities while she was away. She said “One of the measures of how effective I am as a leader is not how poorly the unit performs while I am away, but how well it performs.”

It’s counterintuitive at first because you might think that if someone who plays an important role within an organization suddenly leaves, that the organization would suffer as a result. But her point was that if things fall apart, that means she hasn’t done an effective job teaching us about what she does and how to do it. That’s not only important if she goes on vacation, but also because by teaching us she’s helping us become more effective leaders and preparing us for commands of our own one day.

I’ve been thinking about this a lot lately because I realized that in my role at Automattic, there are several things that I work on where I’m basically the only one who knows how those things work. For example, I built a system for tracking visitors who click on our ads so that we can measure our return on ad spend, but haven’t done a good job making sure other developers on the team understand how it works. Similarly, I work a lot on building email marketing lists for WordPress.com users who meet certain criteria, but never took time to document how to do it until recently.

Teaching others has so many advantages:

It ensures you’re not a bottleneck for the work that you do
If you go on vacation, change roles, or leave the organization, it ensures your team will continue operating smoothly because someone else will be able to carry out the tasks that you previously performed
It helps you learn from others because they’ll likely have feedback that will help you improve the way you do things
It helps you clarify your own thinking and processes
It will help others develop their skills and grow professionally

Also, if you’re an entrepreneur, teaching everything you know also has huge advantages which Nathan Barry has written about at length (there’s even a t-shirt!).

If you find yourself in a position where you’re the only one who knows how certain things work, find ways to involve your coworkers or hold a learn-up or just write documentation – whatever you do, don’t let yourself continue being the only one who knows how to do those things. Good things will follow.

More hours != more results

I used to play a lot of online poker and thanks to a combination of third party analytics tools like PokerTracker and ones I built myself, I was able to gain a lot of insights into my own play.

For example, the generally accepted best time of day to play online is usually late at night because that’s when people are the most tired and therefore make poor decisions. I once looked at how much money I made per hour broken down by hour of the day. Because I played a lot at night to take advantage of the poor play by others, I expected my highest hourly rate to be in the evenings and early morning. But the numbers told a different story: my hourly rate after 11pm was about break even. After countless hours of late night play, my overall profit would have been the same had I not played at all after 11pm.

Turns out that even though others were playing poorly during those hours, I was too, which negatated any advantage that I had.

The late night play also took its toll on my wellbeing during the day because I’d inevitably be tired from staying up late to play.

I bring this up because the experience taught me that working harder often isn’t the solution to a problem. It’s tempting to think that you can just put in more hours and you’ll achieve more results, but if the quality of your work suffers you might not make any meaningful progress or worse, you could undo the previous progress you made.

Next time you catch yourself trying to push through on a task even though you’re not feeling 100%, consider taking a break until you are.