[MUSIC]
All of us have way too much complexity in our lives, too many devices,
our kids going a million places.
At work we have all kinds of things we're supposed to be doing.
And realizing, is there a way to be simple?
And noticing that some of, I think, the most effective people actually are simple.
And so that kind of led us to start exploring
what's the value of simple rules and simplicity?
We started studying companies and
their product development processes, and we realized that
there were companies that had these very complicated product development processes.
And what would happen was they would do the wrong product really efficiently.
And conversely we would see these other companies where there were no rules at
all, and they were having a great time getting nothing done.
And we saw that then again, there was this intermediate.
A few simple rules, who's in charge of what, what kind of a product do we do.
But just maybe four or five rules that constrained what people did but
gave them some flexibility to innovate.
[MUSIC]
There's really three steps to coming up with simple rules.
One of them is what's the objective.
What are we really trying to achieve here?
Is it revenue, is it growth?
Is it notoriety?
So what's the underlying business objective we're trying to achieve?
And so profitability and growth, for example, would be two different things.
So what are we trying to achieve and
that's usually fairly straightforward with business people to come up with.
What's harder to figure out is what's the bottleneck process, what's the repeated
thing that we do often that really keeps us from achieving that objective?
So an interesting example is actually Google, back in the day.
There were some problems at Google early on, where they weren't getting enough
product improvement in their search engine, and some of their other products.
So their product development wasn't as good as they wanted it to be.
So they thought a bit about, do we reorganize product development?
What do we do?
Well then as they thought about it more, the real bottleneck that was keeping them
from being successful was the quality of their computer scientists.
And so they realized the real bottleneck was the hiring process.
And what we need to do is get top-flight computer scientists because
top-flight computer scientists are substantially
better than the average computer scientist.
And then they developed some simple rules that were things you might not expect.
Things like look for people who are eccentric because they're more creative.
So, people who ride a unicycle, climb the Himalayas, do something strange.
And look for people, always prefer referrals from other Googlers.
Because the idea there was Googlers know what good Googlers look like,
and they want to work with them.
And then the third one was stop the hiring process if you see anything phony or
fake on the resume because we want high integrity people.
So the point here is that the objective was not
what they first thought it was going to be.
It's actually the bottleneck was not what they first thought it was going to be,
it ultimately became something around computer science hires.
And then finally developing the rules.
That's the third step is what are the rules?
And that's usually a combination of looking back at your own data.
So for example, I worked with a company in biotech where partnerships
were very important, and they looked back at their data trying to figure out when
were we successful when weren't we.
So trying to understand from your own data.
Another strategy is bringing outside experts.
You come up with rules by thinking about what your objective is, trying to figure
out what that the bottleneck is, which is probably the hardest thing to do.
And then developing rules partly your experience,
partly working with outsiders to come up with them.
[MUSIC]
You can make faster decisions when you're simple because you only have to think of
a few factors.
The second reason is that there are certain situations that
simplicity actually is not only faster but
it's actually better than using complicated formulas and lots of data.
Because if you use too much, if you use a lot of data on a formula,
what you tend to do is overfit the past, which is a poor predictor future.
So it's actually, because what you're trying to do is predict the future.
And given let's say you have a set of data and
there are multiple ways you can fit that data.
The simplest fitting of that data is the most likely to be predictive
of the future.
And then it's better because people actually do it.
So, for example, if I give you three rules to remember for partnerships.
Or three rules remember for investing or dieting.
You are more likely to remember and do it, even if you're stressed out,
you're busy, you're got a zillion things to do, you can say, wait a minute.
I'm supposed to think about hiring people and I remember these three rules.
And that's all I have to remember.
So it's particularly effective when you're just stressed out with too much to do.
[MUSIC]
It works best when you involve the people in your organization.
So not just you at the top deciding what the rules are, but
getting the talent below you thinking about it and participating in the process
of coming up with the rules, looking at the data, testing them out.
[MUSIC]
The stopping rules are the hardest to learn.
When do I back off?
We're trying to sell to a client and when is it time to say no?
We're developing a product, when is it time to stop?
We have an investment, when do we sell it?
Those stopping rules are the hardest for people to learn.
And in I think maybe ironically, they're also related to higher performance.
So, probably one
of the biggest mistake business people make is they stay in something too long.
And a stopping rule helps you get out of that.
People are really good at starting and are really bad at stopping.
And so a rule that says I'm done with this partner, I'm done with the sales call.
I'm done with this product, I'm done with this person, I'm not hiring them.
Whatever the process is that you have rule that says I stop.
[MUSIC]
When it's time to change is when they don't seem to be working anymore, or
the situation has somehow changed.
One of the examples, I think is pretty obvious and
people would know well is the Moneyball.
The Oakland A's Moneyball, which was focused on,
among other things, getting players with high on-base percentage.
Well once everybody else in the major leagues figures that out,
they're doing it too.
They're paying more than you are, and the Boston Red Sox are winning the pennant,
and you're not.
And so, when your rules are starting to be copied, by competitors.
When, or when the situations change, as it did for the A's,
you have to change up the rules.
[MUSIC]