[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]
