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A Millionaire_s Guide to Effective Decision-Making · The Empire of Wealth

• 0:00
  Speaker 1: Successful people are incredibly successful because they know how to
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  deal with the things that they don't know just as
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  well as the things that they do know. They all
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  have this framework that has made them successful. For those
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  that are new here, I'm Davey Fogarty. My Shopify stores
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  have done over $ 400 million in sales. I wish I
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  had this framework when I first started. Let's get into it.
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  The first step, defining the decision. You want to be
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  very specific. So if I'm going to say, should I
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  expand my business to the United Kingdom? That is not
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  a great decision. That is not specific. A better one is,
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  is expanding to the UK the best use of my
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  capital and time.
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  Speaker 2: At this stage?
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  Speaker 1: To produce free flowing cash because that might be my goal.
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  Now there's actually a matrix that we can use to
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  really understand that. It's called the Eisenhower's matrix. It's pretty
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  easy to remember. All you need to do is draw
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  a Y axis and an X axis. And up here,
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  you've got urgent, not urgent. And then you've got important
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  and not important. In the top left quadrant, we've got
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  urgent and important. These are the things that we need
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  to do like right away because they're both urgent and important.
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  They're not urgent, but important. We need to actually schedule these.
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  This is where time blocking comes in, really important, making
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  sure that if it's got a deadline eventually, we're not
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  leaving it to the last minute, but making sure that
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  it is recognized and we're progressing it on a little bit.
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  This is where we can delegate. We can give it
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  to someone else so we can free up our own time.
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  We're going to delete.
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  Speaker 2: These things because... Why are we wasting our time?
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  Speaker 1: And then the next step that we're going to do
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  is we're going to get the people in the process.
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  Speaker 2: We're going to people the process.
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  Speaker 1: I like to say we don't actually want to distract
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  people in the early stage if it's not a problem
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  or if it's really not urgent and there's other pressing issues.
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  If you are CEO or, you know, the manager, you
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  need to be able to prioritize these kind of issues
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  as well. Let's say we have decided that it is
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  urgent or it is important and the time to decide
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  about it is now.
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  Speaker 2: This is when we get people in.
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  Speaker 1: You can do a brainstorming session, write down kind of
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  the people that have firsthand versus theorized knowledge. Firsthand knowledge
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  is they have experienced the thing or have a high
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  degree of conviction within the circle of competence. Whereas theorized knowledge,
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  it should be discounted. This might be someone that knows
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  someone that has.
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  Speaker 2: Dealt with it.
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  Speaker 1: While you're trying to get as much information as possible,
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  It really is a balance.
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  Speaker 2: You're kind of seeking.
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  Speaker 1: Two conflicting opinions so that you can judge those opinions
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  based on merit. At the same time, as Ray Dalio says, though,
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  like you're better off not asking uninformed people because it's
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  just going to confuse you. Sometimes that's better than having
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  no answers at all. One thing that I found very,
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  very useful and practical is actually just going on LinkedIn
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  or Twitter and doing a post trying to find an
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  expert in the subject. Then I kind of classify the expert.
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  They have first hand experience. And or can they recommend
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  me to someone that has? The questions that you can
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  ask these people is, you know, do you think this
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  is the problem? Have you experienced this before or heard
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  of anyone that has? How did they or you solve it?
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  Or do you have anyone else that I can talk
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  to about this topic? Now, one thing I will point
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  out is when you're talking to these people, you can
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  actually really shape their decision making. As a leader or,
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  you know, someone that they're trying to help, they will
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  generally just agree with you. Now, this is really important
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  around how you frame the story and frame the facts
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  so that you aren't getting your biases into their story
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  because they're just going to agree with you. One situation
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  that is a prime example of that is I had
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  a decision to make. I went to all of my advisors, mentors,
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  incredibly smart people, and I framed it in the wrong
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  way and it ended up costing me millions in a
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  legal battle. The other thing to consider in this situation
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  is two other concepts, which is skin in the game
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  and incentives. If you're consulting someone within your organization or
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  like an advisor that gets paid for success of course
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  their decision making and their inputs into the the thing
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  is going to push you to what helps them and
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  that's just a natural human desire. They have their own
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  confirmation biases. On the flip side, you know, it is
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  really beneficial if someone has skin in the game and
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  what we mean by that, if they actually have investment
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  on the line, they may be less prone to take
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  risks and make.
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  Speaker 2: Better decisions as well.
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  Speaker 1: So really do weigh those things up to make sure
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  that you're talking to the right people and getting the
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  right data for your next few steps.
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  Speaker 2: So the third step is actually just getting as many
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  alternatives as possible.
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  Speaker 1: You can add this to the master doc that we've
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  been preparing in the first and second stage.
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  Speaker 2: Sit with the person or sit by yourself.
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  Speaker 1: It could be a whiteboarding session with your team. And
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  you're just going to write as many alternatives for the
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  decision as possible.
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  Speaker 2: Because the truth is.
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  Speaker 1: There may be a situation where you can have your
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  cake and eat it too. I think too many people
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  look at decisions as there's two binary outcomes. It's like
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  either do this or do that. And there's been lots
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  of studies showing that if there's more options, people, executives
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  make better decisions. One good exercise here is actually the
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  vanishing options test. So for example, if you're thinking about
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  hiring a new employee to do TikTok, should we make
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  a hire to grow this channel?
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  Speaker 2: And you might want to.
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  Speaker 1: Remove that option in your workshop so that people are
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  forced to think about it differently. That will make people
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  think about what are some other systems and processes that
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  we could do. It might be that someone within the
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  team has work that they're doing that's providing no value,
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  that they have some level of creativity that they can
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  go and help you there. The next step is to
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  classify the complication. I'm gonna use two frameworks here that
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  you can incorporate in this step. The first one is
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  from Jeff Bezos and it was in one of his
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  shareholder letters. Again, we've got a four by four matrix
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  as we usually do. So at the top, we've got
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  irreversibility and we've got a line across here where this
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  is high and this is low. And then on this side,
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  we've got consequentiality. And then we've also got a similar
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  arrow where this is high and this is low. In
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  this top quadrant, top left quadrant, we've got a very
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  highly irreversible decision. meaning that we can't actually go back
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  on it once we decide. And then we've got, it's
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  got very, very high consequences. So a good example of
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  this would be like a big operating expense. You're going
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  to build a new factory. You're going to rent a
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  new office that you're going to grow into. That's like
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  a three-year list. So in these situations, we're going to
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  be slow and we're going to be calculated because it's very,
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  very hard to go back. In this top right section,
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  we've got a thing that possibly is reversible. So it's
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  not a one-way door, but it's got high consequentiality. So
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  it's a really important decision. This is where, you know,
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  for example, this could be, you know, you're a single
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  person founder and you're like, okay, is TikTok ads going
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  to work for me?
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  Speaker 2: I can buy all the media myself. It's not going
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  to have a.
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  Speaker 1: Huge opportunity cost. Should I test this new channel? It's
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  very low irreversibility. You just turn off the channel in
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  turn of consequence. It could be quite high because you
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  could double your business by creating a new marketing channel.
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  Speaker 2: So with this, you're going to test.
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  Speaker 1: And you're going to learn, and you're going to execute
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  it relatively quickly as well.
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  Speaker 2: Cause it's a, it's a high impact task in the
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  bottom quadrants.
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  Speaker 1: We actually have a Jeff Bezos refers to as type
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  two decisions. Now he just thinks that you should just
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  decide on those quickly. And he generally gets his executives
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  or the person that's best placed to make.
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  Speaker 2: The decisions to just decide fast and act.
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  Speaker 1: Now, this is actually a categorization model, which you can
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  actually look at a sense-making model. I believe that maybe
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  Jeff Bezos kind of adapted the Knife and Framework, which
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  I'm going to show you now. Kind of different looking
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  matrix here. So the idea of the framework is basically
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  you're trying to put the problem, the thing that you're
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  trying to decide to solve into one of these categories.
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  Down the bottom right, we have what's called the obvious. Now,
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  a lot of people think kind of all of their
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  decisions fall into this category. This is where there's no knowns.
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  And basically you can categorize it in this section and
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  you can apply best practice. The step-by-step process that you
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  would use for something like an obvious decision, which let's
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  say for example, it's you're getting a lot of customer
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  service emails. Should you apply a basic CRM framework or like,
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  sign up to gorgeous.
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  Speaker 2: That is, you know, best practice.
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  Speaker 1: It exists. You've got a very simple problem with cause
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  and effect. With this section, you're going to sense, then
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  you're going to categorize, and then you're going to respond.
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  We're sensing that we've got a problem. We've got lots
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  of emails. We're going to categorize it into a standard issue.
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  We can Google it. It's just common. And then we're
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  going to respond. We're just, we don't really need to
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  do too much more asides from rolling out the issue. Now,
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  one thing that may come up is this obvious section,
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  you might not need the entire decision-making framework that we're
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  going through because you're not really involving other people in
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  the process and it's quite a simple decision. So that's
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  kind of where, you know, once you get really good
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  at this, when you're defining the problem, you'll generally sense
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  how much, you know, if you need to get other
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  people involved. That's why I gave you those initial questions.
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  If it is complicated, which is actually this top right section,
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  you're gonna be glad we have our framework that we're
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  going through. So with complicated, this is where there's known unknowns.
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  So the best course of action is sense, analyze, and
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  then respond. Now, I like to really put a lot
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  of business decisions within the complicated section. There's variables that exist,
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  they're complicated variables, but there is some level of good
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  practice or best practice, and we're generally sensing the issue,
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  then we're analyzing it, we're bringing people in, And then
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  we're responding with, as I said, good practice. And a
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  really good example of this, business forecasting. Is your business
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  going to grow year on year? Like what is the
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  seasonality curve? You might need to bring in your operations team.
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  The top left section is actually complex. Now this is
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  where you're going to probe, sense, and respond. Now this
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  is where there's unknown unknowns. Now the main reason that
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  I really like the Knife and Framework is it is
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  humbling because if we can classify something as complex, we
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  know that we can't have a high degree of conviction
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  around the solution because there's an emerging practice, but it
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  might not be good or best practice.
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  Speaker 2: New product development.
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  Speaker 1: There are so many times anyone that's had a business
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  that's launch secondary products, like even at the UDI, we
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  have a high degree of conviction around these product categories,
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  invest lots in them and we're like, we're guaranteed this
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  is going to work.
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  Speaker 2: It is a complex system. There's different competitors.
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  Speaker 1: For that product category or the ad ecosystem might not
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  enjoy that type of content around that product.
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  Speaker 2: Chaotic. Now this is.
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  Speaker 1: Probably less relevant to business, but you're going to act,
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  sense and respond. Now this is basically where everything just
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  absolutely goes to a mess. A good example of this
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  is the Tylenol crisis. People were putting cyanide capsules within
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  the Tylenol packets. Johnson & Johnson in that situation, the
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  seller of Tylenol, was thrown into a chaotic situation where
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  lives are at risk. They really needed to act before
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  probably getting all the information that they'd want to, both
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  for people's safety and also their brand longevity. They managed
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  to pull it all off the shelves very, very quickly
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  and then do further analysis after that. probably less likely
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  you're going to be thrown into a chaotic situation. It
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  is our job as entrepreneurs and CEOs to avoid chaotic
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  situations by risk analysis before it becomes a chaotic situation.
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  And this is, I think, another thing that I love
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  about the Knife and Framework. We tend as entrepreneurs and
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  business owners to build systems to make things obvious all
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  the time. So we're trying to Put things into the
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  obvious category. We build certain meeting cadences where we're like, okay,
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  we meet on this every week. You know, forecasting is
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  good example or, you know, marketing plans and stuff like that.
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  And we're like, this is working, this is working. And
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  then an iOS privacy update hits. And then we're thrown
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  into a chaotic situation or a complex situation. And this
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  is why it's drawn a little bit weird. because this
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  section here is actually a cliff. We're operating in this
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  obvious section without constantly analyzing it and adjusting it. And
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  then suddenly we find ourselves in one of these situations.
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  So it's basically a lesson of not being complacent always
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  kind of analyzing and doing risk analysis. Now, once you've
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  gone through either the Jeff Bezos or the Knife and Framework,
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  you might wanna go back to step one or step two.
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  This is because you might have a lesser degree of
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  conviction because you understand the complexities around the system.
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  Speaker 2: So the next step I love to refer to as
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  data the decision.
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  Speaker 1: Now, this is really, really important and you can't skip
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  this step because we are human beings and we generally
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  have a lot of biases that affect our ability to
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  make the right decision in these situations. you've got two
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  types of biases that you need to be very, very
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  aware of. The first one is confirmation bias. Generally, when
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  we go into these decision-making, we have some level of
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  original position that we have. And that original position is
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  based on previous life experiences or previous things we've been told.
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  The truth is we might be wrong. but we have
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  this initial position that is hard to dislodge.
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  Speaker 2: We need to.
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  Speaker 1: Remove this if we're going to think objectively about the
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  decision and solve the problem accordingly. Data is one of
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  the best ways to do this. The second thing we
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  need to consider.
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  Speaker 2: As well is loss aversion.
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  Speaker 1: I think that this is a really, really big one
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  for someone that is already successful. Humans typically feel losses
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  twice as badly as they feel wins. So generally when
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  we have something that is worth keeping, we really, really
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  try to avoid losing it. Now, what this generally means
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  is we can sometimes become very risk adverse. There are
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  countless examples of this, obviously within business, like you want
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  to be Netflix.
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  Speaker 2: You don't want to be blockbuster.
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  Speaker 1: They obviously had a huge amount of loss aversion for
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  their existing business model, their huge amount of stores and
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  stuff like that. They failed to make their products digital.
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  And then Netflix obviously started taking their market share. The
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  main thing that I like to do once I have,
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  you know, the alternatives and I started to get a
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  lot of data. I like to make a decision making
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  tree and try to assign probability to that. One of
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  the key things with the decision making tree is the
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  formula return on invested capital. We are constantly looking at
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  decisions that we make within business around return on invested
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  capital or return on invested time as well, if we're
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  a solo entrepreneur. So for example, let's work with a
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  growth plan. We've got two options as a business. We
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  can either create a new product, launch it in our
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  existing region, Or we could expand globally like my UK
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  example before. So here we have growth plan, and then
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  we have our two decisions out this way and out
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  this way. We have new product and we've got new region.
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  Now looking at this, you probably spotted a flaw. We've
• 14:51
  got new product and new region, but because we've mapped
• 14:54
  out all of our alternatives before, we also know that
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  there's a fourth option, which is actually do nothing. which
• 15:02
  is basically we're going to continue investing our funds into
• 15:06
  our existing products and our existing region as well. So
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  we've got all of our decisions here now, but we
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  need to start adding data to this decision-making tree so
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  we can start visualizing it. Now, the new product, we're
• 15:18
  going to use a return on invested capital framework.
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  Speaker 2: This might cost $ 50, 000 to create. Now, you'll.
• 15:25
  Speaker 1: You've got to get your team to come up with
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  a product. There's a lot of development. You know, that
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  MOQs of the order is going to be high rather than,
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  you know, say an existing region where you're just selling
• 15:38
  your existing products. This might only cost like $ 10, 000 because
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  all you really need to do is grab the product
• 15:45
  from your warehouse that already exists.
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  Speaker 2: And pay the shipping costs.
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  Speaker 1: You might need to set up a warehouse or a 3PL,
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  which is relatively cheap.
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  Speaker 2: So we've got a.
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  Speaker 1: Significant difference in costs here. And then we've obviously got
• 15:59
  do nothing, which cost is $ 0, which isn't actually the case,
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  which I'll get into in just a little bit when
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  we look at the returns of this, but it's something
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  to consider there as well. So the next step that
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  I like to do is actually just draw a line
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  down here and we're going to do our return on
• 16:16
  invested capital. So basically if we say, let's say we
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  invest $ 1, 000 into this new product, and we make $ 100, 000,
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  that's 1,000% return on investor capital. You could use percentages,
• 16:30
  but let's just say dollar amount. This product is going
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  to make us $ 100, 000 over a set period of time. Remember,
• 16:37
  you need to keep the time variable consistent across this.
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  Speaker 2: Hitting a new.
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  Speaker 1: Feature product for Udi, it's not going to beat the Udi.
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  We might just be happy that it's doing you know,
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  say 10% within that region of that product category. The
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  second option here is perhaps we are able to sell
• 16:54
  the new product, but it's not a huge success. There's
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  no really returns, but we're able to kind of sell
• 17:00
  the inventory at cost and get our money back. So
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  let's just say, um, we have a $ 0 here. The
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  other option is it completely bombs. It's we spent all
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  the money on development, all of the money on, on
• 17:12
  people as well. and it turns out that we minus
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  the $.
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  Speaker 2: 50, 000 here.
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  Speaker 1: Now, if we're going new region, let's say we're going to,
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  there's a good chance that if this region works, let's
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  use the UK versus Australia example, the region is considerably
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  bigger than Australia, the trend is really hot, so we
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  might be able to expect a $ 1 million return for
• 17:37
  this option because we can apply all of our same
• 17:40
  processes Despite the initial cost being $ 10, 000, we're going to
• 17:45
  have to reinvest more in inventory, obviously, but we're talking
• 17:49
  about upfront the returns, the data that we're going to
• 17:52
  get from this decision that we're about to make, whether
• 17:54
  to invest in a new product or send some product
• 17:56
  over to the UK.
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  Speaker 2: The probably middle tier.
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  Speaker 1: Again, is let's say $ 0, we get the stock there
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  and we have to clear it at cost. There's always
• 18:04
  going to be a couple of options there. And then
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  the other one is minus $ 10, 000, which we've invested into
• 18:09
  the product. And then of course, down here, we've got $ 0. Now,
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  the final step that I like to do is I
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  like to look at all of these options and I go, okay,
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  which is most likely? Let's say the new product hitting
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  success in my existing region is maybe based on my
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  past success is maybe a 30% chance in terms of
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  clearing it, might be more of like a 60% chance.
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  And then there's a 10% chance I'll lose it all.
• 18:39
  I'm just giving some examples here. And then in terms
• 18:43
  of the new region, what I'll look at is, you know,
• 18:46
  considering I will look at a lot of different variables,
• 18:49
  some of them that I've just mentioned, but there's maybe,
• 18:51
  you know, a 60% chance that I can make this
• 18:54
  new region work with all of my existing systems, maybe 30%.
• 18:58
  And then again, a 10% chance that it will fail.
• 19:03
  And then, you know, this is obviously 100% because it
• 19:06
  needs to add up to 100%. So just by visualizing this,
• 19:10
  you can see that the best option is probably to
• 19:13
  do a new region with my existing products because there's
• 19:16
  a significant upside, a lower upfront cost, and yeah, not
• 19:22
  that much downside. The one thing I will talk about
• 19:25
  is around doing nothing is a big factor of, you know,
• 19:29
  by doing nothing, you're basically saying that that time and
• 19:33
  that capital should be applied to your existing framework, your
• 19:37
  existing flywheel. So you could calculate your return on invested capital,
• 19:42
  let's use URI for example, for our existing regions and
• 19:45
  our existing product categories. What is our return on invested
• 19:48
  capital there? And that should actually be this metric down
• 19:52
  the bottom right. So if you are a new entrepreneur,
• 19:54
  the truth is this doesn't actually incorporate all the data
• 19:57
  that you're going to need. You might want to be
• 19:59
  looking at more things than return on invested capital, like
• 20:01
  cashflow as well, because this new region, as I referenced before,
• 20:07
  While the upfront cost to get to the more data
• 20:11
  and to feel out the decision may require a significant
• 20:15
  amount of cash upfront before we can realize this million
• 20:20
  dollars of return on invested capital. So keep that in
• 20:24
  mind as well. Also keep in mind the opportunity cost
• 20:27
  of your team and what they're currently working on, as
• 20:30
  well as other risks that we kind of can highlight
• 20:33
  through the rest of the process. So if we can recap,
• 20:35
  we've basically decided if the problem's worth solving, we've gone
• 20:39
  through all of the alternatives, we've brought people in, we've
• 20:42
  classified the problems, we've identified risks through categorizing the product.
• 20:47
  We've really used data to refine our decision-making. Now it's
• 20:51
  time to actually decide. Now it's really important that when
• 20:54
  you're leading teams, that once you make the decision, the
• 20:57
  whole team rallies behind the decision. There can be really
• 21:00
  thoughtful disagreement during the process, during what we've just talked about.
• 21:05
  But once you have a high degree of conviction, this
• 21:07
  is the path that you need to do. Then everyone
• 21:09
  needs to rally behind it. Now this can be done
• 21:12
  through milestones, such as, you know, if we look at
• 21:15
  the categorization method and we're like, okay, we only need
• 21:18
  to probe sense, and then respond, then we may be
• 21:21
  able to build some tests around the decision rather than
• 21:24
  just going fully in. This is where I do like
• 21:27
  OKRs because we can basically go, let's say we're talking
• 21:30
  about new product diversification. You can put a really small
• 21:34
  milestone three months out that you're going to at least
• 21:37
  get that product up on Facebook ads to test if
• 21:40
  it's going to work in the future. You don't want
• 21:42
  to put a KPI or something like that with your
• 21:45
  team to say, we need to do $ 500, 000 in sales
• 21:49
  for this product category when you don't even know it's
• 21:52
  going to work. If there's no urgency around the decision,
• 21:54
  make sure that you build some sensible tests.
• 21:58
  Speaker 2: To cover your risk as well.
• 21:59
  Speaker 1: If you enjoyed today's episode, make sure you leave some
• 22:01
  feedback below. I know it can be a little bit dry,
• 22:03
  but this kind of stuff is going to change your life.
• 22:06
  And just being a little bit more pragmatic and slow
• 22:09
  to make the decisions where you're able to make slowly
• 22:12
  is really going to get you out of a lot
• 22:13
  of mess. So in terms of a series of questions
• 22:16
  and also this entire framework, I'll leave it in our
• 22:19
  mentorship group and you can go join and check it
• 22:22
  out there.
• 22:22
  Speaker 2: Yeah, don't forget to like and subscribe. Thanks.