A Simple Tool to Make Radically Better Decisions

Have you ever made a high-risk decision? If so, did you think for a while before making the “right” decision, and even then, you still weren’t sure about the best course of action?

In cases like these, you may need a decision tree. It’s more formal than a chat with a friend or a list of pros and cons.

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Here, we’ll show you how to build a decision tree and analyze risk versus reward. We’ll also look at some examples so you can see how other marketers have used decision trees to become better decision makers.

Table of contents

What is a decision tree?

Decision tree analysis

How to create a decision tree

How to create a decision tree in Excel

Examples of decision trees

When it comes to marketing, decision making can be especially risky. What is it that my colleague is so attached to a new product that she does not want to mention any of its shortcomings? What if my marketing team doesn’t care about office growth, but haven’t considered how it will affect our long-term strategy?

The visual element of a decision tree helps you include more actions and potential outcomes than you might if you just talked about them, mitigating the risks of unintended consequences.

Plus, the diagram allows you to include smaller details and create a step-by-step plan, so once you choose your path, it’s already laid out to follow.

Decision treeA decision tree contains four elements: the root node, the decision nodes, the leaf nodes, and the branches that connect them.

  • The root node is where the tree begins. It’s the big problem or decision you’re tackling.
  • As the name suggests, the decision nodes represent a decision in your tree. They are possible ways to “solve” your main problem.
  • The main nodes represent the possible outcomes of a decision. For example, if you are deciding where to eat for lunch, a possible decision node is eat a hamburger at McDonald’s. A corresponding leaf node could be: Save money by spending less than $5.
  • Branches are the arrows that connect each element of a decision tree. Follow the branches to understand the risks and benefits of each decision.

Now let’s explore how to read and analyze the decisions in the tree.

Decision tree analysis [Example]

Let’s say you’re deciding where to advertise your new campaign:

  1. On Facebook, using paid ads or
  2. On Instagram, using influencer sponsorships.

For simplicity, we’ll assume both options appeal to your ideal demographic and make sense for your brand.

Here is a preliminary decision tree that you would draw for your ad campaign:

As you can see, you want to put your end goal at the top; in this case, Advertising campaign it’s the decision you have to make.

Next, you’ll need to draw arrows (your branches) to each potential action you can take (your leaves).

For our example, you only have two initial actions to take: Facebook Paid Ads or Instagram Sponsorships. However, your tree may include several alternative options depending on the goal.

Now, you’ll want to draw branches and leaves to compare costs. If this was the final step, the decision would be obvious: Instagram costs $10 less, so you’d probably go for it.

However, this is not the final step. You have to figure out the odds of success versus failure. Depending on the complexity of your goal, you can look at existing industry or past project data at your company, your team’s capabilities, budget, time requirements, and expected results. You can also consider external circumstances that can affect success.

Assessing risk versus reward

In the advertising campaign example, there is a 50% chance of success or failure for both Facebook and Instagram. If you are successful with Facebook, your ROI is around $1,000. If it fails, you risk losing $200.

Instagram, on the other hand, has an ROI of $900. If it fails, you risk losing $50.

To assess risk versus reward, you need to figure out the expected value of both paths. Here’s how to find your expected value:

  • Take your predicted success (50%) and multiply it by the potential amount of money earned ($1,000 for Facebook). That’s 500.
  • Then take your predicted probability of failure (50%) and multiply it by the amount of money lost (-$200 for Facebook). That’s -100.
  • Add these two numbers together. Using this formula, you will see that the expected value of Facebook is 400while the expected value of Instagram is 425.

Expected value

With this predictive information, you should be able to make a better and more confident decision; in this case, Instagram seems to be a better choice. While Facebook has a higher ROI, Instagram has a higher expected value and risks losing less money.

How to create a decision tree

You can create a decision tree by following the steps below. Remember: oOnce you complete your tree, you can begin to analyze each decision to find the best course of action.

Decision tree analysis

1. Define your main idea or question.

The first step is to identify your root node. This is the main problem, question or idea you want to explore. Write your root node at the top of your flowchart.

2. Add decisions and potential outcomes.

Then expand your tree by adding potential decisions. Connect these decisions to the root node with branches. From there, write the obvious i possible outcomes of each decision.

3. Zoom in until you reach the endpoints.

Remember to specify each decision in your tree. Every decision should come to an end point, ensuring that all outcomes surface. In other words, there is no room for surprises.

4. Calculate risk and reward.

Now it’s time to crunch the numbers.

The most effective decision trees incorporate quantitative data. This allows you to calculate the expected value of each decision. The most common data are monetary.

5. Evaluate the results.

The last step is to evaluate the results. In this step, you are determining which decision is most ideal based on the amount of risk you are willing to take. Remember that the highest value decision may not be the best course of action. Because? While it comes with a high reward, it can also come with a high level of risk.

It’s up to you, and your team, to determine the best outcome based on your budget, schedule, and other factors.

Although the ad campaign example had qualitative numbers to use as indicators of risk versus reward, your decision tree could be more subjective.

For example, you might be deciding whether your small startup should merge with a larger company. In this case, there might be math involved, but your decision tree might also include more quantitative questions like: Does this company represent our brand values? If not. Do our customers benefit from the merger? If not.

To clarify this point, let’s take a look at some examples of various decision trees.

Examples of decision trees

The following example is from SmartDraw, a free flowchart creator:

Example 1: project development

Here’s another example from the Become a Certified Project Manager blog:

Example 2: Office growth

Here’s an example of Statistics How to do it:

Example 3: Develop a new product

To see more examples or use software to create your own decision tree, check out some of these resources:

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Remember that one of the best advantages of a decision tree is its flexibility. By visualizing different paths you could take, you may find a course of action you hadn’t considered before or decide to combine paths to optimize your results.

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