The root node is your chance to create a solid foundation for the growth of your tree. It's where you define your central question, keeping it as simple and focused as possible. It may be a simple yes or no question—like whether to redesign a product—or a question with multiple options like what consulting firm to hire. But either way, it should be one question for which you want to find a single answer.
For our example, your root node may be: Should my company move to the office development or the building downtown?
Branches are the lines that grow from the root node and connect it to the leaves. Each branch represents a choice or an option and can include information like the cost of that particular choice or the likelihood of an event happening.
For our office relocation decision tree, your root node will generate two branches—one for each of the two options. But of course, if the decision were that easy, you wouldn't need to grow the tree at all. In our example, you can use your data about cash flow for facilities management to help project future data and assign a probable cost to each choice.
Sometimes there are only a limited number of paths from a node. In computer-based decision tree algorithms these are known as categorical variables, in which data is assigned to one of a certain number of predetermined categories.
A tree looks pretty bare without leaves, and the leaf node is where your decision tree starts to bear fruit.
Leaf nodes, sometimes called child nodes (because they grow from the branches of former decisions and choices) represent either a place where your decision tree path divides into more branches (and more options for you) or comes to an end point, allowing you to see the ultimate result of your decision path.
There are different kinds of leaf nodes, which can be either internal (leading to more choices) or end nodes (where you can see one of your possible destinations). Internal leaf nodes themselves can be either a point where you make a decision, sending your business down one path or another, or chance nodes, where you can project what might happen in circumstances that have some element of chance.
Let's look at each type in more detail.
The most common way to represent a decision point is with a square box, but however you choose to visualize it, this type of internal node is a point where you (or your organization) need to make a decision. Using your decision tree, you can think through the consequences of that decision to see how it might play out, then go back and do the same with different decisions and pathways.
For our example case, if the business is one that serves customers on-site, which location would be better for both your existing and potential customers? If your employees regularly attend industry networking events, which location makes that easier for them?
Cost is a factor in most business decisions, and it's no exception here. Before you can make an informed decision, you'll want to know the cost of both moves for things like leasing space, renovation, utilities, insurance, and the actual move. Once you have an estimate, you can enter that information into your decision tree.
One decision that may need to be made early on is whether your employees will all be on-site, or if some will be remote, or if some may work a hybrid model of the two. Depending on what you decide, you'll need a different amount of office space and may want to prioritize a location that's convenient for your on-site staff. Each of those options will send you down a different path to the next node.
When there's an uncertain outcome, many trees use a circle to indicate that there's some element of chance—or at least an outcome to that choice that's not entirely controllable.
For example, your decision about where to expand your offices to may be influenced by the possibility of reduced demand for your product.
You can make some predictions about the likelihood of that based on market research, studies of the economy, and past performance. But it's impossible to know precisely how the future will unfold, so the chance node is where you will explore what will happen if any of several different scenarios occur.
Like anything, there's an element of chance even with the best research. You may have several consistent estimates for renovations, but if the contractor discovers additional pipes that need to be replaced once the work starts, that will change things.
With a chance node, you can make your best guess—informed by discussing the situation with experts, learning what other businesses in the building have experienced, and estimating your "just in case" costs. If the renovations come in at or under budget, your chance node branches off one way. If they cost more than you expect, it goes
Assign a chance percentage to each outcome so you can look at the completed tree and understand the estimated likelihood of each scenario, then compare it to the costs and benefits. You may have access to information that can help you estimate those percentages, but chance being what it is, you won't be able to put an exact value on it, so take your best guess.
Both decision nodes and chance nodes are internal nodes—places where you have to make a decision or acknowledge that there is some element of chance, which may grow your tree in one of multiple ways. But sometimes leaf nodes are the last step in the decision process. When a branch leads to a final leaf node (also known as an end node), that is one possible outcome of the decision.
In our example, there may be many different end nodes depending on how much information you have available and where you put your resources and priorities.
You may have a lot of data that allows you to make confident predictions showing that the move downtown will be more expensive but ultimately result in longer-term benefits. On the other hand, the move to the office development outside the city center may be a safer short-term bet but could leave you outside the cutting edge if potential future employees want a workplace with more city-center convenience and amenities.
Whichever option you choose, your decision tree will allow you to run the scenarios with all the data to see where each path leads, resulting in a more informed and confident decision.
Five steps to grow your own decision tree
There are many possible uses for a decision tree and the tree model itself can be complex, especially in computer models with access to a lot of training data. But the process for creating one—whether it's a sketch on the back of a napkin or a whole forest of decision trees for analyzing financial transactions—is relatively simple.
Step #1: Use a template or software program
It's possible to draw a tree by hand or create one in a word processing or simple graphic design program. But complex trees for complex decision-making will benefit from a template or program designed for that purpose.
- Microsoft offers tools for making flowcharts, which can be customized for your decision tree.
- smartdraw's decision tree templates are made for specific types of business decisions like financial risk analysis, company mergers, and project development.
- Lucidchart uses a simple drag-and-drop interface to create attractive and visually simple decision trees so you can focus on creating the content.
Step #2: Start with one main question
An individual decision tree works best if it's focused on one discrete question. While many of the issues involved with your decision may be complex, the starting question itself should be relatively simple. Some types of business questions to consider:
- Yes-or-no questions, like whether to move forward with a business merger
- Either/or questions, where you know that action will be taken, but need to decide between two options
- Single-select multiple-choice questions, where you have a few options but can choose only one, like the best person to hire to head up a new division
Step #3: Do your research
Your decision tree is only as good as the data you use to grow the tree. Before you start, make sure you have accurate information about necessary resources, timelines, or anything else that will inform the costs and probabilities associated with each choice.
If you are deciding between two different consulting firms to revamp your hiring practices, you'll want to know as much as possible about their fee structure, their success rate, the estimated time they need to complete your project, and any other key metrics that are important for your brand.
Step #4: Input all the options
Even when you think you're done, go back and see if there are nodes and branches you haven't considered. There's no downside to including and envisioning unlikely or innovative scenarios. Once you compare the resources each option would take and the well-informed probabilities of the chance elements, it may become clear that the new customer segment or merger you've always assumed was out of reach is more practical than you thought.
In addition, are there missing values or any data or metrics you hadn't considered? If so, grow a new branch or add leaf nodes. Including accurate information can involve some element of computational complexity, but putting in the time now is much less work than taking action without imagining all the scenarios first.
Step #5: Make a decision
Here's where the benefits of a decision tree really pay off! Now that you've done all the work of growing your tree and considering all the variables of the situation, you should be confident that you're making the best decision possible with the information you have. While the future is never certain, a decision tree can give you confidence that the basis for your decision is strong and healthy.
Whether your decision tree is hand drawn or you use one of the many available decision tree algorithms, this handy tool can make even the most complex business decisions more rational and straightforward. Small businesses face plenty of challenges every day—attracting customers, building business relationships, and staying ahead of the competition. Let a decision tree take away some of the uncertainty of choosing a path forward.