Originating in the 1960s, decision trees are more popular than ever. Their problem-solving benefits are so well proven that technology experts are using them in machine learning. You certainly don’t need to be an IT specialist to take advantage of decision trees. Decision trees can help professionals in any industry solve a host of problems once they have a rudimentary understanding.  

Once you’re familiar with the basics, their applications become endless. So how does a decision tree work? First, let’s start with a quick introduction.

What is a Decision Tree?

A decision tree is a non-linear visual map of decisions or actions and their outcomes. Their roots, branches, and leaves guide the reader along a sequence of possibilities and outcomes. This wholistic illustration shows all the consequences of a single decision or action. Each pathway stems from an idea or question and leads to multiple choices, each helping you reach a final decision.

Why decision trees are important

Decisions are rarely straightforward, no matter how simple they might seem. There are always multiple outcomes, each with its own ripple effect that ultimately impacts another area of your business. Decision trees help you see those outcomes and ripple effects to make the most educated decision possible.

For example, you might be looking at switching to a new piece of software, one that’s cheaper. But, after drafting up a decision tree you realize that new piece of software might result in numerous instances of other tools not syncing, which requires those tools also get replaced with other options. In the end, what seemed like a cheaper option could end up costing you more in numerous new software solutions.

Ultimately, decision trees help you see every potential outcome and how it impacts your business, before you actually make the decision.

Pros and Cons of Decision Trees

While decision trees help you make more informed decisions, there are of course drawbacks. It’s important to understand the strengths and weaknesses of a decision tree so you can know what to expect. The pros and cons are largely dependent on how they’re used in a given scenario (more on that later).
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Decision trees are great problem-solving tools because they’re:

• quick to create with almost no risk—you have nothing to lose!
• easy to read, making them ideal for multiple users
• flexible to change and edit at any time
• applicable to endless scenarios or use cases

Decision trees might not be a good option because they:

• don’t account for continuous changes or variables
• can quickly become convoluted and over-complex
• must be updated if any information changes
• only reflect information you include

Like many techniques, context matters. The above breakdown is intended to give you a general understanding so you can quickly add another arrow to your problem-solving quiver.  

How Does a Decision Tree Work?

Learning how a decision tree works is simple—this is one of the main reasons they can be so beneficial. The world of decision trees and their applications quickly opens up once you master the basics. Nearly every tree is comprised of these core components, even though there are a variety of elements and methods.

• The root node: This is the tree’s starting point, or the main goal or decision to which all other components trace back. If you’re looking to answer a question, it goes in the root node.
• The branches: These are the pathways that link all the choices and steps of your tree. Because decision trees are non-linear, you can travel back and forth between ideas to discover new possibilities or answers. Usually, each branch is lead by an arrow, pointing to the next node.
• Leaf nodes: These are the branches extending from root nodes, which lead to another choice or leaf node. Unlike root nodes, there are always multiple leaf nodes. Each one represents a new decision or action related to the root node.
• Decisions: The network of branches and nodes creates an extended map detailing all the variables that help you come to a final conclusion. After reviewing the decision tree and its pathways, you may find the answer to your question before you exhaust all variables. If not, keep traveling through the map and reach the end.

While decision trees vary in size depending on the nature and complexity of the question or topic you’re exploring, every tree will follow the above model.

The Best Times to Use a Decision Tree

Getting the most out of a decision tree depends on when and how you use it. Understanding how decision trees work and when to implement them depends on your goal. Using a decision tree is an ideal technique when your objective is to explore multiple outcomes or capture a sequence of events. There are two main scenarios or applications where decision trees shine; decision analysis and process branching.

Decision Analysis

Decision analysis is the process of weighing risks, rewards, and possibilities when making a critical choice. Decision trees bring clarity to complex problems by displaying the rippling effects of any given decision. An objective decision can be confidently made with all this information readily available. In 1999, Gerber encountered a challenging scenario which they strategically chose to untangle using a decision tree.

“Gerber, the well-known baby products company...used decision tree analysis in deciding whether to continue using the plastic known as poly-vinyl chloride or, more commonly, PVC.” Pending an investigation by the Consumer Product Safety Commission, they could either take a passive stance or take action.

Regardless of Gerber’s final decision, the takeaway is clear: decision trees are created by massive corporations in times of crisis to make accurate, informed choices. That was true in 1999, and it’s even more true today.

Process Branching

Decision trees are an effective tool for mapping workflows and complicated processes in any industry or team. For organizations with complicated protocols and vast amounts of information, decision trees can make a huge difference.

Situations such as buyer journeys, call scripts, or support chains are prime contenders for decision trees. Large teams in need of one succinct source for “what to do if” situations will find great utility in a decision tree. At organizations where process is fluid and constantly evolving, changes can be captured and disseminated quickly due to the flexibility of the decision tree model.

For “if this, then that” situations, decision trees eradicate any grey area by plotting out every possible scenario.
Tidio, a company that provides automated chatbots, claims, “if we tried to boil chatbots down to their basic components, they would turn out to be little more than decision tree diagrams...”

A Tree Worth Climbing

The benefits of experimenting with decision trees come with almost no costs except for the time it takes to create one. If you have pen and paper, you can make a decision tree. But don’t confuse simplicity with low impact.

In the realm of technical decision-making, decision trees are at the forefront. Dr. Sam Kirshner, Senior Lecturer at the University of New South Wales’s business school, suggests, “...almost all areas and industries are using predictive analytics involving decision trees.”

Luckily you don’t have to be a computer to effectively weigh risk, reward, and probable outcomes. Next time you’re at a crossroads, consider climbing a decision tree.