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Using Decision Trees to Improve Your Business: A Comprehensive Guide

Decision trees are a valuable tool for analyzing complex decisions. Learn how to understand, create, and use them to make strategic business choices.

As a business owner, it may feel like most of your job is making decisions. Whether it's whom to hire, what product to launch, or how to retool your marketing campaign, it's easy to feel overwhelmed by options. There are so many factors to consider—your customer base, sales figures, growth's surprising any decisions get made at all!

A decision tree can increase your business performance by helping you make better decisions. It takes into account the most important factors involved in each decision and analyzes them in a rational, visual way. Decision trees process information in a recursive manner—asking questions and breaking down the problem into smaller and smaller pieces until you have an answer.

Understanding what decision trees are, how to grow one, and how to use decision tree models to think critically about your business operations can give you more confidence that your decision-making will lead you down the right path.

What is a decision tree?

At its most simple, a decision tree is a visual method of analyzing and making complex decisions. It allows you to see all the possible paths you can take, based on numerous factors, including both things that are within your control and things that have some element of chance.

Decision trees can be especially helpful when outcomes are uncertain. Analyzing and incorporating data points, sales metrics, and probabilities—sometimes using well-informed guesses—can help determine which path is the right one for your company.

Sometimes the decisions that need to be made involve complex mathematical computations or the inclusion of other algorithms. In those cases, it's possible to use a collection of decision trees known as a random forest. A random forest works best when options are being run through a computerized decision tree algorithm and there is a lot of uncertainty, like with a volatile stock market.

Using multiple trees at the same time allows the computer to randomize the data points for the target variable and then average the results to determine the most likely outcome for each decision. Random forests work best for businesses that have the need and digital infrastructure to analyze, for example, complex financial data points.

Benefits of the decision tree approach for small business owners

There are many ways to approach decision-making for small businesses. Read on to discover the benefits of the decision tree model and see why many business owners find it useful.


The optimal decision tree includes all the information it needs and no more. Since the benefit of a decision tree is to clarify what can otherwise be a confusing and overwhelming task, this method allows you to focus just on the factors that really matter.


A decision tree is a visual tool—whether it's hand drawn or generated by a design program. It's especially useful for people who process data visually and find it easier to wrap their head around all the information that goes into a decision when they can follow a path along the branches.

It's also useful to test out other decision paths. In this way, one can consider all the possible outcomes and identify the relationships between decisions and outcomes.


Making decisions is easier if you have clear reasons for making one choice over another. Decision trees can incorporate data to clarify the costs of different options and the statistical likelihood that certain events will happen. Your business decisions don't need to feel like taking a shot in the dark.

The decision tree model is particularly useful for businesses in the digital economy, where a large data set can be overwhelming unless it's incorporated as one clear element in a decision.

When small businesses might use decision trees

Using a decision tree for important business moves can help accelerate growth, improve your customer experience, and decide the best path forward for the company. These are just a few ideas for the applications when small businesses might consider using a decision tree.

Analyzing complex issues

The decision tree model is particularly suited for making difficult decisions with a large data set. When one decision leads to numerous other possible options, seeing all the potential pathways laid out makes it easier to get a handle on the big picture.

Strategic planning

Many businesses become consumed by day-to-day operations and find themselves going down a path without a solid roadmap. Decision trees are a great tool to think about where you want your company to be in a year, 5 years, or more.

Using a decision tree to see where fresh ideas can take you or to project the growth of your brand if you add a new product or service means you can think strategically and make decisions with confidence.

This can be especially useful in the early years of a business when you have the chance to make small adjustments will have large effects down the road. The visual nature of decision trees means that you can see how small variations in actions near the root of the tree result in a larger divergence of ultimate outcomes.

Marketing efforts

Your marketing budget and staff probably aren't unlimited, so deciding how to spend those valuable resources often involves analyzing numerous factors. If you run print ads, your budget for community sponsorships will be lower. Video can be more expensive to produce, but maybe it's more eye-catching than static graphics.

To decide what method will be most useful for attracting customers, a decision tree can help businesses examine their data to find ways to increase sales and overall company growth.

Data analysis and machine learning

Many computer programs use data to train computer models to run decision trees that analyze information. One popular use of these programs is to generate text. The algorithm makes predictions about the next most likely word or data point based on the sample information it's been given. Of course, decision tree learning is only as good as the training data it has.

Decision tree algorithms are also used for machine learning tasks known as classification and regression tasks. In simple terms, a decision tree classifier decides which category something fits into (like whether a customer comment is positive or negative) while regression tasks are used to predict a specific value, like the most profitable price for a product.

Having a clear decision model structure makes it easy to see how computer programs make the decisions they do about what action to take—whether it's the wording used by a chatbot in a customer service query, or the placement of ads by an AI-directed program.

The elements of a decision tree model

In order to examine the parts of a decision tree, let's look at a concrete example. Your small business is growing fast—that's good news! But you've outgrown your current office space and need more room to accommodate your expanding staff. You've narrowed your options to: A) lease a ready-to-move-in space in a newly built office development on the outskirts of the city or B) renovate space in a historic building in the city center.

Let's use this example to see how a decision tree might help by looking at each specific attribute of the model.


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.

Decision nodes

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.

Chance nodes

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 another.

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.

End nodes

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.

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