Stop Analysis Paralysis With These 5 Best Decision Tree Tools

Decisions are tough. Learn how decision trees can make your life easier, and some handy tools to start using them right away.

Stop Analysis Paralysis With These 5 Best Decision Tree Tools

If you've ever found yourself stuck with multiple choices, wishing you had an advisor to help you make the best, most perfect decision, you need to experience the magic of the decision tree.

Maybe you're trying to decide where to go for dinner or which butternut squash stew recipe to try. Maybe you're programming a customer service bot or in charge of a 5-year financial investment plan for an engineering firm. Whatever (equally important) decision you're making, a decision tree can help. In this post, we break down what exactly decision trees are, why they're important, and what the top decision tree software and tools are in 2023.

What are decision trees?

Put simply, a decision tree helps you make a decision or solve a problem by mapping out all the possible options, steps, and outcomes in a tree-like diagram.

It typically starts with a single question or problem called the "root." The root then branches into different choices or conditions ("branches"). Further branching occurs based on additional choices or conditions, ultimately leading to various endpoints (or "leaves") which represent the final outcomes or decisions.

Here’s an example of a decision tree that helps you decide what you should have for a snack on any given day.

A decision tree for choosing the perfect snack

But decision trees are often used for much more complex questions and processes.

  • Customer service: guiding agents through a series of structured questions and paths to identify and solve customer issues with a standardized approach, as well as programming chatbots to ask relevant questions and provide appropriate responses.
  • Product development: helping identify potential paths for development processes and their various expected outcomes.
  • Corporate strategy: assisting executives in visualizing the potential impacts of various strategies.
  • Machine learning: in the fields of data science and machine learning, assisting with classification and regression tasks to predict outcomes based on input features.
  • Healthcare: diagnosing medical conditions by mapping out symptoms and potential causes.
  • Manufacturing: aiding with quality control in product development processes by identifying potential causes of defects and developing strategies to prevent them.
  • And more!

Here is an example of a much more complex decision tree that lays out the options for a chemical manufacturer trying to decide whether to invest in a small or large chemical plant. It includes financial data and timelines.

A decision tree showing outputs of investing in a large or small chemical plant

Source: HBR

There are two types of decision trees — classification trees and regression trees.

Classification trees typically deal with "yes" or "no" questions and are best suited to solve real-world problems and topics. Regression trees are designed to predict continuous values and are built from historical data. They are often used in finance for predicting stock prices, in healthcare for predicting disease progression, and in real estate for predicting house prices, among other industries.

Why are decision trees important?

Simplified decision making

This one's almost too obvious to be said out loud, but we're saying it anyway. In all use cases, decision trees help break down complex problems into more manageable parts, facilitating an easier decision-making process — no matter how many stakeholders are involved.

Empirical decision making

Besides simplifying the decision-making process, decision trees promote a more scientific approach to decision-making. They encourage problem solvers to consider different options and their potential outcomes based on data and evidence rather than relying on intuition or gut feeling. (So, even if your gut is telling you crackers and cheese, science might tell you fruit leather is actually the snack you need right now.)

Visual clarity

Having a graphical representation of choices also helps simplify the decision-making process for many people. It’s easier to identify potential risks, outcomes, and options. This is especially important for presenting to stakeholders who may not be as familiar with the subject of your decision as you are.

Quick training

Especially in high-turnover fields like customer service, decision trees take the bulk of problem-solving off of employees and ensure that even new employees are able to make decisions and offer solutions that make sense, using a pre-defined and standardized decision-making framework.

5 best decision tree tools and software to help you work smart


Our low-code browser extension builder enables productivity nerds to build all sorts of automations to make daily tasks easier and more efficient. Decision trees are one of those many awesome automations.

Our decision tree making mod could be adapted to any use case, but so far, we’ve found it to be especially useful in the customer service space.

Pixiebrix decision trees are endlessly customizable and easy-to-use. You design the logic of your decision process in a spreadsheet, connect it to your decision tree mod, and are then able to access it from any webpage and quickly make decisions. A sidebar appears to walk you through the tree and help you take the right action at any stage.

As a decision tree making software, Pixiebrix goes beyond simply visualizing the decision-making process to guiding users through a decision-making process step-by-step. It’s there for you whenever you need to make a decision on any webpage!


A decision tree made with Lucidchart
Source: Lucidchart

When you search online for “decision tree software,” what comes up most often is diagramming software, a.k.a. tools that help you design and visualize pretty decision trees you’ve already crafted in a spreadsheet (or in your mind). If that’s what you’re looking for, Lucidchart is one of the best and most popular tools out there.

Lucidchart is known for its intuitive interface, large library of templates, and collaboration features. It calls itself an “intelligent” diagramming platform, meaning that it can link to and import data and offers “auto-visualization” to help generate charts, so you’re not always starting from scratch. It’s best for people who want to visualize decision-making processes and might not be the best for complex modeling tasks or guiding you through a live decision-making process.

There’s a free plan, but you can also pay as little as $8 per month to get started.


Decision tree software dashboard by Venngage
Source: Venngage

Venngage is another visual-forward decision tree making tool. They call themselves an “infographic creation platform.” They also offer a large library of templates and collaboration features. Without the “intelligent” capabilities of Lucidchart, however, they are basically an easy-to-use design tool for when you’re ready to present your decision tree to stakeholders in a nice deck.

Venngage has a free plan, but you can also pay as little as $10 per month to get started.


If you’re in the customer service world, ZingTree is the decision tree software for you. This tool’s primary use is for creating scripts for call center agents and self-service chatbots in a decision tree type format. It differs from diagramming tools like Venngage and Lucidchart in that it is light on visual appeal and more about providing AI-powered prompts to users in customer support.

Zingtree decision trees are known to reduce call center onboarding time by up to 85% and automate up to 50% of ticket volumes. Those are pretty convincing numbers

A 30-day trial is free and monthly plans start at $50 per user per year.


Scikit-learn is a popular open-source analytical decision tree software, which is very different from the diagramming tools listed above. Analytical decision tree tools are like smart detectives. They sift through data and find connections and patterns by asking a series of questions, each one helping to narrow down the information to find more specific patterns.

Decision trees in this type of software can help you make educated guesses or predictions about new data by following the same set of questions. Financial executives use Scikit-learn to build models to predict stock prices or identify potential fraud. Ecomm businesses use it to build recommendation systems that suggest products to customers based on their browsing and purchasing patterns.

If it sounds cool and like you need a couple of data analysis or programming courses under your belt to operate it, that’s because it is, and you do.

If you are new to programming or data science, there can be a learning curve. You’ll need some knowledge of Python programming and data analysis to get started. It’s also not a graphical tool, so you’ll need to engage a tool like Venngage or Lucidchart to make your decision trees pretty if you plan to present them to a wider audience.

Ready to end analysis paralysis and try making your own decision tree? Sign up to try PixieBrix today.