Decision Tree Examples — Templates & Inspiration
Get started quickly with these ready-made decision tree examples. Each one demonstrates a different use case and can be loaded directly into the Build / Edit page.
These examples are drawn from the DrawDecisionTree public directory — the internet's indexed collection of actionable decision trees published by practitioners. Browse the full directory to find trees for your specific industry or use case, or publish your own to contribute to the collection.
1. Data Warehouse Tier Selector
Use case: Classify incoming data into the correct warehouse tier (Copper, Bronze, Silver, Gold) based on its characteristics, integration method, and quality requirements.
Mode: Elimination — answers progressively narrow down which tiers are applicable.
This is the default example loaded in the editor. It demonstrates the power of elimination mode for classification use cases. Rather than routing users down a single fixed path, each answer removes tiers that no longer qualify — mimicking exactly how a data engineer would apply classification criteria mentally.
The example covers questions about data type, source system, integration method, refresh frequency, and downstream consumption. With 8+ questions and 4 possible outcomes, it shows how a complex classification decision can be structured clearly without overwhelming the person making it.
Data engineering teams use this pattern to enforce consistent tier assignment across assets, ensuring that data quality standards are applied uniformly whether the classification is done by a senior engineer or a new team member.
→ Try this example in the editor
2. Incident Severity Classification
Use case: When an incident occurs, classify its severity level immediately to trigger the correct response procedure and escalation path.
Questions covered: Is the system fully down or degraded? How many users are affected? Is there a known workaround? What is the business impact?
Speed matters in incident response. A decision tree removes ambiguity — instead of an on-call engineer making a judgment call under stress, they follow a structured sequence of questions that always produces a consistent severity classification.
The tree maps four severity levels (SEV1 through SEV4) to specific response protocols: who to page, how quickly to respond, and what communication to send. This ensures that a SEV1 critical outage triggers an immediate all-hands response every time — not just when the right person is on call.
Support teams and site reliability engineers use this pattern to reduce mean time to resolution (MTTR) by eliminating the classification step as a source of delay and variation.
This tree is also available via the Decision Tree API for programmatic consumption in incident management pipelines and AI-powered on-call tools.
→ Load this example in the editor
3. Onboarding Role Assignment
Use case: Determine the correct role, access level, onboarding track, and equipment provisioning for a new employee based on their department, seniority, and work location.
Questions covered: What department are you joining? What is your seniority level? Are you remote or office-based? Do you need system admin access?
HR onboarding is full of conditional logic: a remote senior engineer in the platform team needs different access than a junior office-based marketer. That logic currently lives in tribal knowledge, email threads, and onboarding checklists that nobody keeps current.
A decision tree encodes this logic explicitly. The IT provisioning team, the HR coordinator, and the new employee themselves can all run through the same wizard and reach the same outcome — the correct laptop configuration, software licences, access groups, and onboarding schedule for that specific person.
→ Load this example in the editor
4. Product Fit Assessment
Use case: Guide a potential customer through a series of questions to recommend the right product tier or plan for their needs.
Questions covered: What is your team size? Which features are most important? What is your monthly budget? Do you need enterprise features like SSO and audit logs?
A product fit assessment embedded on a pricing page removes one of the most common conversion blockers: confusion about which plan to choose. Instead of comparing a feature matrix, visitors answer questions about their situation and receive a specific recommendation.
This example demonstrates how a decision tree can replace a generic "contact sales" CTA with a self-serve qualification flow that converts at a higher rate because it gives prospects a concrete next step tailored to their situation.
The tree uses routing mode with 5 questions leading to 3 plan recommendations, each with a description of why it fits and a direct link to sign up.
→ Load this example in the editor
5. Troubleshooting Guide
Use case: Walk a user or support agent through diagnostic steps to resolve a technical issue without requiring escalation to a specialist.
Questions covered: What type of problem are you experiencing? When did the issue start? Have you tried restarting the device? What error message are you seeing?
This example demonstrates how to transform a flat troubleshooting document into a guided diagnostic flow. Instead of asking users to read through sections that may not apply to them, the wizard asks targeted questions and shows only the relevant diagnostic steps.
The tree branches at the top level by problem category (connectivity, performance, error messages, hardware), then narrows within each category until it reaches a specific resolution action. Each outcome includes a description of the fix and a link to detailed instructions.
Support teams using this pattern report significantly lower repeat contact rates — users arrive at the correct resolution on the first attempt rather than trying multiple fixes from a generic FAQ.
→ Load this example in the editor
Discover More in the Directory
These five examples represent a fraction of the trees available in the DrawDecisionTree public directory. The directory includes trees across engineering, HR, finance, legal, sales, data, marketing, and operations — all free to use, embed, and consume via the Decision Tree API.
→ Browse the full directory by category
→ Browse engineering and IT operations trees
→ Publish your own decision tree
Decision Tree Tools Comparison
DrawDecisionTree vs Lucidchart
The decision tree generator vs Lucidchart question comes down to what you need the examples above to do for your team.
Lucidchart lets you draw decision tree diagrams that look like the examples above as static visuals — polished for presentations and documentation. But none of them can be run by an end user. A support agent cannot follow a Lucidchart troubleshooting tree interactively; they read it and navigate it themselves.
DrawDecisionTree's examples are live tools. A new employee can run the onboarding wizard and get their specific provisioning checklist. A support agent can follow the troubleshooting guide step by step without reading an entire article. An on-call engineer can classify incident severity in seconds without judgment calls.
For teams building tools that users run — not documents users read — DrawDecisionTree is purpose-built for this purpose. No account required to get started.
DrawDecisionTree vs Draw.io
The decision tree maker vs Draw.io comparison is similar. Draw.io produces excellent static diagrams in a free, open-source tool. For sharing visual representations of decision logic in wikis, Confluence pages, or design documents, it works well.
For embedding interactive decision tools on websites, portals, and applications — and for building those tools from plain text in minutes rather than hours — DrawDecisionTree provides capabilities that Draw.io does not.
Industry-Specific Decision Tree Examples
Healthcare: Patient Intake Triage
A decision tree guides intake staff through patient symptom questions to determine triage priority: high-acuity (immediate treatment), medium-acuity (urgent but stable), or low-acuity (standard queue). Questions cover onset, pain level, vital signs indicators, and existing conditions. Consistent triage reduces both under- and over-prioritisation.
Customer Service: Return and Refund Routing
A decision tree determines the correct resolution path for customer return requests: full refund, exchange, store credit, or denial. Questions cover purchase date, product condition, original payment method, and reason for return. This eliminates inconsistency across agents and reduces escalation volume.
Project Management: Risk Classification
A decision tree classifies project risks by severity and required mitigation action. Questions cover probability of occurrence, potential business impact, detection difficulty, and mitigation cost. Outputs include risk tier (critical, high, medium, low) and the appropriate response playbook.
Financial Planning: Investment Suitability
A decision tree assesses investor suitability for different investment products. Questions cover investment horizon, risk tolerance, liquidity needs, and prior investment experience. Outputs recommend appropriate asset allocations — conservative, balanced, or growth-oriented — with an explanation of the rationale.
Marketing: Campaign Channel Selection
A decision tree guides marketers to the most appropriate channel for a specific campaign. Questions cover target audience demographics, budget size, campaign objective (awareness vs conversion), and content format availability. Outputs include channel recommendation (paid search, social, email, content) with rationale.
Frequently Asked Questions
What is a decision tree template? A decision tree template is a pre-built DSL file with example questions, answers, and outcomes for a common use case. You can load it into the editor and customise it for your specific needs.
Can I create my own decision tree from scratch? Yes. Open the editor, clear the default content, and start typing your questions and outcomes in the DSL format. The DSL Reference covers all available syntax.
How many questions can a decision tree have? There is no hard limit. Practical decision trees typically have 5–15 questions. Trees with many more questions often benefit from being split into multiple smaller trees.
Can the same outcome be reached by multiple paths? Yes. Multiple answer combinations can lead to the same outcome — this is common in troubleshooting trees where different symptoms converge on the same resolution.
What is the difference between routing mode and elimination mode? In routing mode, each answer directs the user to a specific next question (one path at a time). In elimination mode, each answer removes outcomes that no longer apply, narrowing the field until one remains. Use routing for step-by-step diagnosis; use elimination for recommendation and classification.
Can I embed decision trees built from these examples? Yes. Once you've customised an example, click the Embed button to generate an iframe code. Paste it into any website, wiki, or application.
How is DrawDecisionTree different from Lucidchart or Draw.io for decision trees? DrawDecisionTree produces interactive wizards that users can run; Lucidchart and Draw.io produce static diagrams that users read. DrawDecisionTree also uses a plain text source format, supports elimination mode, generates three output views automatically, and publishes trees to a searchable directory with an API for programmatic consumption.
Is DrawDecisionTree free? Yes. Building, testing, and sharing decision trees is free. No account is required for basic use. See pricing for details on advanced features.
Creating Your Own Decision Tree
These examples are just starting points. To create your own decision tree:
- Go to the Build / Edit page
- Clear the editor and start typing your DSL
- Use the DSL Reference for syntax help
- Test with the Run Wizard and validate with Path View
→ Read the full guide on building decision trees