Decision tree examples and use cases

A decision tree is a visual representation of a business process organized in a tree-like structure, enabling users to navigate through step-by-step instructions for executing the process. A decision tree maker is a tool that facilitates the creation and implementation of such decision trees. Examples range from including cold calling scripts directly into decision tree nodes to incorporating troubleshooting steps, aiding users in pinpointing issues starting from symptoms to identifying the root problem and its solution. Call center automation often utilizes decision trees to streamline customer support processes and enhance efficiency. This article describes examples of decision trees used for technical support and lead generation across various industries.

Decision tree examples & applications in technical support

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The most important use case for decision trees here is for use in troubleshooting. A decision tree is used to probe customers with a sequence of questions that start from the symptom to get to the underlying root cause. Such interactive decision trees are used for call center automation or for customer self-service. Each step in the decision tree has a question with multiple-choice answers. Each answer leads to a subsequent question with its multiple-choice answers, and so on until the user reaches the underlying root cause and the corresponding solution.

Decision trees are used for troubleshooting in multiple industry verticals – e.g.

Troubleshooting Manufactured Goods using Decision Trees

Products ranging from Televisions, IOT (Internet of Things) devices, consumer electronics products like Smartphones, computers, cameras, modems, and routers to heavy engineering equipment used in manufacturing, construction, agriculture, or mining all break down at times and need troubleshooting.

Decision trees are used as a preferred method of harnessing troubleshooting knowledge and helping technical support engineers, technicians, distributors, customer service representatives or end customers utilize this knowledge to address issues.

A simple decision tree for troubleshooting TV issues can resemble the example below.

Troubleshooting in Telecom Industry using Decision Trees

Telecom vendors offer a service that involves myriad technologies, including radio, telephone, television, satellite, microwave, data communication, computer networking, etc.

Although an end user may occasionally experience issues with the service, thousands of customer service agents rely on decision trees to troubleshoot customer problems.

A simple example of a decision tree to help troubleshoot modem issues is shown below.

Troubleshooting in Retail using Decision Trees

Customers return consumer electronics devices such as smartphones, computers, cameras, modems, and routers, as well as IoT (Internet of Things) devices such as vacuum cleaners, doorbells, smart locks, smart appliances, and health monitors in large numbers every day.

To prevent such returns, customer service representatives use decision trees to educate customers about initial setup issues. This saves the retailer millions of dollars in terms of shipping and handling costs, re-testing the returned item, restocking the item, and reselling it at a discount as a refurbished item.

An example of a decision tree that helps prevent returns of a smart speaker purchase is shown below. Preventing such product returns by using decision trees also helps increase customer satisfaction (CSAT).

Troubleshooting in Information Technology using Decision Trees

Complex machines, electronics, computers, and software systems are handled by IT organizations. Due to the vastly varied nature of issues they face, IT personnel in roles such as desktop support, ServiceDesk support, network architects, systems analysts, network administrators, systems administrators, etc., find it almost impossible to have the breadth and depth of knowledge required to troubleshoot issues given their heavy workload and tight deadlines.

To address this, decision trees are used as a preferred method of capturing, sharing, and improving tribal knowledge within IT organizations (and Managed Service Organizations, also known as MSOs) for troubleshooting customer issues, whether it’s a slow computer, an employee not receiving emails, or the company network facing a meltdown!

Troubleshooting Engineering Equipment using Decision Trees

The Engineering goods sector encompasses a variety of industries such as industrial machinery, electronics, other electrical equipment, transportation equipment, and instrumentation. Data centers, hospitals, laboratories, public safety centers, agriculture, and military installations all depend on this equipment to function continuously.

Technicians use interactive decision trees to troubleshoot and identify the root cause of equipment failures in order to restore its functionality. This process saves millions of dollars in expensive equipment replacement costs and prevents unnecessary downtime that could disrupt critical functions such as railway and aircraft operations, various control systems, electric power systems, navigational aids, water filtration, mining, manufacturing, and radio communications.

Troubleshooting in Science & Technology using Decision Trees

A variety of industries fall under the umbrella of science and technology, such as biotechnology, life sciences, data centers, SaaS organizations, and animal and plant science.

Around the world, there are approximately 30,000 SaaS companies with a collective customer base of around 14 billion users. When customers encounter a problem, they contact the technical support department of the respective company.

Technical support personnel at these companies utilize interactive decision trees to troubleshoot issues faced by customers. The use of decision trees within science and technology organizations enhances issue resolution efficiency, allowing customers to quickly return to normal operation.

An example of a decision tree that helps troubleshoot a SaaS application is shown below.

Decision tree examples & applications in lead qualification.

Decision trees are used for lead generation in multiple industries. A decision tree helps call center agents through a sequence of questions they ask customers to qualify a business lead.

Lead generation is a crucial process for businesses, whether they offer insurance services, mortgage services, education services, or specialized healthcare services. This process involves identifying and cultivating potential customers through marketing efforts to help the business succeed.

Call center agents utilize cold calling scripts embedded in interactive decision trees to qualify and score leads by asking customers a sequence of questions where each subsequent question depends on the answer to the previous question. Each answer is assigned a score, and the decision tree captures a path score as the agent navigates through the tree based on the customer’s responses.

Leads with higher scores are considered more qualified for further follow-up and are passed on to the next stage of the sales cycle. Thus, interactive decision trees play a critical role in the lead qualification and scoring process.

A general-purpose decision tree example for lead qualification in any industry is shown below. Decision trees are used for lead generation, qualification, and scoring, in multiple industry verticals – e.g.

Lead Generation for Insurance Products using Decision Trees

Insurance companies use decision trees to systematically evaluate and classify potential customers for health insurance based on their demographic and health-related information. Factors such as age, gender, income, occupation, and smoking history are taken into account when determining the appropriate insurance plan and carrier for each customer.

As a result, decision trees enable insurance companies to quickly and accurately evaluate leads, allowing them to make informed decisions about which products and services are best suited to each customer’s needs.

A decision tree example for lead generation in the insurance space is shown below.

Lead Generation for Mortgage Industry using Decision Trees

Lenders use decision trees to assess the creditworthiness of potential borrowers for credit scoring, loan underwriting, and risk management purposes.

Underwriters use decision trees to evaluate a borrower’s credit score, income, employment history, loan-to-value ratio, debt-to-income ratio, and other factors to determine whether to approve or deny a loan.

Decision trees are a valuable tool for identifying potential risks associated with mortgage lending and separating qualified leads from non-qualified ones.

A decision tree example for lead generation in the Mortgage space is shown below.

Lead Generation for Higher Education using Decision Trees

Education institutions use decision trees for lead generation to identify potential students who are most likely to enroll in their programs.

By streamlining their marketing efforts and focusing on leads that are most likely to enroll, institutions can use decision trees to make more efficient use of their resources and increase their conversion rates.

Ultimately, this results in more students enrolling in their programs and increased revenue for the institution.

A decision tree example for lead generation in the education industry is shown below.

Lead Generation for Home Improvement using Decision Trees

Home improvement companies use decision trees extensively for lead generation to identify potential customers who are most likely to purchase their products or services.

By using decision trees, companies can prioritize their marketing efforts and resources on high-priority leads who are most likely to convert into customers. This allows companies to make more efficient use of their resources, focusing on leads that are most likely to purchase their products or services while deprioritizing low-priority leads.

The global home improvement industry, which includes products and services related to home renovation, remodeling, repairs, and maintenance, as well as home improvement products such as appliances, furniture, and home decor, is estimated to be worth around $1 trillion.

A decision tree example for lead generation for the Home Improvement industry is shown below.

Lead Generation for Real Estate Industry using Decision Trees

Real estate professionals use decision trees for lead generation to identify and prioritize potential leads based on specific criteria.

Using an interactive decision tree, real estate professionals can identify potential leads who are most likely to be interested in buying or selling a home in a particular area. This is based on various factors such as income, age, location, financial condition, and other relevant data points.

By using decision trees for lead generation in the real estate industry, professionals can focus their efforts and resources on high-priority leads, increasing the likelihood of closing deals and generating revenue.

A decision tree example for lead generation in the real estate industry is shown below.

Lead Generation for Automotive Insurance using Decision Trees

Sales agents use decision trees to qualify leads for automotive insurance by considering various factors that may affect the qualification criteria. Although qualifying for automotive insurance may seem simple, there are several exceptions that need to be considered by sales agents.

Sales agents use a simple decision tree, similar to the example shown below, to qualify leads for automotive insurance based on factors such as age, driving history, type of vehicle, and coverage needs.

A decision tree example for lead generation in the automotive insurance industry is shown below.

Lead Generation for Plaintiff Qualification in Mass Tort Law

Decision trees are used in qualifying plaintiffs for mass tort cases, which involve a large number of individuals harmed by a common product or event. Legal experts establish eligibility criteria based on various factors, which can include injury type, exposure duration, and relevant legal precedents. A Mass Tort Lawyer then creates interactive decision trees to express this criteria. E.g. Did the plaintiff live in one of the qualified states? The answer Yes leads to the next qualifying question, but No leads immediately to disqualification.

Data Analysis: Each plaintiff’s data is analyzed, and the decision tree is traversed based on the relevant criteria. This process helps identify whether an individual qualifies for the mass tort.

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