Customer service chatbots: How they improve support at scale

• Customer service chatbots use automated conversation to answer questions, escalate issues to agents, and collect information from customers

• There are several types of chatbots, including rule-based chatbots, AI-powered chatbots, and virtual agents

• Chatbots are great for simple questions and requests, but a human should handle complex issues and high-risk changes

• Effective chatbots rely on trusted knowledge, clear conversation paths, escalation rules, and ongoing performance reviews

Customer service chatbots help customers get quick answers, complete simple requests, and find the right support path without waiting for an agent every time. For growing teams, chatbots can handle repetitive questions while leaving more complex issues to people.

When connected to trusted knowledge and clear escalation paths, chatbots can improve response times and streamline service request management. The right approach combines automation with human support so customers get the help they need at the right time.

What is a customer service chatbot?

A customer service chatbot is software that uses automated conversation to answer customer questions, collect information, route requests, or escalate issues to a human agent.

Chatbots can appear in help centers, websites, customer portals, mobile apps, messaging tools, and collaboration systems.

While many chatbots use AI to process and respond to user messages, not all chatbots are AI-powered:

• Some follow predefined rules or decision trees

• Some use AI only to understand intent and generate answers

• Some combine structured flows with AI-powered knowledge retrieval

Chatbots are also different from virtual agents, which can go beyond answering questions to complete support tasks and guide customers through service workflows. 

How customer service chatbots work

Customer service chatbots follow a simple workflow to provide customer support. Here’s a breakdown:

• A customer asks a question

• The chatbot identifies intent

• The chatbot searches for an answer or starts a flow

• The chatbot resolves or routes the request

• The support team reviews performance

Atlassian Customer Service Management supports this kind of experience with an AI customer service agent that can answer questions, surface relevant information, and escalate requests when human support is needed. 

Types of customer service chatbots

There are several types of chatbots that you can integrate into your customer service management, from complex virtual agents to simple rule-based chatbots.

Rule-based chatbots

Rule-based chatbots use predefined paths, buttons, and decision trees to support customers. These chatbots are best for simple, predictable requests.

For example, a rule-based chatbot might recognize keywords in a customer’s question and direct them to a relevant FAQ or predefined response. 

AI-powered chatbots

AI-powered chatbots use natural language processing to understand customer questions. They are best for questions with varied wording, but answer quality depends on the underlying sources.

Hybrid chatbots

Hybrid chatbots combine structured flows with AI answers, such as using AI for FAQs and structured flows for requests that require data collection or other specific steps.

Hybrid chatbots are best for support teams that need flexibility. 

Virtual agents

Virtual agents can go beyond simple questions by guiding customers through workflows, collecting data, creating tickets, or handing off with context.

Virtual agents can handle access requests, status updates, troubleshooting, and policy questions.

Benefits of customer service chatbots

The value of a chatbot depends on the quality of its knowledge, workflows, and escalation process. Connecting it to the right support channels helps teams get more from automation without sacrificing the customer experience.

When to use a chatbot vs. a human agent

The best support experiences use chatbots for repeatable tasks and human agents for issues that require judgment, investigation, or empathy. 

How to build a better customer service chatbot

Building an effective customer service chatbot starts with choosing the right requests to automate and giving the chatbot reliable information to work with. Follow this step-by-step guide to get started.

Step 1: Identify repetitive support questions

Teams should start by reviewing ticket history, chat transcripts, help center searches, and agent feedback to identify the most repetitive support questions. These are often good candidates for automation. 

Some good starting points include:

• Password resets

• Account access

• Order status

• Billing FAQs

• Product setup

• Policy questions

• Common troubleshooting steps

You can also build a knowledge base to answer frequently asked questions. A knowledge base gives customers, agents, and chatbots a centralized source for answers to common questions. 

Step 2: Group questions by intent

Next, take the list of common questions you collected and group them by intent. Intent is what the customer is trying to accomplish by contacting your support team.

For example, a customer who needs account access or a password reset is trying to log in. A customer looking for troubleshooting steps wants to solve an issue with the software.

Understanding intent helps the chatbot connect each request to the right answer, workflow, or escalation path. 

Step 3: Connect the chatbot to trusted knowledge

Customer service chatbots are only as effective as the knowledge sources they can pull from. Connect the chatbot to a trusted knowledge base so each answer is approved, current, and easy to understand.

Weak documentation leads to weak chatbot answers. Audit outdated, duplicate, or conflicting articles before launch.

Setting up a self-service knowledge base allows customers to find answers on their own, limiting support request volume.

Step 4: Design clear conversation paths

Keep conversation paths focused on the information needed to answer the question, complete the request, or route the issue. For teams using conversational ticketing, the same principle applies: collect enough context to move the request forward without making the interaction unnecessarily long. 

Here are some guidelines for designing clear conversation paths:

• Use plain language

• Avoid long menus

• Confirm the customer’s issue before taking action

• Make next steps clear

• Offer a way to reach a person

Step 5: Define escalation rules

Create clear rules to define when the chatbot should hand the conversation off to a human agent or route the request to the help desk. Align escalation rules with your service level agreements (SLAs) so urgent or high-priority requests reach the right team within the required response time. 

Some of the common triggers you can use include:

• Low-confidence answer

• Negative sentiment

• Repeat failed attempts

• High-priority issue

• Sensitive account or billing issue

• Customer asks for a person

Step 6: Measure and improve

After launch, review unanswered questions, deflection rate, resolution rate, customer satisfaction score (CSAT), handoff quality, and knowledge gaps. Use those insights to improve answers, update content, and refine escalation rules over time. 

Customer service chatbot best practices

• Start with high-volume, low-risk requests: Focus on common, easy-to-resolve requests to maintain customer satisfaction.

• Keep answers short and useful: Include only the information customers need to understand the answer and take the next step.

• Make escalation easy: Give customers a clear path to a human agent when they need one.

• Use approved knowledge sources: Only pull answers from approved knowledge sources to ensure answers are accurate.

• Review failed conversations regularly: Look for unanswered questions, deflections, unnecessary escalations, and knowledge gaps.

• Be clear that the customer is talking to a bot: Clearly identify the chatbot so customers know when they’re interacting with an automated system. 

• Protect sensitive information: Limit the collection and sharing of sensitive data and use secure, approved workflows when verification is required. 

Scale customer support with connected AI 

Customer service chatbots work best when they have access to trusted knowledge, clear workflows, and an easy path to human support. Automating repetitive questions and requests can give agents more time to focus on complex issues while helping customers get answers faster.

Atlassian Customer Service Management connects customer support with product, development, and operations teams through shared context and workflows. Its AI customer service agent can field queries, surface helpful information, and redirect complex requests with context so customers don’t have to start over.