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Useful website chatbots do more than greet visitors: they can answer repeat questions, help people find products or information, and pass an unresolved conversation to a person. These five examples show different ways to apply that idea. Most are vendor-published customer stories, so their reported results are examples—not independent benchmarks or forecasts for another business.
Five chatbot examples at a glance
| Example | Visitor or team task | Information used or response pattern | Handoff or reported result |
|---|---|---|---|
| RateMyAgent with Intercom | Bring marketing and support conversations together | Consolidated customer messaging and support | Intercom reports an 80% reduction in median response time compared with email; the page excerpt does not state the methodology or year. |
| Best Egg with Zendesk | Automate recurring chat inquiries | Zendesk AI for chat inquiry automation | Zendesk says 80% of chat inquiries were automated; its directory excerpt does not state the measurement period or methodology. |
| Zendesk’s after-hours chatbot use case | Give visitors a first response outside staffed hours | Answers to common support questions | Zendesk describes availability and fast responses as use cases; it does not provide a named customer outcome for this pattern on the cited page. |
| Salesforce’s Agentforce website agent | Help website visitors find products, content, and pricing information | Salesforce says the agent searches more than 2,000 pages and more than 400 product and customer catalog records | A visitor can request a person, with the conversation and summary transferred to a sales development representative. Salesforce reports 150 rep hours saved per month in this deployment. |
| Intercom Copilot for support agents | Help human agents find answers in support documentation | Internal assistant drawing on support documentation | Intercom presents a customer statement about finding answers faster. This is an agent-assistance example, not a customer-facing website chatbot. |
The figures in this table measure different things and come from vendor-published pages. They should not be compared as if they used a shared definition or independent evaluation.
1. RateMyAgent: bring support and marketing conversations together
Intercom’s RateMyAgent customer story describes consolidating marketing and support messaging to provide faster resolutions. The pattern is useful when customer conversations are split across teams or channels and staff need a more unified way to respond. Consolidation is the design idea; the story does not specify a particular website-chat flow or list the channels included.
Intercom reports an 80% reduction in median response time compared with email. That is the vendor’s customer-story claim: the page excerpt does not give a publication year or explain how the figure was measured. It is a case-specific result, not a forecast for a company adopting the same software.
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For a site considering this pattern, map where a visitor’s question goes after it arrives: which team owns it, what context that team needs, and how the conversation reaches the right person if it cannot be resolved at first contact. Measure response time using a consistent definition and comparison period; otherwise, a before-and-after percentage may not describe the same work.
2. Best Egg: automate recurring chat inquiries
Zendesk’s customer directory names Best Egg as an example of using Zendesk AI to automate chat inquiries. The underlying support pattern is to identify questions that recur often enough for an automated response to be useful, rather than treating every conversation as a candidate for full automation.
Zendesk says Best Egg automates 80% of chat inquiries. The directory excerpt does not explain the measurement period, what counted as automated, or the methodology, so the percentage should be read only as Zendesk’s vendor-presented customer-story claim.
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A sensible implementation starts by separating routine requests with clear, stable answers from cases that require judgment, account-specific investigation, or empathy. A bot should make its limits legible and give visitors a route to a human when the available answer does not solve the problem. Track whether conversations are actually resolved, not just whether the bot sent a reply; those are different outcomes.
3. Zendesk’s after-hours use case: provide a first response when staff are offline
Zendesk describes customer-service chatbots as a way to provide availability beyond staffed hours and respond quickly. This is product guidance about a use case, not a measured outcome for a named customer. The value of an after-hours bot depends on what it can answer accurately and what options it offers when the question needs a person.
For example, an owner can define which common questions have approved answers, tell visitors when a live agent is unavailable, and collect enough context for a later response. The bot should not imply that a person is present if no one is monitoring the conversation. Zendesk’s page was last updated July 21, 2026: Zendesk’s customer-service chatbot guidance.
Rank #3
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Useful checks for this pattern include whether answers remain current, whether the bot can distinguish an unanswered question from a resolved one, and whether a visitor knows what will happen next. Availability alone is not the same as resolution.
4. Salesforce: help visitors navigate content and product information, then hand off
Salesforce’s Agentforce website-agent case study describes an agent that searches more than 2,000 pages in Salesforce’s content system and more than 400 product and customer catalog records. Salesforce presents this as a way to answer questions about its website content, products, and pricing information. This example is especially relevant to sites where visitors need to explore a large body of information before they know what to ask a salesperson.
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Rank #4
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The pattern depends on the agent having access to relevant, maintained content and records. When adapting it, decide which sources the bot may use, how changes to product or pricing information are reflected, and what context should travel with an escalation. Salesforce’s account is at Agentforce is reinventing how visitors navigate Salesforce’s websites.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Intercom Copilot: make support knowledge easier for agents to use
Intercom’s customer stories also describe Copilot, an assistant for support agents that draws on support documentation. This is not a visitor-facing chatbot example: it shows a related way to apply conversational AI, by helping the human support team find information while serving customers.
Intercom includes a statement from Abigail Whitwham, Customer Success Manager, who says the team used Copilot internally as a quick way to find answers without spending time searching. The statement is a customer quotation presented by Intercom, not an independent assessment. The story points to a practical distinction: a support system can assist the visitor directly, assist the agent, or do both, but those uses should be evaluated separately.
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For an agent-assistance pattern, useful questions include whether the assistant points staff to relevant support documentation, whether agents can judge the answer before sending it, and how the team handles gaps or outdated material. The customer stories are available from Intercom.
How to choose a pattern for your site
Start with a visitor task, not a vendor feature list. A chatbot intended to answer recurring support questions needs different content and escalation rules from one intended to guide product discovery. An internal agent assistant has a different user and success measure again.
- Name the task. Decide whether the first goal is answering a recurring question, responding outside staffed hours, finding products or site information, routing a conversation, or helping an agent locate an answer.
- Identify the answer source. Specify whether the response should come from approved support documentation, website pages, or product and customer records. Keep the source relevant to the question the bot is expected to handle.
- Set the boundary. Define what the bot should do when it lacks a reliable answer or the visitor needs a person. For handoffs, determine what conversation context should accompany the transfer.
- Choose a metric that matches the task. Response time, inquiry automation, resolution, and staff time saved describe different outcomes. Define the measure before launch and use the same definition when comparing periods.
- Review actual conversations. Look for unanswered questions, incorrect or stale responses, failed handoffs, and cases where a visitor needed more than an automated reply. Use those patterns to revise the bot’s content and boundaries.
The examples illustrate possibilities, not a ranking of chatbot vendors. Their outcome figures come from vendor-published customer stories or product guidance, and the cited pages do not provide a shared methodology for comparing performance.
Frequently Asked Questions
Are all five examples customer-facing website chatbots?
No. Intercom’s Copilot example is an assistant for support agents. Zendesk’s after-hours example is product guidance about a chatbot use case rather than a named customer case study.
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No. Intercom’s RateMyAgent story reports a reduction in median response time compared with email, while Zendesk’s Best Egg listing reports the share of chat inquiries automated. The cited excerpts do not establish a common methodology.
Do these case studies show what results my business should expect?
No. The figures are vendor-reported outcomes for specific examples, and the cited pages do not provide independent evaluations or a shared basis for forecasting results elsewhere.
Quick Recap
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