Suggestion Chips

What are Suggestion Chips?

Suggestion chips are pre-defined option buttons displayed in chatbots and conversational interfaces, enabling users to respond rapidly without typing. Instead of entering text, users tap chips to respond quickly. Chips appear only for relevant questions and responses, then disappear after selection, keeping the conversational interface clean and focused.

In short: Suggestion chips are “shortcuts to answers.” They enable users to respond without lengthy typing—just tap a button to reply.

Key Points:

• What it does: Accelerates chatbot conversation flow and enables users to rapidly select optimal response options
• Why it’s needed: Reduces text input effort, lowers errors, and dramatically improves conversation completion rates
• Who uses it: Chatbot developers, customer service companies, conversational AI implementers

Measurement Approach

Suggestion chip effectiveness is measured through several metrics. Chip selection rate = (Number of times chip was tapped ÷ Total times chip was displayed) × 100. For example, if “Yes” and “No” chips display 100 times and receive 80 taps, the selection rate is 80%. Conversation completion rate improvement = (Pre-implementation completion rate - Post-implementation completion rate). If completion rates improved from 5% to 8% after chip implementation, that’s a 3-percentage-point improvement.

Generally, conversations with displayed chips achieve 70-85% completion rates compared to 40-50% without chips—a substantial improvement. The percentage of users selecting free text input (ignoring chips) is also important, with 5-15% being typical. Higher percentages indicate unclear chip labels or insufficient options.

Benchmarks and Standards

Industry averages show chatbots using suggestion chips achieve the following performance. For email and chat systems, first-contact resolution rates exceed 60%, improving significantly from pre-implementation 35-45%. User satisfaction scores (CSAT) reach 4.2/5.0 or higher, compared to 3.5/5.0 for text-input-only interfaces. Agent transfer rates drop to 15-20% or below, down from 35-40% before chip implementation. These metrics demonstrate that suggestion chips are not merely UI components but tools simultaneously enhancing customer experience and business efficiency.

Implementation Best Practices

Using suggestion chips effectively requires prioritizing user experience. Limit chips to 3-5 options, never exceeding 8. Excessive choices overwhelm users and decrease completion rates. Keep label text concise (ideally under 20 characters) so users instantly understand meaning. Clear, unambiguous expressions like “Yes,” “No,” and “Tell me more” work effectively.

Chip placement matters—display them immediately after bot responses, making them visually prominent. Position chips where they’re immediately obvious. Additionally, handle cases where users ignore chips and enter free text; always process that input correctly and continue the conversation. This demonstrates that chips are recommended but not mandatory. Regularly review analytics dashboards to identify underperforming chips and consider improvements or removal. When users ignore suggested chips, monitor those patterns to understand whether labels need clarification or options need expansion.

Advanced Features

Dynamic chip generation creates chips in real-time based on user history and context. If users previously asked about one issue, display chips for related problems to address potential needs. Personalization optimizes chip text based on language settings and past behavior, presenting basic options to new users and advanced options to returning customers.

Localization provides multi-language chip support, maximizing global chatbot effectiveness. Intelligent routing automatically directs users to appropriate agents or knowledge base articles based on their chip selections. These advanced implementations evolve suggestion chips from simple UI components into systems intelligently guiding conversations and supporting users.

Related Terms

• Chatbot — The conversational AI system foundation leveraging suggestion chips
• Conversational UI — The important UI element where suggestion chips are implemented
• Natural Language Processing — Technology for understanding user intent and presenting appropriate chips
• User Experience (UX) — The core concept underlying suggestion chip design
• Dialogflow — Google’s platform providing native suggestion chip support

Frequently Asked Questions

Q: How should we handle users who ignore chips and enter free text? A: Always validate and handle text input correctly. Despite displayed chips, users have the right to respond differently. Properly processing that input and ensuring seamless flow is critical.

Q: Why is limiting chip numbers important? A: Excessive choices create decision paralysis, causing users to spend too long deciding or abandon chips for text input. Research shows 3-5 chips represent the optimal balance.

Q: Do all chatbots need suggestion chips? A: They effectively handle simple conversation flows (yes/no questions and simple choices) but may not suit complex inquiries or open-ended questions. Adoption should be carefully considered based on use cases.