Conversational UI design is the practice of creating digital interfaces that users interact with through natural language, via chat or voice, instead of traditional screens, menus, or forms. It powers experiences such as customer support chatbots, WhatsApp banking, in-app assistants, and AI copilots embedded within products.
From our experience across diverse digital transformation and product design projects at Onething Design, we’ve seen what makes conversational experiences succeed. In this article, we’re sharing 10 best practices for conversational UI design that will help you design conversations that are structured, scalable, and truly user-centric.
What is Conversational UI in UX design?
In UX design, conversational UI focuses on designing structured interactions that feel like a dialogue between a human and a system. The system is expected to understand what the user is trying to do, ask relevant follow-up questions, maintain context across messages, and guide the user toward an outcome.
A well-designed conversational interface behaves less like a tool and more like a service representative. Apart from responding to inputs, it helps users complete tasks.
For example, MakeMyTrip (MMT) heavily utilizes Conversational UI as a core part of its platform, particularly through its AI-driven initiatives. Its AI chatbot assistant named Myra handles end-to-end travel needs, including research, booking, and post-sales support for flights, hotels, and holidays.
Also Read: Chatbot Design - A UI UX Guide for Creating Human Bots
Conversational UI vs Chatbot UI vs Voice UI
Conversational UI, chatbot UI, and voice UI are often used interchangeably, but they refer to different things.
Conversational UI is the overall interaction approach that involves completing tasks through dialogue.
On the other hand, a chatbot is one implementation of conversational UI delivered through text, such as website chat or WhatsApp.
Voice UI (VUI) is another implementation where the conversation happens through speech, such as Alexa, car assistants, or IVR assistants.
In simple terms, conversational UI is the design philosophy, while chatbots and voice assistants are delivery channels. A product can include a chatbot but still not provide a good conversational experience if it behaves like a rigid form.
GUI vs Conversational Interface: When Each Works Better
Conversational UI does not replace graphical user interfaces (GUIs). Both are useful, but for different types of tasks.
Traditional GUI works better when users need visual understanding or comparison, such as browsing products, reviewing dashboards, or editing content. These situations require an overview and control.
Conversational interfaces work better when users have a clear goal and want to complete it quickly. They are especially useful for repetitive tasks, guided assistance, or situations where users may not know where to start. On mobile devices or messaging apps, conversation often reduces effort.
A helpful way to think about this is that graphical interfaces support exploration, while conversational interfaces support completion.
Is WhatsApp Automation Considered Conversational UX?
WhatsApp automation can be conversational UX, but only when it is designed as an actual conversation.
Many WhatsApp bots simply replicate IVR menus in text form by asking users to choose numbered options. This is automation, but not conversational design.
A true conversational experience understands natural text, remembers context, asks meaningful follow-up questions, and helps users complete a task with minimal effort. Specifically, for Indian users, WhatsApp functions as a service platform for banking, deliveries, and customer support. When designed properly, a WhatsApp interaction becomes a service conversation rather than a menu-driven system.
Real Examples of Conversational UI in Digital Products
Conversational interfaces already exist across everyday services, often without users recognizing them as a specific UX category. For example, Myntra offers a conversational UI and AI-powered conversational shopping features, primarily through their AI assistant, Maya. These tools allow users to interact with the platform using natural language to discover, browse, and purchase fashion items. And then there is Apollo 24|7 that utilizes WhatsApp conversational AI to enable users to book, reschedule, or cancel doctor consultations directly within the chat interface.
Enterprise software also uses conversational UI through IT helpdesk assistants, HR support tools, and onboarding assistants. In each of these cases, users aim to complete a specific action quickly. Conversational UI reduces navigation effort and speeds up completion.
When Should a Product Use Conversational UI Instead of Forms, Apps, or Dashboards?
Conversational UI works best when users have a clear goal and want to reach it quickly. Forms and dashboards, on the other hand, are often better for exploration, comparison, and complex control.
What Problems Does Conversational UI Solve Better Than Apps?
Conversational UI is particularly effective when users:
- Know what they want to do, but don’t know where to find it
- Want to complete a repetitive task quickly
- Need guidance through a process
- Are interacting on mobile or messaging platforms
- Prefer natural language over navigating multiple screens
For example, if a user wants to reorder a previously purchased product, typing “Reorder my last purchase” is faster than navigating through account history, order details, and checkout screens.
Conversational UI also reduces cognitive load. Instead of presenting multiple options at once, it asks one relevant question at a time. This is especially helpful for first-time or less digitally fluent users.
In service-led products, such as banking, telecom, and healthcare, users typically have specific tasks such as checking balances, booking appointments, or upgrading plans. These goal-driven actions are well-suited for conversational flows.
When Conversational UI Fails
Conversational UI often fails when it is implemented as a surface-level feature rather than a well-designed interaction model.
Often, common failure reasons include:
- Over-reliance on rigid decision trees that don’t understand natural language
- Poor intent recognition leading to repetitive clarification loops
- Lack of context memory
- No seamless escalation to human support
- Trying to handle complex workflows that require visual comparison
Many bots fail because they are built as cost-saving automation tools rather than user-centered solutions. When a system repeatedly says, “I didn’t understand that,” user trust declines rapidly.
Conversational UI also fails when the task is inherently visual or exploratory. For example, comparing insurance plans, analyzing financial dashboards, or designing creative layouts is better suited to graphical interfaces.
The success of conversational UI depends on the clarity of scope. Therefore, it must be designed for the right problems.
What are the Use Cases of Conversational UI?
Conversational UI performs particularly well in specific use cases:
- Onboarding is more effective when the system asks step-by-step questions instead of presenting long forms. Breaking the process into a conversation makes it feel guided rather than administrative.
- Customer support is yet another strong use case. Conversational UI can resolve common queries instantly and escalate complex issues to human agents when required.
- Payments and transactional flows benefit when the process is straightforward and secure. For example, paying a bill or recharging a plan can be simplified through conversational prompts.
- Product discovery works when the system narrows options based on user responses. Instead of browsing multiple filters, users can describe what they need.
- Lead generation also improves when users can express interest naturally. Asking “What are you looking for?” often feels more approachable than filling out a static inquiry form.
In each of these cases, conversation reduces blockages and guides users toward completion.
Also Read: Top 5 Tips to Ace the Chatbots UI with Real-life Examples
When Not to Use Conversational UI
Conversational UI should not be used when:
- Users need a visual overview or comparison
- Tasks require precision editing or customization
- The workflow involves multiple complex variables
- Speed depends on scanning large amounts of information
For example, financial dashboards, analytics tools, design platforms, and advanced configuration systems are better served by graphical interfaces.
Conversational UI should also be avoided if the backend systems are not mature enough to handle intent recognition reliably. A poorly functioning conversational system creates more frustration than value.
The most effective products do not replace traditional UI with conversational UI. Instead, they integrate both strategically by using conversation for goal-driven assistance and graphical interfaces for exploration and control.
The decision should always come back to one principle. That is, if a conversation makes the task simpler and faster for the user, it is worth designing. If it complicates clarity or removes necessary visibility, it is not.
Best Practices for Conversational Design
Conversational UI succeeds when it behaves like a helpful service interaction, you know. Many teams focus on building a bot, but the real work lies in designing how the system understands people, guides them, and helps them complete tasks with minimal effort.
The best practices below form a practical framework you can apply while designing, reviewing, or auditing a conversational experience.
1. Design Conversations & Not Merely Command Flows
A common mistake is treating a conversational interface like a form presented one question at a time. When the system only accepts specific phrases or rigid responses, users are forced to learn how to talk to the bot. That reverses the purpose of conversational design.
A real conversation allows flexibility. Users may start with incomplete information, change their mind mid-flow, or ask something unrelated. The system should handle natural phrasing and gently guide the user rather than forcing them through a fixed path.
Instead of asking users to follow instructions step-by-step, design the interaction around how people naturally request help. After all, the experience has to be shaped in a way that feels like interacting with a service representative.
2. Start With User Intent Mapping
Many teams begin by writing sample bot dialogues. This usually leads to brittle experiences because scripts assume a single predictable path.
The correct starting point is intent mapping that involves identifying what users are trying to accomplish. Users do not care about features… but yes, they care about outcomes. For example, “I lost my card” and “Block my card immediately” represent the same intent even though the wording differs.
Before writing any conversational copy, define:
- The primary user goals
- Possible variations in phrasing
- Required information to complete the task
- Backend actions that must occur
Once intents are clear, conversation flows become easier to design because the system is responding to goals, after all.
3. Keep Conversations Goal-Driven and Concise
Conversation in products should not mimic social chat. Users are not looking for long dialogues. They want efficient assistance.
Every message should move the user closer to task completion. Avoid unnecessary greetings, long explanations, or repeated confirmations. A conversational interface should ask only what is required to proceed.
For example, asking for five pieces of information in a single message overwhelms users, while asking irrelevant questions frustrates them. The best conversational flows ask one relevant question at a time and immediately use the response.
The design principle is simple – usability should always outweigh stylistic choices.
4. Design for Context Awareness and Memory
One of the biggest expectations from conversational systems is memory. Users assume the system remembers what they just said and understands the ongoing task.
If a user already provided an order number, the system should not ask for it again. If the user switches from tracking an order to cancelling it, the system should carry forward the same context.
Context awareness includes:
- Remembering previous answers
- Understanding follow-up questions
- Continuing unfinished tasks
Without memory, conversations feel mechanical. And with memory, they feel intelligent.
5. Build Strong Error Handling and Fallback Mechanisms
No conversational system understands everything. The difference between a frustrating bot and a helpful one lies in how it handles failure.
When the system cannot understand a message, it should not simply repeat “I didn’t get that.” Instead, it should guide recovery. This can include rephrasing the question, offering suggestions, or clarifying the expected input.
Effective fallback responses:
- Explain what went wrong
- Provide clear next steps
- Keep the conversation moving
A well-designed fallback preserves user confidence even when the system makes a mistake.
6. Make Escalation to Humans Seamless
Users should never feel trapped in automation. The moment the system cannot confidently resolve a request, it should offer human assistance.
Escalation works best when:
- The system detects frustration
- The query is complex
- The request involves risk or financial decisions
Equally important is context transfer. When a human agent joins, the user should not have to repeat everything. Passing conversation history to the agent significantly improves user trust and reduces support time.
7. Write Conversational Microcopy that Builds Trust
Words define the experience. In conversational UI, microcopy replaces interface elements such as labels, tooltips, and instructions.
Good conversational microcopy is:
- Cear rather than clever
- Helpful rather than promotional
- Specific rather than vague
Users trust systems that communicate predictably. Overly casual tone, jokes, or marketing language often reduces credibility, especially in banking, healthcare, or service contexts. The tone should match the product’s purpose and user expectations.
8. Balance Personalization with Privacy
Conversational systems often use user data to personalize responses. While personalization improves efficiency, it can also feel intrusive.
A system that remembers preferences can reduce effort, but referencing sensitive data without context can create discomfort. Users should understand why the system knows something and how it is being used.
Good practice includes transparency, optional personalization, and allowing users to correct stored information. Trust increases when users feel in control of their data.
9. Design for Ambiguity and Unexpected Inputs
Users rarely communicate in perfectly structured sentences. They may:
- Change topics
- Send incomplete messages
- Make spelling mistakes
- Ask multiple questions at once
A conversational interface should anticipate this variability. Instead of failing when input does not match expectations, it should interpret meaning, ask clarifying questions, or redirect appropriately.
Handling ambiguity gracefully is essential because real conversations are unpredictable.
10. Use Confirmations Wisely to Reduce Friction
Confirmations help prevent mistakes, but excessive confirmations slow users down. The goal is to confirm only when the action is important or irreversible.
High-risk actions such as payments, cancellations, or account changes should be confirmed clearly. Low-risk actions such as checking information do not need repeated verification.
A well-designed confirmation reassures the user without interrupting progress. It should clearly state what will happen and provide a simple way to proceed or correct.
Also Read: Chatbot Designing - What's New to Adopt
Conversational UI Design Patterns with Real Examples
Design patterns make conversational UI predictable, scalable, and easier to design. Instead of inventing flows from scratch each time, product teams can rely on proven interaction structures that solve recurring user problems.
Patterns are especially important in conversational design because users expect consistency. If a conversation behaves unpredictably, trust drops quickly. The patterns below represent some of the most widely used and effective conversational structures across industries.
1. Guided Conversation Pattern
The guided conversation pattern leads users step-by-step toward a defined outcome. Instead of presenting multiple options at once, the system asks one relevant question at a time and adapts based on the user’s response.
This pattern works best when users may not know what information is required. Food ordering assistants are a clear example. Domino’s chatbot allows users to order and customize a pizza by responding to simple prompts, showing menus and selectable options rather than requiring typed instructions. The conversation begins with a greeting and quickly moves the user toward reordering or choosing toppings through guided steps, minimizing effort and speeding decision-making.
2. Slot-Filling Pattern
Slot-filling is a structured conversational pattern where the system collects required data pieces one by one. Instead of showing a long form, the interface asks sequential questions and fills required fields in the background.
The user feels like they are chatting, but the system is completing a structured transaction.
For instance, banking assistants use the same pattern. Bank of America’s “Erica” gathers account-related inputs conversationally and helps users check balances or perform transactions without navigating banking menus.
3. Quick Replies and Suggestion Chips
Quick replies and suggestion chips provide structured response options inside a conversation. Instead of typing free text, users can tap predefined choices.
This pattern reduces typing effort and minimizes misunderstanding. It is particularly useful for mobile-first experiences where speed matters.
Suggestion chips are helpful when:
- The system anticipates common next actions
- The user might need guidance
- Clarity is more important than flexibility
For example, after a user checks an order status, the interface might show options such as “Cancel Order,” “Track Delivery,” or “Talk to Support.”
This pattern strikes a balance between natural language flexibility and structured efficiency.
4. Conversational Search and Product Discovery
Conversational search allows users to describe what they are looking for in natural language instead of applying filters manually.
Beauty brand Sephora’s chatbot recommends products based on user preferences and questions, making product discovery interactive rather than navigational.
This pattern works well when users have a general idea but not a precise specification. It feels more intuitive than traditional search, especially for mobile users.
5. Conversational Onboarding Flows
Conversational onboarding replaces long signup tutorials or instruction screens with an interactive walkthrough.
Instead of explaining how a product works, the interface teaches while interacting.
Language-learning app Duolingo uses chat-based interactions to simulate real conversations with native speakers, helping users learn by doing rather than reading instructions.
This pattern works because users understand interfaces faster through action than through documentation.
6. Payment and Transactional Patterns
Transactions inside conversations remove the need to switch screens. The user asks, confirms, and completes an action within the chat.
Ride-hailing, banking, and commerce products use this heavily. Some AI assistants can book services or order items directly after understanding the request and confirming details. For instance, quick commerce brand Big Basket and Eternal’s Zomato and BlinkIt are pioneering the integration of conversational commerce, allowing users to place orders directly within ChatGPT.
The success factor here is clear confirmation. Before payment, the system summarizes the action and asks for approval.
7. Customer Support Automation Patterns
Customer support is one of the strongest applications of conversational UI. Automation patterns in support typically involve triaging, resolving common queries, and escalating when necessary.
A well-designed support conversation:
- Identifies the issue category
- Attempts resolution with guided steps
- Offers self-service resources
- Escalates to a human agent when required
The key is progressive assistance. The system should attempt simple solutions first, but recognize when automation is insufficient.
Support patterns succeed when they reduce wait time and effort. They fail when they block access to human help.
Conversational UI Metrics: How to Measure Success and ROI
Conversational UI is an operational investment. To measure success, you need to look beyond engagement and focus on business impact, efficiency, and user outcomes.
Below are the most important metrics that define performance and ROI.
1. Containment Rate, Completion Rate, Drop-Off
Containment rate tells you how many conversations were fully handled by the bot without needing a human agent. A high containment rate usually indicates cost efficiency, but only if resolution quality remains strong.
Completion rate measures how many users successfully finish a task they started (for example, booking, payment, onboarding). This is directly tied to business outcomes.
Drop-off rate shows where users abandon the conversation. High drop-off at specific steps often signals friction, confusion, or excessive questioning.
2. Conversation Success Rate
Unlike completion rate (which tracks task finishing), conversation success rate evaluates intent resolution. For example, if a user’s goal was “track my order,” the session is successful only if the correct tracking information was delivered clearly.
This metric forces teams to measure outcomes. It is one of the strongest indicators of real user value.
3. CSAT and CX Measurement
CSAT (Customer Satisfaction Score) surveys are often embedded at the end of an interaction with a quick rating prompt. This provides immediate sentiment feedback.
Beyond CSAT, teams should analyze:
- Sentiment trends in conversation transcripts
- Escalation frustration signals
- Repeated queries for the same issue
High containment with low CSAT is a red flag. It indicates automation is resolving tickets but not satisfying users.
4. Reduction in Support Costs
Conversational UI reduces dependency on human agents for repetitive queries such as order tracking, password resets, and FAQs.
ROI is typically calculated by:
- Reduction in average handling time
- Decrease in live agent workload
- Cost per interaction comparison (bot vs human)
For high-volume industries like telecom, e-commerce, and banking, even small efficiency gains can translate into significant annual savings.
5. Lead Generation and Conversion Metrics
Conversational interfaces often increase engagement during discovery. Metrics to track include:
- Lead capture rate
- Qualified lead percentage
- Conversation-to-demo booking rate
- Conversation-to-purchase conversion rate
Because conversation reduces friction, it can outperform static forms, especially on mobile devices.
Also Read: Post-Launch UX Monitoring Checklist - Metrics to Track
Build Conversational Experiences That Drive Real Outcomes
Well, to sum up, conversational UI is not about merely adding a chatbot to your website. It is about redesigning how users interact with your product. When done right, it reduces friction, increases task completion, lowers support costs, and creates faster, more intuitive experiences. When done poorly, it becomes a rigid form disguised as a conversation. The difference lies in intent mapping, structured dialogue, contextual intelligence, and clear outcome-driven design.
If you are exploring conversational UI for your product, the right design foundation makes all the difference. At Onething Design, we help brands craft intelligent, goal-driven conversational experiences that not only automate interactions but genuinely improve customer journeys. If you’re ready to design conversations that convert, support, and delight, let’s build it together.