Key terms for chatbot UX design
- Intent: Intent is a user’s purpose for interacting with your chatbot—the specific goal or problem to address, like paying a bill or getting a question answered.
- Utterance: An utterance is any individual statement made during an exchange, like “hello!” or “I’d like to pay my bill” or “yes.”
- Exchange: An exchange consists of two or more utterances. Essentially, the term refers holistically to the back-and-forth conversation between user and chatbot.
- Step: Each back-and-forth interaction between chatbot and user is a step. Steps might include greetings, clarification questions, actions, handoffs, or even small talk.
- Contact: Each instance of a user engaging a chatbot is a contact. Contact is not entirely coterminous with exchange: if a user initiates a contact by opening the chatbot window but doesn’t respond to the chatbot’s greeting, no exchange has occurred; if a user restarts a conversation, that contact now includes multiple exchanges.
- Domain: Domain is a broad description of your chatbot’s scope, like customer support or human resources. Each domain comprises multiple topics. Chatbots designed to converse on any subject whatsoever, like ChatGPT, are called open-domain chatbots.
- Topic: A topic is a specific subject matter or set of tasks within a domain. For example, the domain of customer support might contain topics like bill payment, store hours or returns. Each topic maps to specific user intents.
- Entity: An entity is a noun relevant to user intent—like a product, document, or service—mentioned in an utterance. Proper identification of entities is an important element of natural language processing (NLP).
- Escalation: Escalation is a handoff from chatbot to human agent. This can be a “planned” escalation, or a “fallback” escalation (when a bot cannot recognize or resolve the user’s intent).
- Flow logic: Flow logic governs how bots react to each utterance and proceed to the next step. This may be involve simple if-else statements, decision trees, complex algorithms or machine learning-driven probabilistic logic.
- Preferred response: An utterance that positively moves the exchange toward resolving user intent.
- Non-preferred response: An utterance that does not progress the exchange toward intent resolution.
Determining use cases and goals for chatbot UX
Choose the right domain(s): where can a chatbot help most?
Your FAQs are an excellent knowledge bases for queries, tasks and problems that surface frequently and predictably. Your customer service teams are likewise an important source of insight. Robust business process management can further identify opportunities and inefficiencies, as well as help delineate the different knowledge centers, communication channels and levels of complexity, security and privacy germane to each domain.
Chatbots offer the most value when two-way conversation is needed or when a bot can accomplish something faster, more easily or more often than traditional means. Some domains might be better served by help articles or setup wizards. Others, like those requiring highly technical assistance or sensitive personal information, might be better left to a real person.
Balance short-term and long-term business goals
For your first chatbot, it’s wise to walk before trying to run. The less data you have, the less confidently you can make predictions: companies that spend months building an inaugural chatbot spanning many topics often learn (after launching) that key assumptions about user behavior were wrong—and have to practically start again from scratch. Effectively addressing a shorter list of topics and intents yields a better user experience than providing inconsistent results across a wider domain.
Having said that, choose a domain with growth potential. Truly successful chatbot strategy yields not standalone solutions, but conversational tools deployed across all relevant channels—websites, messaging apps, phone systems—that enrich each other by generating shared data for training and optimization.
Choosing the right type of chatbot
Broadly speaking, chatbot offerings fall into two categories: rule-based chatbots and AI chatbots.
Rule-based chatbots are simple and economical. They operate on if-then-else rules: each step (or branch in a decision tree) is assigned specific inputs the chatbot can recognize, each matched to a scripted response. Lacking natural language processing (NLP), rule-based bots must restrict user utterances to simple phrases or pre-written options. This may limit success unless your users’ needs are highly predictable, repetitive and straightforward—and will stay that way as you scale up.
AI chatbots are more robust, versatile and scalable. Artificial intelligence capabilities like conversational AI empower such chatbots to interpret unique utterances from users and accurately identify user intent therein. Machine learning can supplement or replace rules-based programming, learning over time which utterances are most likely to yield preferred responses. Generative AI, trained on past and sample utterances, can author bot responses in real time. Virtual agents are AI chatbots capable of robotic process automation (RPA), further enhancing their utility.
Many situations benefit from a hybrid approach, and most AI bots are also capable of rule-based programming.
Planning your chatbot
Before designing the fine details of your customer experience, plan the foundation of your chatbot.
- Determine starting channels:The channels where users interact with your bot should naturally align with functions it serves. Each different channel affects how users articulate themselves, how your bot should respond, and which systems are available for integration. Once you’ve picked a domain, make sure you can work with relevant channels. For example, don’t automate payment questions if the team running the payments page of your website won’t let you add the chatbot client to it.
- Identify primary topics: The fundamental goal in chatbot planning is to determine your bot’s “minimum viable knowledge,” or MVK. MVK is the minimum topic coverage the bot must be capable of in order to fulfill its purpose. This entails both topic breadth—the full range of different topics to cover—and topic depth: how thoroughly each topic must be covered. Start with topic breadth: list all the possible topics relevant to the chosen domain, then prioritize. Focused topic breadth facilitates greater topic depth, increasing your chance of success.
- Aggregate all relevant knowledge bases: Your bot must be able to access any info necessary to understand and address user intents that fall within its scope. That info might not be all in one place: it’s often spread out across disparate sources like webpages, databases, documents, FAQs, CRM platforms and online transactional processing (OLTP) systems. Intelligent search is the ideal way to aggregate all relevant data sources and streamline information retrieval.
Chatbot personality
Users unconsciously, automatically infer a character behind your bot. It should convey the positive characteristics we seek in human conversation—empathy, curiosity, patience, affability—while maintaining the transparency of being a robot. The latter is essential to both managing user expectations and avoiding the “uncanny valley” effect: the strange uneasiness provoked by humanoid things that are not-quite-right. This can be most easily achieved by thoughtful choice of name, avatar and greeting.
Your chatbot’s personality impacts most elements of conversation design. It should reflect your brand and be appropriate for its intended users and function: a fitness assistant bot should use active language; a healthcare diagnosis app should avoid jokes.
Start by considering where your chatbot falls on various spectrums:
- Funny vs. Serious
- Enthusiastic vs. Calm
- Formal vs. Informal
- Warm vs. Cool
- Authoritative vs. Relatable
Conversation design
A chatbot provides only half of a conversation. You can’t control or fully predict the user’s half. Strong conversation design ensures a positive user experience by approaching conversation flow in a way that, no matter the user’s utterance, the chatbot’s response feels natural, believable and productive.
Topic depth
True coverage of a topic requires not only designing ideal conversational paths, but envisioning all unique paths a conversation might follow, including potential confusions, detours and dead ends. You might program your scheduling bot to recognize “I want to change my appointment,” but a user might say, “I can’t do Tuesday anymore.” You might have an optimal path, but is there a plan B if plan A fails? If plan B fails, can your bot explain the problem to the user? If the user doesn’t understand a request, can the bot phrase the utterance differently?
Resilience
Even if your flow logic is flawless, mistakes happen—but minor imperfections should not derail an exchange. Here again, AI chatbots have a major advantage: instead of manually predicting and planning for every single typo to avoid interruptions, artificial intelligence can make educated assumptions and keep things moving. For example, IBM watsonx Assistant features autocorrect for mispellings, as well as fuzzy logic to aid recognition of intents and entities. Likewise, AI bots with speech-to-text can be trained to properly interpret accents, mispronunciations and jargon in voice inputs.
Explore tips for training speech-to-text AI – This link opens in a new tab
Relevancy
As in regular human-human conversation, users want to feel understood. Chatbot design can achieve this by ensuring that all bot responses, even non-preferred responses, are informative and relevant to the user’s utterance. When copywriting chatbot dialogue, aim to acknowledge what the user has said and avoid blunt changes of subject, random leaps in conversation, or “forgetting” information the user provided earlier in the contact.
Repair
Chatbots have limitations. The capacity to fail elegantly and provide routes to repair the conversation is essential: it’s okay for a bot to be wrong, but being wrong and irrelevant may doom the exchange and deplete trust in the chatbot. Bots must be designed to gracefully handle harassment, recognize nonsense or irrelevant utterances, react to topic shifts and get the conversation back on track.
Ease of use
Always decrease the user’s burden.
- Ask for less: Instead of a long order number, would the last four digits suffice? Would their name and an additional piece of simple information—say, the order date—eliminate the need for the number altogether?
- Clear choices: Word every question carefully. A user might reply to “would you like a Wednesday or Thursday appointment?” with “yes”—a non-preferred response. But there are only two responses to “please choose Wednesday or Thursday.”
- Buttons: When there is a small list of potential options, choosing a pre-written option—or, for phone systems, a keypad number—makes things easier and eliminates the possibility of a non-preferred response.
Word diet
Clear, concise copy reduces friction and demonstrates respect for the user’s time. Reconsider your conversation flow if it requires lengthy instructions.
Effective chatbot design involves a continuous cycle of testing, deployment and improvement. Individuals may behave unpredictably, but analyzing data from past contacts can reveal broken flows and opportunities to improve and expand your conversation design.
