11 Steps to Design a Voice-Embedded Chatbot

    11 Steps to Design a Voice-Embedded Chatbot

    With great voice-embedded chatbots design, developers can improve the user experience and build solid business-customer relationships.

    Conversation Design is a complex subject; a lot depends on the project, the resources, and the company involved. According to the Grand View Research report on Chatbot Market Growth & Trends, the market size of chatbots will reach $3.99 billion by 2030. Here are 11 essential steps to designing a voice-embedded chatbot for developers-

    Step 1: Understand the Goals of the Conversational Interface

    Before designing a voice-embedded chatbot, developers must determine the main goals of the conversational interface. It can be selling products or services or a medical examination so that it can navigate users to the right website page and find relevant information quickly to engage them in long conversations to generate leads.

    Defining these goals will help developers in making decisions while designing & also helps in setting up the right metrics and KPIs to measure the success of this Voice-embedded chatbots once it’s live. To fulfill these requirements, developers can reach out to the stakeholders, learn their needs and expectations, and consider whether these goals fit the voice bot.

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    Step 2: User Personas

    The development of a chatbots requires developers first to know their target audience. For this, they can circulate an easy, user-friendly feedback form, or else they can analyze the contact history (emails or tickets the client’s contact center receives) and learn what target users are looking for.

    Developers must start with user research and set user personas, the parameters of general characteristics of their target users, and then check whether their voice-embedded chatbots goals match the requirements of targeted users. This way, developers can find out what the popular topics they have asked for how.

    Step 3: Mapping Topics for AI chatbots

    Developers must determine whether their voice-embedded chatbots will answer a wide range of queries or focus on limited topics. Developers will be able to map which requests their AI chatbots will understand and which answers it will be able to provide. It is a crucial step in designing Voicebot as conversational interfaces cannot handle human knowledge.

    Therefore, the more the chatbots knowledge base is specific, the better customer satisfaction it will give its users. This is because of semantic ambiguity. The narrow context helps the chatbots choose the correct answers to the query.

    Step 4: Voice Chatbot Personality & Voice Tone

    After topics mapping, it’s time to define the chatbots personality and tone of voice. The main thing developers must emphasize is that their AI-based voice-embedded chatbots is speaking in the same language their target users are speaking.

    Its overall personality reflects the brand they are working for. Developers need to decide on the formality and informality of the voice chatbots. Will it be serious or fun, lengthy or concise, etc.? This is a crucial step in developing a chatbots as it will influence the stylistic choice made while drafting the copy and designing flows.

    It is suggested to developers while designing voice chatbots, they can play with the pons and cultural standards and must focus on giving the bot a human voice via which it can create an emotional connection with the users and enhance the whole conversational customer experience. They need to be consistent always while creating a chatbots personality.

    Step 5: Conversational Bot Infrastructure

    In this step, developers must create architecture behind every activity of their conventional bot. Here, the developer must define how the voice bot will introduce itself, how it will answer the queries, and how it will retrieve data—setting infrastructure based on which conditions it will consider before answering and how users will achieve their goals. In addition, voice-embedded chatbots designers need to design the repair sequences. Some tips for these are-

    Do not blame users but be honest, give recommendations to continue the conversation, and provide the opportunity to contact humans. This can be done by email, ticket, or real-time live chat, ensuring customers are heard and increasing customer experience.

    Step 6: ChatBot Prompts & Copywriting

    Developers must ensure that their chatbots is clear & concise, availing much information in a few sentences in the easiest way possible. Adding a button at the end of the text allowing users to ask more, is an excellent option.

    Consistency in the personality and tone of voice of the voice-embedded chatbots is critical. The channels developers use generally solicit the use of a variety of languages between written and spoken. Because of this, developers are allowed to use abbreviations, emojis & some characteristics of a spoken variety, but it still is not a face-to-face interaction.

    Step 7: Prototype Testing

    Once the main conversational Flow and prompt are done, it’s time for an early user test. While developing a voice-embedded chatbots, developers need a prototype to test its user interface and a speech synthesis tool to record answers.

    To test the conversation interface, developers can use the Wizard of Oz method that presents the prototype as the final software making the user believe it is the original voice bot, in which the responses are human-generated.

    The developers must conduct these tests as soon as the prototype is ready to find out what flows & wordings are not working as per plan and change them before they are executed.

    Step 8: Natural Language Understanding (NLU) Setup

    NLU is a component allowing the voice-embedded chatbots to understand the inputs expressed in natural language. Currently, not all bots use NLU, as some focus on Conversational Flow, where users can only select options and cannot chat freely.

    Voice chatbots using NLU will offer a better natural conversational user experience. Developers need to set up NLU to improve voice bot performance constantly. They will be required to select good training phrases to give to the Machine Learning algorithm and keep tuning set.

    Step 9: Flows Implementation

    Now, it’s time to implement chatbots flow by setting its features to engage visitors with quick questions and answers. Developers can convert this voice-embedded chatbots flow into a more straightforward stage called a node with the help of different actions., like setting a welcome greeting, collecting user data, and providing various alternatives to the request.

    Developers must ensure that the chatbots flow should be as conversational as possible to enhance customer experience. Adding visuals in videos, emojis, and gif forms makes the bot quite interactive. Developers must ensure that the voice bot design is a conversation and have open communication letting developers understand customers’ requirements better and collect more valuable information. Designing a bot with human support is essential and must avail human agents whenever needed.

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    Step 10: Go Live

    After following the steps mentioned above to design a voice-embedded chatbots, from goal setting to prototype testing and flow implementation, the conversational chatbots to go live into production.

    Step 11: Chatbot Metrics Analysis

    Even though the voice chatbots is live, the final stage of design is to analyze the chatbots performance. Is it user-friendly? Is it achieving all the goals that were expected?

    Developers can measure chatbots metrics by the honest conversations transferred by the bot to human customer support; this will analyze the capability of the bot to engage customers by resolving their queries. Measuring how many successful conversions are done by bots where no human interactions have been done. Do NSP surveys to calculate the score and know how satisfied customers are with the voice-embedded chatbots responses.

    Following each step mentioned above, developers can design haul for their customers by engaging them interactively.

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    Nisha Sharma- Go beyond facts. Tech Journalist at OnDot Media, Nisha Sharma, helps businesses with her content expertise in technology to enable their business strategy and improve performance. With 3+ years of experience and expertise in content writing, content management, intranets, marketing technologies, and customer experience, Nisha has put her hands on content strategy and social media marketing. She has also worked for the News industry. She has worked for an Art-tech company and has explored the B2B industry as well. Her writings are on business management, business transformation initiatives, and enterprise technology. With her background crossing technology, emergent business trends, and internal and external communications, Nisha focuses on working with OnDot on its publication to bridge leadership, business process, and technology acquisition and adoption. Nisha has done post-graduation in journalism and possesses a sharp eye for journalistic precision as well as strong conversational skills. In order to give her readers the most current and insightful content possible, she incorporates her in-depth industry expertise into every article she writes.


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