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What is conversational design?
Conversational design is the process of designing interactive conversations and experiences between people and digital platforms.
It’s a sub-discipline of user experience (UX) design that involves creating interactions that are responsive, helpful, natural, and pleasant. It has become increasingly popular over the last few years as more companies move to online channels and use tools like chatbots to interact with customers.
The goal of conversational design is to create an enjoyable user experience that meets users’ needs and helps businesses to meet customer demands in a cost-effective way by reducing the labor costs associated with customer service staff.
What are the benefits of conversational design?
One major benefit of conversational design is improved customer satisfaction through clear, helpful responses from digital assistant software or chatbots.
Rather than enduring long wait times and receiving generic answers that don’t help the customer feel heard or understood, conversational design ensures these interactions are positive and useful.
The advantages extend beyond improved customer service; conversational design can also increase efficiency by providing personalized answers quickly instead of generic replies or stock answers that require additional input or research on the part of the agent or person responding to inquiries.
Additionally, using natural language processing capabilities allows automated responses to get better over time as they learn how humans communicate with each other day-to-day — without having to add extra resources.
Furthermore, organizations can use this technology to capture insights about their customers based on these conversations, which can be used for marketing purposes or product development initiatives down the road.
How to implement conversational design
Businesses wishing to implement conversational design should start by establishing an understanding between designers and engineers about what needs are driving the initiative as well as aligning on definitions for enabling technologies such as natural language processing (NLP).
They should also decide what data points will be used for training algorithms such as machine learning (ML) models before beginning actual implementation work such as building out conversation flows, selecting color schemes/fonts/textures etc., handling dead ends if a conversation suddenly stops making sense, dealing with intonation variation due to regional accents, and testing permissions & privacy protocols.
All this requires doing up front planning and making architecture decisions to avoid costly redesigns later in the process.
