Conversation design is the next step in UX evolution and to understand the need for a new kind of UX design, we need to look back.
Conversation design isn’t a new field – in fact, it has existed in various forms over the decades. One of its most prominent sub-fields being the conversational user interface, which helped revolutionize computer systems and is still extremely prevalent today.
In many ways, conversational UI was the logical next step in IT development after graphical interfaces. We went from terminals to pretty graphics and clickable icons because it was easier. In the same way, we are adopting conversation design because it was easier when a computer gave us a message instead of a code.
For instance, when an operation fails to complete, your OS might return an error like “No internet access found” along with a few solutions instead of leaving you with a numerical error code to decipher. Similar examples can be found everywhere including the Search bar which might say “Type here to search”.
In other words, we went from the first commercial GUI, to application likes Slack that leverages conversational user interfaces to make work collaboration easier:
Design Standards for Conversation Design
Conversation design has made computers personal and engaging to use. And even in its static (and primitive) form, conversation design is able to deliver fantastic results because of how consumer trends are changing. For instance, according to Google, 20% of all web searches are now voice-based while Gartner predicts that almost 1/3rd of all online sessions would be voice-based. As a result, this is pushing companies like KLM to embrace conversation design.
That said, conversation design is still in a similar spot as web design was back in the early 2000s. This means that the design standards for conversation design are only just emerging. Through of years of work in this field, we have tried and tested numerous design patterns, such as the following UX text patterns:
- Empty States: Intentional negative space within text to build excitement.
- Labels: Additional information that reduces the effort required in any particular experience.
- Controls: Let users know what they can control (customize).
- Text Input Fields: Make it easier for users to enter the right information.
- Transitional text: Use a temporary text element to let users know that the computer is processing something.
These design patterns, like every other design pattern, give designers a place to start but are only a small part of the world of conversation design. Numerous simple yet very effective practices such as phased editing and UX content heuristics can be applied to your design process to enhance purpose.
Conversational AI to meet new consumer demands
As we speak, the conversation design industry is being revitalized and becoming commercially viable for consumer-level applications, partly due to companies like Google, Amazon, and Microsoft who have invested billions into these advanced technologies – the most famous of which is artificial intelligence and its subfields.
We all know about AI but for conversation design, a popular subfield of AI, machine learning is even more important. ML goes one step further and adds self-correcting measures to the algorithm which means that whenever an operation fails, the algorithm learns and takes a different approach – improving accuracy.
Still, we’re not stopping there, advancements in natural-language-processing (NLP) mean that we can now create extremely versatile virtual agents by simply pointing it to the right dataset and it has the ability to extract and use the information to satisfy user queries. At the extreme end of the spectrum, we also have deep learning which allows chatbots to use neural networks to discover new answers using hidden (unspecified) variables – in other words, learning from its environment.
And conversation design is focused on leveraging these technologies to create the 21st century equivalent of talkative robots – called conversational AI chatbots.
These complex chatbots have potentially thousands of unique responses that they can create from large datasets provided by the developer. In addition to the sheer quantity of responses, conversational AI chatbots are capable of differentiating between different contexts, user intents, and tones.
That said, all of these technologies and their capabilities are only worth what they can add to the user experience. So what does all of this mean for businesses? It means that:
- Virtual agents leveraging the most advanced AI technologies are now more accessible and cost-effective
- Businesses can replace customer-facing positions (in grievances, post-purchase, loyalty programs, etc) with 24/7 and highly intelligent chatbots
- Businesses can outdo their competitors on customer experience by offering personalization at scale
- Virtual agents can be deployed internally for recruitment, business intelligence (BI), collaboration, and employee satisfaction
At a higher level, conversation design can significantly improve the efficiency and speed of traditional sales funnels by improving every step of the consumer journey (from initial lead engagement and lead nurturing to retargeting and customer retention through loyalty programs). All of this ultimately results in higher conversions.
Wrapping up: Conversation Design and the perfect UX
Conversation design is an extremely fascinating field that lends businesses greater insight into their sales journey and user experience. Right now, we’re in the middle of a major evolution in conversation design implementations thanks to AI, creating a huge first-mover advantage for businesses to perfect their UX.
Master of Code has used conversation design to build numerous AI chatbots that have complimented global campaigns, mobile apps, and websites and brought companies closer to the “perfect UX”. And while an AI chatbot isn’t the only implementation of conversation design, it certainly offers one of the best cost-to-performance ratios.