We’ve been building chatbots since 2016 and understand how difficult an enterprise chatbot can be to design, build, deploy and maintain. The graphic user interface was introduced in 1960 as the basis of modern user interface development. Since its invention, the graphical user interface (GUI) has influenced how people interact with technology.
But conversational interfaces, chatbot technology, chat apps, and AI-based software are different. Great chatbot design requires the application and conversational interface to mimic human conversation in real-time, like text messaging and voice.
Today, you can easily find several online customer support chatbot examples that offer product suggestions, book reservations, place food orders, and more.
For example, we built a conversational interface called HealthyScreen based on user feedback and user research to help solve a daily problem for businesses trying to stay open and safe.
Below, we highlight some tips for creating enjoyable customer experiences when creating a good bot personality and best practices for chatbot design.
A conversational bot design should first identify what potential value a given customer will gain from the chatbot. As chatbot technology has evolved, there are great use case examples where live chat and chatbots integrate into an excellent model handling customer support and communicating with customers efficiently in real-time.
Using a chatbot to answer frequently asked questions may be more productive. Good natural language processing technology can sort through more complex conversations. And then only edge-cases can be pushed to human agents.
Before creating chatbot experiences with what we think of as human-in-the-loop, chatbot designers should think about various design factors and work through fallback scenarios that may improve customer experience without needing human assistance.
It's critical to have a solid concept of where your chatbot will be deployed before you start creating one. The platform you choose to deploy your bot will significantly impact chatbot design, user flows, and the target audience.
Facebook Messenger is a messaging app that lets you communicate with friends and family. Messenger can send text messages, photos, videos, and audio clips. You can also use Messenger to make voice and video calls. Messenger also has a robust chatbot ecosystem with many quick keys and tools to rapidly build a Facebook Messenger Chatbot or conversational interface or bot for WhatsApp.
The Google Assistant is a virtual assistant developed by Google that debuted on the Pixel and the Google Home smart speaker. Users can ask questions, control smart home devices, listen to news, search apps or play music.
Today there are thousands of unique voice-activated chatbots designed for the Google Home. User requests to invoke a skill or app, in this case, start with a simple "Hey Google." With the Google ecosystem, they will expect that you use their natural language processing solution, but that may create some issues depending on how robust or specific you want your voice-based chatbot app to be.
Echo is a line of Wi-Fi enabled smart speakers powered by Alexa, Amazon's intelligent personal assistant. Echo speakers connect to Alexa to play music, control smart home devices and answer questions. Alexa has tens of thousands of "skills" that act as chatbot experiences but are powered by voice commands and conversation. Because Echo fits nicely inside the home it can control and integrate with a host of devices and appliances inside the home. Amazon has also pushed the Alexa platform further to encompass various other devices, from Sonos speakers to the iPhone to your car.
After you have figured out a use case for the chatbot and which messaging platform to use, you should consider the bot experience you want for your target audience. When we advise customers about building bots we tell them up-front that a chatbot can be designed with two conversational interfaces in mind:
Completely scripted, rule-based bots today can be built by kids using Google Sheets or professionally using the hundreds of bot platforms in the marketplace. There are so many to choose from that we have stopped trying to catalog them. We published a brief blog post on several of them way back in 2017, which you can find on our blog. But rule-based bot tools force chatbot design into a corner from the outset.
Build your chatbot using artificial intelligence. 100% machine learning, AI-based bot that takes advantage of NLP offered by services like Amazon Lex, Google, Facebook, and the several outstanding and easier to use open source solutions. We strongly prefer going down this path with most bots of any objective complexity.
We built our own solution, which we called ACE (Azumo Chatbot Engine). The solution enabled us to build more robust chatbots more quickly. We've used it to build chatbots for Discovery Channel, Wine Enthusiasts and Coppola Winery and many others.
For complete candor, we do not like to create scripted chatbots. They break easily and bore the user. Unfortunately, they are easy to assemble, and many developers pushed these fragile snowflakes onto customers.
To make them work well, however, an excellent scripted bot experience is one where the designer and development team have spent a lot of time thinking through the bot's overall personality and the type of questions they anticipate users will have. But a rules-based bot design process will look like a decision tree where each action by the user prompts the bot's responses.
Depending on the use case, this could be perhaps 10-20 lines of scripted text to hundreds of lines of scripting. In one scripted experience, we wrote over 500 lines to handle just a small set of use cases where natural language processing (NLP) would not be a good substitute.
Many bot developers who create scripted experiences will see their scripts grow to thousands of lines. This is especially important for entertainment-focused bots that are attempting to keep the user's interest.
When thinking about using these elements, you must know that the messaging platform you decide to deploy your bot plays a crucial role in design choices.
Many bots use graphic elements like cards, buttons, or quick replies to the design flow. A visual design element helps users access key features of the bot more quickly and help users move through conversation faster.
Messages that include pictures or graphical elements, such as contact cards and buttons, may not be displayed correctly on certain messaging platforms.
For example, if you wanted to build a bot for SMS/texting, you won’t have access to cards or buttons. But if you were creating a chatbot experience for Facebook or a web interface, you can take advantage of these options and more.
In defining the aim of chatbots, designers should consider design considerations and design options to build a practical conversational experience.
Here’s a quick snapshot of elements you can use for your bot:
These are just a selection of popular elements that can be embedded into a bot experience. And while you can employ many or all of these on some platforms, it’s best to try to pick the option that is right for the moment.
The goal when designing chatbots is to create a fluid chat experience for the end user and customers. If not, you could run into a very cluttered and confusing experience for the user. After-all the bots’ purpose is to make the user’s life simpler.
In defining the aim of chatbots, designers should consider design considerations and design options to build a practical conversational experience.
We built our own solution as well which we called ACE (Azumo Chatbot Engine). The solution enabled us to build more robust chatbots more quickly. We've used it to build chatbots for Discovery Channel, Wine Enthusiasts and Coppola Winery and many others. Most of those chatbots were designed for Alexa and Google Assistant. We used ACE to also build for Facebook Messenger and other apps as well as the web.
As bot designers, we know that user data helps improve user experiences and helps us better understand user behavior. A bot needs to gather data in the background to support its iterative improvement. As a result of improved data insight, as a chatbot designer, we may enhance the customer experience by altering the bot's tone of voice or its reaction to various user inputs.
The analytics allow us to improve the overall customer experience by altering the chatbot flow. Tracking data also informs us of why a bot fails and what error messages or where in the customer journey or conversation flow needs changing.
It is possible to monitor chatbot information using an analyze engine. An analytical tool or analyzer API provides information about how bot use was used, how user interaction was handled. The app will automatically receive user feedback directly from a chatbot. It shows the simple replies button and the user is now given the option to ask whichever question or reply the button has to be sent immediately. In this respect they can give feedback to whoever answered it, but not to what it responded, and they can decline it.
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