Although chatbots have been under development for at least a few decades, they did not become mainstream channels for customer engagement until recently. Due to serious efforts by industry giants like Apple, Google, Microsoft, Facebook, IBM, and Amazon, and their subsequent investments in developing toolkits, chatbots and conversational interfaces have become a serious contender to other customer contact channels. In this time, chatbots have been applied in various sectors and various conversational scenarios within sectors like retail, banking and finance, governmental, health, legal, and many more.
Over the last few years, an ecosystem of tools and services has grown around the idea of conversational interfaces. There are a number of tools that we can plug and play to design, develop, and manage chatbots.
Mockup tools
Mockups can be used to show clients as to how a chatbot would look and behave. These are tools that you may want to consider using during conversation design, after coming up with sample conversations between the user and the bot.
Mockup tools allow you to visualize the conversation between the user and the bot and showcase the dynamics of conversational turn-taking. Some of these tools allow you to export the mockup design and make videos. BotSociety.io and BotMock.com are some of the popular mockup tools.
Channels in Chatbots
Channels refer to places where users can interact with the chatbot. There are several deployment channels over which your bots can be exposed to users. These include
- Messaging services such as Facebook Messenger, Skype, Kik, Telegram, WeChat, and Line
- Office and team chat services such as Slack, Microsoft Teams, and many more
- Traditional channels such as the web chat, SMS, and voice calls
- Smart speakers such as Amazon Echo and Google Home.
Choose the channel based on your users and the requirements of the project. For instance, if you are building a chatbot targeting consumers, Facebook Messenger can be the best channel because of the growing number of users who use the service already to keep in touch with friends and family. To add your chatbot to their contact list may be easier than getting them to download your app. If the user needs to interact with the bot using voice in a home or office environment, smart speaker channels can be an ideal choice. And finally, there are tools that can connect chatbots to many channels simultaneously (for example, Dialogflow integration, MS Bot Service, and Smooch.io, and so on).
Chatbot development tools
There are many tools that you can use to build chatbots without having to code even a single line: Chatfuel, ManyChat, Dialogflow, and so on. Chatfuel allows designers to create the conversational flow using visual elements. With ManyChat, you can build the flow using a visual map called the FlowBuilder. Conversational elements such as bot utterances and user response buttons can be configured using drag and drop UI elements. Dialogflow can be used to build chatbots that require advanced natural language understanding to interact with users.
On the other hand, there are scripting languages such as Artificial Intelligence Markup Language (AIML), ChatScript, and RiveScript that can be used to build chatbots. These scripts will contain the conversational content and flow that then needs to be fed into an interpreter program or a rules engine to bring the chatbot to life. The interpreter decides how to progress the conversation by matching user utterances to templates in the scripts. While it is straightforward to build conversational chatbots using this approach, it becomes difficult to build transactional chatbots without generating explicit semantic representations of user utterances. PandoraBots is a popular web-based platform for building AIML chatbots.
Alternatively, there are SDK libraries that one can use to build chatbots: MS Bot Builder, BotKit, BotFuel, and so on provide SDKs in one or more programming languages to assist developers in building the core conversational management module. The ability to code the conversational manager gives developers the flexibility to mold the conversation and integrate the bot to backend tasks better than no-code and scripting platforms. Once built, the conversation manager can then be plugged into other services such as natural language understanding to understand user utterances.
Analytics in Chatbots
Like other digital solutions, chatbots can benefit from collecting and analyzing their usage statistics. While you can build a bespoke analytics platform from scratch, you can also use off-the-shelf toolkits that are widely available now. Many off-the-shelf analytics toolkits are available that can be plugged into a chatbot, using which incoming and outgoing messages can be logged and examined.
These tools tell chatbot builders and managers the kind of conversations that actually transpire between users and the chatbot. The data will give useful information such as the conversational tasks that are popular, places where conversational experience breaks down, utterances that the bot did not understand, and the requests which the chatbots still need to scale up to. Dashbot.io, BotAnalytics, and Google’s Chatbase are a few analytic toolkits that you can use to analyze your chatbot’s performance.
Natural language understanding
Chatbots can be built without having to understand utterances from the user. However, adding the natural language understanding capability is not very difficult. It is one of the hallmark features that sets chatbots apart from their digital counterparts such as websites and apps with visual elements.
There are many natural language understanding modules that are available as cloud services. Major IT players like Google, Microsoft, Facebook, and IBM have created tools that you can plug into your chatbot. Google’s Dialogflow, Microsoft LUIS, IBM Watson, SoundHound, and Facebook’s Wit.ai are some of the NLU tools that you can try.
Directory services
One of the challenges of building the bot is to get users to discover and use it. Chatbots are not as popular as websites and mobile apps, so a potential user may not know where to look to find the bot. Once your chatbot is deployed, you need to help users find it. There are directories that list bots in various categories.
Chatbots.org is one of the oldest directory services that has been listing chatbots and virtual assistants since 2008. Other popular ones are Botlist.co, BotPages, BotFinder, and ChatBottle. These directories categorize bots in terms of purpose, sector, languages supported, countries, and so on. In addition to these, channels such as Facebook and Telegram have their own directories for the bots hosted on their channel. In the case of Facebook, you can help users find your Messenger bot using their Discover service.
Monetization
Chatbots are built for many purposes: to create awareness, to support customers after sales, to provide paid services, and many more. In addition to all these, chatbots with interesting content can engage users for a long time and can be used to make some money through targeted personalized advertising. Services such as CashBot.ai and AddyBot.com can integrate with your chatbot to send targeted advertisements and recommendations to users, and when users engage, your chatbot makes money.
In this article, we saw tools that can help you build a chatbot, collect and analyze its usage statistics, add features like natural language understanding, and many more.
The aforementioned is not an exhaustive list of tools and nor are the services listed under each type. These tools are evolving over time as chatbots are finding their niche in the market. This list gives you an idea of how multidimensional the conversational UI ecosystem is and help you explore the space and feed your creative mind.
If you found this post useful, do check out the book, Hands-On Chatbots and Conversational UI Development, which will help you explore the world of conversational user interfaces.
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