Chatbots are still an emerging technology that has been somewhat relegated to futurism. However, chatbots are incredibly useful tools that can interact with customers and keep users engaged. Creating effective chatbots is about utilizing the technology available to get as close to customers as you can.
Thankfully, much of the chatbot technology is open source and available to modify to fit your needs. Chatbots are nowadays part of the ERP software tools and they can be tuned to suit whatever specific needs you may have. To create the best chatbots you can, you must know how to hire an AI engineer that will help you succeed. Although it might seem an easy process, finding a suitable candidate is not always an easy task.
There are many options when it comes to developing scale chatbots that will be customers forward but they all rely on powerful AI to truly pull off an engaging experience.
1. Use a pre-built AI program
Luckily, much of the technology required to make an engaging live chat is open source and can be modified to suit whatever needs you may have. AI is specifically required to make real chatbots that are dynamic and engaging.
Thankfully, there are several AI programs available to choose from that will fit your specific needs so that your team doesn’t go about creating a chatbot in the hardest way possible.
2. Start building a decision tree
A decision tree provides a model for your chatbot. When a customer opts-in or out of a certain action, they are led down a different branch of the decision tree.
Creating engaging decision trees is about coming to the chatbot from an outsider’s perspective to gain an objective understanding of the chatbot and its limitations.
Gaining an understanding of the chatbot requires that you can mimic a real conversation if only to a limited degree. A chatbot’s ability to convince the user of its success is defined by whether or not it can simulate a conversation at all. For example, chatbots are very often found in financial services CRM tools to ensure the clients’ needs are understood and met.
3. Simulate a real conversation
Creating a convincing copy can be one of the hardest aspects of crafting an immersive and engaging customer service chatbot. But, it’s essential to test and tweak the copy to sound inviting and on-brand.
Simulating real conversation is the only way for the chatbot to gain a full understanding of how to interact with the customer and how the chatbot handles decisions, something very helpful when it comes to UEBA tools as well.
Testing the chatbot repeatedly by holding conversations like a customer would be the best way to prepare a chatbot for real-world use.
4. Gather feedback
Gathering honest feedback is an essential part of the development process. However, gaining great feedback requires that you test the chatbot against the consumer base and the internal development team.
Getting great feedback works only if you apply it but the changes should not run counter to the goal of the chatbot. Some innovations and changes can take a chatbot to the next level.
5. Tweak your logic and automation
Only by testing the bot outside of the internal team will you truly be able to understand its limitations and the boundaries of its functionality. Every engaging chatbot is built with the customer first in mind and is obsessed with creating a convincing experience that users will always remember.
Simulating a real interaction requires not only great copy, but you need to make sure that your decision trees, customer options, and integrated functionalities are all working together and playing nice. This requires testing and possibly messing around with a few APIs; although you may want to enlist an automation engineer or technical marketer to help you smooth the rough edges of your chatbot.
Choosing the right AI depends on the disposable resources allotted to such an endeavor and the boundaries of functionality for the app specifically. Using a prebuilt AI program is recommended but which one you choose will be up to you as long as it is powerful and capable of imitating a true interaction.
Once a foundation is made then you will want to create branching decision trees that are complex and robust enough to answer any questions or requests that a user might have.
However, building an engaging and complex chatbot requires that users outside of the team must test it as well. If your marketing team has been performing well then you will undoubtedly have a fanbase with which you test the chatbot.