What is a chatbot?
A chatbot is software that can undertake a human like conversation with a user. A user can either speak to it or message it through a chat application, and it will respond as appropriate by speaking, typing something or showing something graphical. At the center point of digital transformation lies projects looking to improve and automate customer experience. Rightfully so, self-service options are what a rapidly growing number of customers are looking for when they interact with a business. Customers are looking for 24/7 support with personalized experiences and interactive channels they already use. We have seen plenty of chatbots launch with high expectations only for them to fail, leaving frustration and a waste of resources for all involved.
Chatbots have struggled to meet the demands of customers due to the technical limitations of the platforms they were built upon. Without robust capabilities and functionalities, and in-depth customization, most chatbots remain lackluster. With that in mind, we have created this guide to help you understand the critical capabilities of a chatbot platform and how to evaluate the solutions in the market to achieve the experience you are looking for.
There’s no question that FAQs have their place within a chatbot. Giving your customers access to multiple-choice or pre-determined answers quickly and directly might be all you need for certain areas of your use case. However, if you want to create an intuitive and sophisticated chatbot experience, you need to expand capabilities past predefined questions and answers. QnAs break down if the user provides any input outside of the predetermined options.
To build an intelligent chatbot, you need to be leveraging NLU, for both FAQs and advanced flows. A successful chatbot needs to reduce friction for a customer and help them achieve what they set out to accomplish. One of the benefits that NLU provides is allowing users to complete their requests using their own words (AKA natural language). If the chatbot understands the natural language of a person and can respond intelligently by tapping into data that has not been pre-programmed, the user remains engaged, and likely will continue their interaction with the bot. From answering questions to handling user transactions to providing recommendations, NLU expands what your chatbot can do exponentially.
Chatbots that are limited to multiple-choice and simple FAQs won’t be impactful for a business or its customers. If you are not using a chatbot platform that has the means to use NLU or NLG, you are missing out on one of the most exciting and rewarding opportunities in conversational AI, building a truly intelligent chatbot. Leveraging natural language understanding or natural language generation isn’t easy though, and you can’t just flip a switch and have all of it configured and ready to use. It takes careful planning to be able to benefit fully from it.
NLU goes beyond just allowing customers to talk more naturally to a chatbot assistant. Advanced capabilities include language identification, spell checking, detecting entity patterns automatically, retaining context after a conversation has ended, and much more. NLU utilizes large sets of intelligent data to accomplish this and gives users a more personalized experience as a result. To reduce the time it takes you to build an assistant, expand it, and create an experience that will be accessible and valuable to your user base, look for platforms that come equipped with an NLU/NLG engine.
Every conversational AI project will need some level of customization at some point. There are many places in which customization is critical, but not all may be important from the get-go. Customization includes both integrability and creating a unique customer experience.
The last thing you want is another piece of software that doesn’t integrate well into your system. Not only is it important for managing the complexity of your tech stack, but also, leveraging those integrations allows you to access data that can set your chatbot apart from the competition.
For example, being able to create a chatbot that integrates well with your CRM system will allow users to check their banking account balances and the last time they visited a local branch for fraud detection purposes. You can further ask them for feedback on their experience via the chatbot and add that to your customer satisfaction scores. Additionally, if they requested information about a home loan or other related services, you can utilize the data collected through the bot to proactively supply to your agents.
A successful customer interaction results in the customer wanting to return. Most of all, customers want to be provided with a service that is available 24/7, that feels personally catered to them, with access to a live agent when they need it. To accomplish this, ensure your platform can integrate well with channels and other software to provide the best experience.
As your chatbot starts to get more traffic and interactions, it is paramount that you have a platform that can support the increase in volume. The platform’s infrastructure that you build your chatbot on needs to be reliable, provide SLAs with minimum downtimes, and resolve bug-related conflicts quickly. Having a reliable platform to back you, means peace of mind once your project begins to make headway with your target user group. Platform scalability can mean both being able to operate under growing user engagement and the performance of the platform when your use cases start to see an increase in volume.
After you have launched your initial chatbot, naturally you will start to think about what is next and how you can expand on the capabilities. Leveraging usage metrics, detecting misunderstood intent topics, and identifying out-of-scope areas currently not covered by your bot will help you iterate and improve your bot over time. Your chatbot platform should have the necessary functionalities in place to identify these metrics and implement improvements without needing to use alternative products.
Additionally, the platform you use should have mechanisms in place to rapidly reuse objects that have already been created with your first use case. For example, it shouldn’t take weeks to swap out content or make changes to an existing set of flows. Be sure to consider what it will take to scale and manage your chatbot once it reaches a certain size.
There are some platforms out there that excel at providing you with the base-level tools of creating a chatbot, but be cautious. These platforms don’t scale with a fully realized conversational AI chatbot. They give you tools that help you build and test which at face value seem like they work great for getting your solution to functioning status. However, after the solution is deployed, customers start to run into issues with the assistant, and support tickets start to flood in. Due to these tools not having functionalities to enable having a dedicated support team to assist customers, the chatbot struggles.
Chatbot platforms like this are enticing, especially when they are provided by well-established providers, but they aren’t specialized for building conversational AI, they are created to complement the rest of the product suite that the provider offers, including the deployment infrastructure, the analytics layer, and even the data integration layers.
To create ground-level customization, it can seem like an easy decision to choose solutions with BYO NLP models. Being able to create the core machine learning models that support your project, and owning your code can sound perfect for creating a custom experience. The devil is in the details.
Giving chatbot builders a blank slate can seem exciting at first, but for those not familiar with NLU or conversational AI, it can be daunting and discouraging. You might have the resources to bring on expertise like a team of data scientists, but that can be exceedingly pricey and a highly competitive field for hire. Customers who are interested in starting from scratch need to be prepared to incur additional resourcing costs, slower time-to-value and potentially challenges around expertise.
Finding a platform that can meet your customer expectations and can design and build one-of-a-kind experiences will make a huge difference in elevating your customer experience. Try to find a platform that has a managed NLU engine (meaning you don’t have to be a data scientist to reap all the benefits, but can if you want to), is extensible to meet your customer expectations, provides support to your team, and gives you what you need to create a chatbot that can scale. Developing a conversational AI-driven chatbot doesn’t need to feel like learning a new language. Having the right platform for you and your team does more than just give you peace of mind, it gives you cost-savings, accelerated TTM, and a support team always ready to help.
You now know what you need from a conversational AI platform at a high level, but what capabilities and features should you be on the lookout for? While some chatbot features are available to make your life easier when creating a chatbot, there are critical functionalities that are non-negotiable when deciding who to go with.
Being able to visualize the conversational logic behind your chatbot so you can have confidence in each step you take
Robust APIs are a must when it comes to ensuring you have access to data to both pull into the chatbot platform and push to other tools when needed.
Excited about scrolling through endless pages of NLU documentation to figure out how to configure and tune your models? Unless your launch is in two years, we wouldn’t be either. A managed NLU engine means you get all of the power of conversational AI, without the hassle.
Chatbots don’t provide any value if they aren’t available where your users are, so ensure there are integrations available for channels that your users are already accessing or will be soon.
As your users start to interact with the chatbot, you need to be able to utilize the data and continuously improve your chatbot. Capabilities that help you identify misunderstandings, successful conversations, and future chatbot topics, are all critical in the long-term success of your chatbot.
Especially when things start to scale up, having regression testing in a platform will save you from having to manually go back to test any changes to your chatbot.
Documentation and self-learning options are great, but having a dedicated support team available will help alleviate any issues you may have with your project.
Being able to have your whole team coordinating in real-time will save you from having to create extensive documentation for managing changes to content, flows, code, etc.
Having access to all of the content being used across your chatbot makes it substantially easier to make changes in bulk or one tweak at a time.
Addressing customers with a chatbot experience that feels impactful and pleasant to use takes time, but if you have a process in place from the beginning, it can make things drastically easier. If you are looking for a more in-depth content piece on how to build a successful chatbot, be sure to follow the link.
Begin with conceptualizing chatbot capabilities, known technical challenges, target channels, company branding, personalization, and other considerations for your chatbot. Design is a step that cannot be rushed, the last thing you want to occur is creating a chatbot that doesn’t maximize the value it provides to you and your customers.
While not all chatbots require the usage of NLU, sophisticated chatbots often do. When training your NLU, clarity is key for the chatbot to be able to identify user intents and give proper responses back to customers. So spend time recognizing what intents require NLU and which might be better off as a straightforward experience. Both provide an immense amount of value to the overall user experience.
Review with your business teams: this is not necessarily a step you take after NLU training. You should pull the list of business requirements into your design and NLU training. You can either loop your customer-facing teams periodically, or you can co-create using platforms that have an integrated conversational design and development environment. Your content and messages will always have to change and tune, and having the right platform to do that helps you accelerate your cycles.
Catching potential bugs and issues before they happen is the payoff of having a good, thorough testing process. Being able to leverage regression testing and conversation emulators will make the process seamless. Ensure that there is proper test coverage across the chatbot and make sure to test early and often to avoid having to dedicate time and resources for backtracking through each flow. This also includes the channels you have configured your chatbot to be available on.
Cheers to a successful launch! All of the necessary steps have taken place in the planning and testing of your chatbot. It can be beneficial to launch on one or two select channels instead of all of them at once in case you encounter any issues.
After you have launched your chatbot, now you can begin monitoring feedback and metrics that your users are generating. Having tools to identify usage metrics and areas to improve can help to make your chatbot successful over the long term.
Finding the chatbot platform for you doesn’t have to be complicated. With so many types of chatbots available, you must be informed about what to look for when considering vendors in the space. If you are ready to start creating your chatbot with a platform that is easy to use, but doesn’t sacrifice customization and capabilities, get started with a free trial or schedule a demo to check out our enterprise offering.