With the number of options for building chatbots seemingly growing by the day, it can be hard to understand what you need to create a chatbot. With this blog post, we aim to provide you with high-level knowledge of the steps you can take to get started with your conversational AI chatbot solution and how to make it successful.
To start, draft up what kind of capabilities you would like your chatbot to cover. For example, if you’d like to offer a banking chatbot within your existing banking app, think through what your customers want to do that would be quicker and more efficient with a chatbot. Identify the use case complexity from a simple FAQ type interaction to chatbots that aggregate information from multiple sources to personalize the experience. Determine if your chatbot needs to be extended with custom code and API calls to other systems or can simply be programmed with a starting FAQ styled questions and answers that provides users 24/7 support.
There are many vendors out there who promise off-the-shelf solutions that take weeks before they are ready to be used by customers. These solutions are detrimental to the success of your project because they risk giving you a generic chatbot without the ability to expand or customize your customer experience.
Choosing a solution can be challenging and confusing depending on the level of expertise required for maintaining conversational AI supported functionality. When hiring or allocating resources, some of the most important members of your team are software engineers, conversational designers, product managers and sometimes data scientists. While having the right team is critical, ensuring you also have rich documentation and a vendor ready to support your project are principal requirements.
Your customer facing teams are best suited for knowing what your audience is searching for. Partner with these business users to design meaningful conversations. To best collaborate with this team, use the chatbot platforms that can bring a visual and intuitive experience in building conversations. A proper conversational design will not only keep your customers happy, but also encourages them to come back. While in the planning phase and development phase you should continually keep the principles of conversational design in mind.
In many ways, NLU can feel like a black box that requires tweaking to produce the desired results. While this is a partial requirement, it isn’t as challenging as it seems. Having a solution that comes armed with a managed NLU engine can move mountains, and it can even remove the need to hire data scientists and machine learning teams! A managed NLU engine allows you to focus on designing an experience that your users deserve, while knowing the vendor supporting you is constantly improving the NLU that you use. You should be confident knowing that the tools that are available to you don't require constant maintenance or a degree to know how to use. Part of this is creating intents, which help the engine recognize what a user is talking about. When you start organizing intents, it is key to avoid overlapping the purpose of said intent or else you will end up with a user response that is incorrect, or out of scope.
NLU is an immensely powerful part of creating conversational AI driven chatbots and what can’t be covered in this blog can be explained with this tutorial video, Requesting and Extracting Information - Part 5 of "Building a Cool Bot with Botpress".
Now it is time to put all of that hard work of planning into action! As your team goes about adding capabilities to your chatbot, it is equally important to do continuous testing to ensure accuracy and consistency. When looking for the right solution for your use case, look for an easy to use emulator / debugger to reduce the time it takes for you to identify problems, fix them and confirm the correct behavior is taking place throughout your chatbot.
Within development is also the configuration of the channels you will be deploying your assistant to. As you start adding channels, it is necessary to think through which channels you’d like the assistant to be available on to ensure maximum usage by your user base. While initially your chatbot may only live on your site, thinking about having a mobile bot, or those that integrate with communication channels such as Vonage, Slack, Twilio, and Facebook Messenger is critical to your success down the road.
As more and more chatbots are rolled out into the world, there is a distinguishing factor that sets effective chatbots apart: personalization. Chatbot users want conversations to feel natural and this means creating an experience that extends beyond just inserting your customers first name into the dialog. To create a truly personalized experience you need to find a solution that allows you to pull external data to bring your customer experience to the next level. This could be done through API calls, database queries, integrating with third-party software and more.
Success can be defined in many different ways depending on the audience, use case, and ultimate goal of a chatbot solution. Your time to market, customer adoption, and user engagement will lead you to iterate and improve your bot. Here are some tips for getting started.
It is critical to understand your existing user base, what their needs are, and where they are actively engaging with your business. Not only for the sake of providing them with a personalized experience, but also to ensure your chatbot is offering the most value. For example, if you have an IVR system in place for users that call into your business, try to improve the experience by configuring an NLU-driven chatbot for more human-like conversations. It is going to be continually important to the success of your chatbot if you treat each channel like its own product launch, matching your customers expectations and your brand experience.
Being ready to continually revise your chatbot based on customer feedback will ensure that you can bring an experience that doesn’t feel stale over time. Having a platform that supports editing content and responses independently makes it easier to continually improve your chatbot and heavily reduces the time it takes for you to make changes.
It can be tempting to believe that after you launch a chatbot your user base is going to flock to it excitedly, ready for the digital transformation that comes with a conversational AI driven solution. As great as that would be, it is important to build out a plan that can drive users to the assistant through a structured launch plan. This might include announcing the offering in a newsletter, continually on social media or through interactive pop-ups within your app, it really depends on the use case, your customers, and your brand.
When you are sourcing different platforms for functionalities you need to be sure you have metrics to support your project. Having access to metrics associated with how your chatbot is behaving, usage behavior, where the NLU is misbehaving, and identifying which intents are most commonly used, will allow your chatbot to continuously improve. You need to recognize that the relationship between the data you have collected and actual customer opinion of the assistant isn’t always one to one.
It can be tempting to rely solely on the metrics you have available to you to determine the success of your project, but you also need to be leveraging tools to gain insight into how users feel about your assistant. Reach out to your users regularly through surveys, interviews, customer satisfaction (NPS/CSAT) scoring, and teams that regularly interface with customers who have recently interacted with the assistant. If your assistant is supported by customer service agents, you are provided with another set of data points that you should be measuring and building correlations with user satisfaction.
Now that you have the information on how to make a successful chatbot, we can’t wait to see what you create. If you have questions during your project having resources like robust documentation, an active developer community, and experts standing by to answer your questions can feel like a light in the dark so be sure to find a vendor that has these options available.
To see a video tutorial covering how to get started with the Botpress platform, we have a playlist available in which you will create your own Mars Rover Photography Chatbot!
Botpress was created to provide developers with the very best tooling to build chatbots today, and future-proof those bots to incorporate coming developments in NLP technologies. Botpress' platform is built to allow companies to easily build the very best chatbot possible now, with a robust dev environment, extensive customization, and an underlying managed NLP engine that continually incorporates the latest in NLP developments. With Botpress, devs can access the latest NLP technologies, without needing a team of data scientists and ML experts. Simply put — Botpress is designed to power the best chatbots of today, and tomorrow.