A chatbot is now a stable factor in our customer communication. We can now imagine more advanced opportunities and possibilities with chatbots and Artificial intelligence. To develop these capabilities, we follow a specific plan and structure for the entire bot lifecycle.
Like website and mobile app development, we follow certain lifecycle steps to develop a perfect smart chatbot. Kevit has experience of building a huge number of advanced AI-powered chatbots, let me show you what is the end to end process you’ll need to follow while getting your bot developed.
- Requirement Gathering and Analysis
This is the first and basic step of each product development lifecycle. Unlike website and mobile app, there are few more aspects you’ll need to cover while gathering requirements for your chatbot. Here are some points you need to think about while collecting the basic requirements of chatbot:
- Which industry you are targeting?
- What will be your audience on chatbot?
- What will be their personality, or we can say “User Persona”?
- What are the basic use-cases you are looking to cover? Ex. – Lead generation, Appointment booking, Product selling, etc.
- What are the pain points you need to cover and what will be the solution you can expect from a chatbot?
- On which channel you need your bot to be on. (Ex. Facebook, WhatsApp, etc.) Do you need your bot on multiple channels?
2. Specification Identification
After gathering a detailed requirement, you can start deciding what are the features and functionalities you are looking for. These functionalities should satisfy the pain points of requirement steps.
You’ll need to consider a few things before moving ahead with confirming your specifications:
- What are the 3rd party integrations you’ll need to cover your requirements? (Ex. Suppose you’ll need a third-party CRM tool to be integrated within your bot to collected leads you’ll get from the bot.)
- Do you need NLP based chatbot or just a flow-based bot can satisfy your need?
- What are the subscriptions that will be required later in the time of publishing?
- Ask your developers, what are the possible blockers which you’ll need to tackle before moving forward.
3. Conversation Design
This is the 3rd step and “Heart” of the chatbot development lifecycle. While the first two steps are like other software development lifecycles, this 3rd step is kind of unique and the most important part of bot building. Unlike building wireframes, this step involves building conversation interfaces by which your bot will interact with your users. These interfaces will represent the actual conversation.
You’ll need to design a whole conversation that can accomplish the desired action (Ex. Giving you their contact details). Another thing, the conversational flow should not be too long that your user will be bored! And it should not be too short that your user can’t even feel connected to perform your desire action.
Depending on requirements, your conversation flow may support or not support NLP. In both cases, the conversation flow and design may vary. If you are covering NLP, you need to make sure your conversation can handle a wide range of variations of user inputs. In this case, your conversation must be ready for the “Error Handling” part too! Yes, this is the most important aspect of your conversational flow. If you can’t handle the error or unexpected inputs from your user and can’t drive them to the right path, your chatbot will be a complete failure.
For more tips and tricks, you can visit these two detailed articles on a complete guide for conversational design -
- Conversation design article 1
- Conversation design article 2
4. Architecture and bot development
By given conversational flow and design, bot developers will brainstorm about creating a base of bot architecture. Rather than just coding and testing the system like we do while developing software, a bot development process would take a lot effort in terms of making the bot smart and to justify the efforts of conversation design too.
As we know, testing is one of the most important steps in any development lifecycle and it’s important in a chatbot development lifecycle too. However, testing a chatbot is a bit tricky. Not only the code should be tested properly, but the messaging and the flow of conversation too! Plus, it includes another complexity when your chatbot relays on some third-party messaging platform, as each messaging platform has completely different guidelines and limitations. The testing phase must involve the “Quality Assurance” part too. The quality assurance process also includes conversation design with the developed bot.
You need to check -
- Whether the bot is working without any technical errors or not?
- Are your chatbot’s look and feel match the industry and users?
- Whether the conversational flow is deriving in the right path?
- Is your bot capable of handling unexpected inputs?
- Any flow is getting a break while entering these unexpected inputs?
- Is this bot able to attract and hold the user's interest till they make the desired action?
- How interesting the bot journey is?
- All the third-party integrations are working fine or not?
- How efficient are the UX and UIs of the bot?
- Can the conversation tone will be matched with the user’s persona and interest?
And many more cases.
6. Deploy and Publish
Once you are done with the bot development it should be deployed in a hosted environment. Some of the messaging platforms require a verification process before publishing a bot. So, in this case, you’ll need to complete this bot approval process, after that only you can go live. They’ll need some short description, a long description, images, scripts, videos, etc - based on their policies. Your bot should not harm any privacy policies and it should be well-behaved in terms of interacting with users, to get “approved”. These approval processes may take 7 days or more and after getting the success in this approval process, you can put your chatbot “Live” on that platform.
7. Monitor and Observe the behavior
Sometimes, it happens that your perfectly tested chatbot can create a huge mess while interacting with the actual audience. Because after n number of test-cases you can’t predict what an end-user going to talk with your chatbot. In the failure cases, you can always improve your conversation, flows, and capabilities of understanding the user’s input and replying with the relevant answer with NLP and AI.
And if your chatbot is behaving perfectly and it’s good at handling your clients and fulfilling your needs, well congratulation! You can enjoy your chatbot service with minimal maintenance and costs.
8. Promote and analyze the response
Once your bot is published, it is the most crucial thing that your targeted audience and your customers should be aware of the presence of your bot. So, after publishing you’ll need to find options to make your chatbot discoverable. There are several strategies you can use to derive traffic to your bot like online ads, cross channel connections, and many more.
As your customers and users start using your chatbot, you’ll need to monitor the result for improvements and future developments. To analyze the success of bot, you can check several matrixes like engagement rate, bot open rate, end conversation rate, etc. Identify and fix the weak points of your bot to get the right outcome.
After the analysis phase, you can repeat the cycle with necessary improvements and make your bot smarter. NLP based chatbots can learn by themselves with knowledgebase which your users created by interacting with a bot. You can check the importance of NLP here.
So, making chatbots is not an easy job! We at Kevit, have years of experience in developing smart chatbots. Contact us at firstname.lastname@example.org to get a one for yourself! Or you can visit our website – www.kevit.io for more information.