Innovations like chatbots are slowly but steadily gaining popularity. It is a technology that every company is willing to invest in for creating efficiency, streamlining processes, and getting a return on investment. However, to be efficient and scalable without hitting the budget, the developing team must understand the life cycle of bot.
- What is chatbot?
- Stages of lifecycle of chatbot
- How Long Does it Take to Develop a Chatbot?
- Final words
What is a Chatbot?
A chatbot is a computer program that encourages Conversational User Experience (CUX) through voice or text interactions.
Users in B2B and B2C ecosystems use chatbots to simplify tasks. For example, chatbot virtual assistants enable organizations to decrease overhead expenses, efficiently use support staff time, boost sales, and even serve customers outside of business hours.
The reasoning behind this write-up is not to discuss what technology and architecture go behind making a chatbot. Instead, we are here to talk about all the different strategies around the different stages of the Chatbot Development Lifecycle.
Here’s an example of our chatbot system:
The Stages of a Chatbot Development Lifecycle
Every phase of lifecycle of bot is essential. While some enterprises may skip some stages to move faster through the bot workflow, they will have to come back later to fix issues they left behind during the development process.
The following are the steps to create a perfect chatbot messaging platform. This is for any team of software engineers looking to construct a new chatbot or add a specific skill set to an existing bot flow.
1. Requirement and Analysis
This is the initial step of every product development lifecycle. Next, in the requirement stage, you will know about your stakeholders and their audience. Here, you will need to learn what they are looking for in a bot. The idea is to overview the business process they want to fix or streamline with the new chatbot.
Here are specific points you can think about while gathering information about the chatbot requirements:
- What is your target industry?
- Who is your targeted audience?
- What will be the audience’s personality or user persona?
- What are the fundamental use cases you intend to cover? Ex – Appointment booking, lead generation, product selling, etc.
- What pain topics will be covered, and how will the chatbot handle objections?
- On what platforms do you need your bot? Do you need your bot on several channels? Ex- WhatsApp, Facebook, etc.
It is important to remember what most chatbot teams forget – the users. Ensure a proper analysis of the intended users happens at the initial stage of chatbot development. As a business, you must hire the services of a team of developers who have expertise in this arena and understand your core requirements.
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2. Identifying Specifications
Once you have settled on the requirements, you must decide what features and functionalities you are looking for in the chatbot. These functionalities would help cover the pain points that cropped up while analyzing requirements.
Before you confirm your specifications, consider these:
- Do you need NLP chatbots or a simple flow-based chatbot would do?
- What 3rd party integrations will you need to cover your requirements? Eg.- you might need to integrate a 3rd party CRM tool within the chatbot to collect leads through the bot.
- What subscriptions will be required later at the time of publishing?
- What blockers will you have to tackle later? Your software engineer would be the best person to answer.
Product specifications should ideally list the features and benefits of a chatbot. If you already have a chatbot platform and are just including extra features, every feature would have to go through the same lifecycle, similar to the production lifecycle of a chatbot.
3. Conversational Flow
Next is the conversational flow. When you are designing the conversational workflow, as a software engineer, you usually need to work with user experience or human factors. You can draw out the flow using the workflow-based tool. This will allow the developers to visualize how the conversation dialogue will flow to the user when completed.
The conversational flow is the heart of the bot development cycle and is a unique and most important part of bot building. This step is all about creating a conversation interface where your chatbot will interact with the users. These interfaces represent the original conversation.
It would help if you kept in mind that the conversational flow should not be too long; otherwise, users might lose interest in conversing. At the same time, it should not be very short so that your users feel disconnected and do not perform the desired task.
Your conversation flow may or may not support NLP based on your requirements. If NLP is covered, ensure your conversation flow can handle a variety of user inputs. Also, the conversation flow should be ready for the “error handling” part. This is crucial for a successful chat because if your chatbot cannot handle errors and direct the user to the correct path, your chatbot is a failure.
This is how Hubspot designed its chatbot in a conversational tone:
4. Entity and Intent Models
Entity and intent models manage utterances. Utterances are nothing but what the user communicates with the chatbot. Usually, chatbot platforms have a simple way of managing entity and intent along with utterances. However, if there is a data scientist in your team, it becomes easier for chatbots to handle a gamut of user inputs. Mainly if the chatbot platform is already operational and the developer team adds skills/apps to the platform.
If you are designing a chatbot architecture for the first time, documentation is crucial. Your front-end and back-end design of the chatbot architecture has to be sound. The front-end of the chatbot is the place where the users converse with the chatbot. The back-end is where various integrations, other system hooks, and web services are pulled to get the desired information.
In this phase, the chatbot is developed, and codes are written. Whatever the requirements and specifications of the chatbot are, here, the same is implemented based on the build and architecture. The citizen engineers work closely with the data scientists to make sure that the entity and intent models are appropriately incorporated.
It would be ideal if the front-end user interface of the chatbot and the back-end integration were worked upon simultaneously. This would allow the features to be integrated and released quickly.
Testing is essential for any development lifecycle. It is essential in the chatbot development lifecycle as well. However, testing a chatbot is not simple. Here, not only should the codes be tested, but the flow of the conversation and the messaging, too, as they are required to be tested. Additionally, there is another complication when your chatbot runs on third-party messaging applications, as each application will have different limitations and guidelines.
The testing of the chatbot must include the quality assurance part, too. The quality assurance part also includes conversation design with the developed chatbot.
You need to check:
- If the bot is working fine without any technical errors
- Does the look and feel of the chatbot match the industry standards?
- Is the conversational workflow moving in the right path?
- Is the bot capable of handling unexpected errors?
- Is there a break in the flow if any unexpected errors occur?
- Can the bot converse and hold the user’s interest till the desired result is received?
- Are all third-party integrations working fine?
- How efficient are the chatbot UI and UX?
- Is it possible to match the conversational tone of the chatbot with the user persona?
- How interesting is the bot journey?
Since this is a time-consuming activity, a part of the bot development cycle needs to integrate the automated testing process to make sure that regression testing happens without manual testing.
After you are done with bot development, now is the time to deploy it in a hosted environment. During the process of deployment, developers ensure that the code is transferred from the testing environment to the production environment.
On the other hand, the solution designer, users, and stakeholders set communication plans and adoption plans to ensure that the audience will know about the chatbot and its features.
Once the deployment process is complete, you will publish the chatbot. If it is a new chatbot, it will have to get approval from the app stores. Submissions to various messaging platforms will need a gamut of documentation, including a short and long description, a logo, videos, images, scripts, etc., in order to get the chatbot approved.
Publishing a new chatbot and subsequent approval can take several days to months. The process takes less time for those chatbots where additional skills/apps are being deployed.
In this phase of the chatbot development lifecycle, both the technical/operational and the conversational side of the development are essential. You have to understand what the users are conversing with the chatbot and how much time is taken for the response. Also, the transaction time, the missed intents, and the error messages that are shown to the users are listed below. These observations will help you generate a list of maintenance and support items and, accordingly, a priority list.
10. Marketing and Adoption
Once your chatbot is ready, it is vital to advertise it. With the help of stakeholder engagement or through an influencer, you need to champion your app/tool for maximum adoption.
If the chatbot developed is for business-to-business, it might be easier to market the tool since the organization’s leaders would help promote the product through various advertisements. If the chatbot is for business-to-consumers, you can do marketing through social media ads and email marketing. Influencer marketing can also help in getting word about the tool/app.
Additionally, if you add push notification or proactive as a skill within your chatbot, you can have your own advertising channel in your platform as you release new skills.
Once your chatbot has been adopted and used, you must track the performance metrics to ensure that all is in line and adoption continues. Never should the performance dwindle. This phase aims to review the conversation logs and review usage metrics. You should also document false positives and understand the missed intents.
Proper evaluation and review of every dialogue would help your team get ideas on the future roadmap. It would also help you make enhancements to the bot to deliver better performance and a better user experience.
12. Repeat the Lifecycle
After evaluating your chatbot, you can cycle back to re-understand the development. Also, make improvements to the chatbot as you revisit the phases. A revisit often helps you identify small factors that otherwise may go unnoticed.
Moreover, you can use self-learning and machine learning programs and platforms to update the chatbot as users continuously use the bot.
How Long Does it Take to Develop a Chatbot?
To develop a simple chatbot with just simple questions and answers would take approximately two weeks. However, creating a chatbot with more than one skill set and capabilities outside the boundaries of simple questions and answers, including switching context, can take several months. This period is taken to ensure that the language processing models embrace most of the roadways that users will commute during a conversation.
Building a bot is simple. Building a good bot is challenging. You need to ensure that your chatbot would enhance your purpose of developing a bot.
When performance is the key, you should always take the help of such a team with expertise in chatbot development and revolutionize how your chatbot interacts with your customers and visitors.
If you need help in developing a custom chatbot solution from scratch, contact our chatbot development experts and get a free quote.
Chatbot Development Lifecycle – Summary
Chatbot development is a complex method. But if you plan it correctly, it becomes a piece of cake. The essential feature in the chatbot development lifecycle is breaking the process into smaller steps and going by them one by one.
You can hire a chatbot development team or use a chatbot builder for creating a chatbot. The idea is to get the best chatbot for your enterprise. That can only happen if there is proper planning and a chatbot roadmap for execution.
Rajamanickam Rajan is the director at Skein Technologies, a leading IT solution company in India. He has 10+ years of experience in developing application using IBM Worklight hybrid, native applications for IOS and Android systems.
The RPA lifecycle is the structure of how automation is delivered and executed. It consists of every one of the phases a bot goes through: from identifying a business process or task to automate through to its deployment as a bot in production and its continuous monitoring thereafter.
This is the development stage where the bot is developed. Given the conversational interface, bot developers will find themselves iterating a lot more between coding and testing than in traditional software development.
Maintenance and bot execution are generally part of this stage. That is the RPA lifecycle in a nutshell for you. Of course, when you go deep into each of the stages discussed above, you will understand the complexity of RPA in Machine Learning.
The discovery phase is the initial phase of the RPA lifecycle. In this phase, the RPA process architect analyzes the requirements of the client. Then it is further decided whether the process can be automated or not.