Chatbots, virtual assistants, and other sorts of conversational agents are becoming increasingly popular. More and more organizations are prepared to engage in research and development to see what these new technologies can do for them in terms of streamlining processes, increasing efficiencies, and generating a return on investment.

Engineers and developers in most businesses are keen to try out new technologies, especially during the research and development phase of a project. However, ensuring that the team understands the lifetime of a chatbot is critical in order to be effective and scale without going over budget.

Understanding Chatbot Development Lifecycle 

The purpose of this article is not to go into all of the architecture and technology involved in building a chatbot. Rather, it is designed to help you comprehend all of the numerous tactics employed during the various stages of the chatbot development lifecycle.

Each stage is critical. While some organizations may choose to bypass some of these steps in order to move faster, they will discover that they will need to devote some of their time later to resolving issues that arise.

While designing a bot may appear to be simple, ensuring that your bot meets the demands of your audience and stakeholders is the most difficult part. Product owners must recognize the value of devoting time to perfecting the architecture rather than rushing to release feature after feature. 

All parts of the life cycle, including completely testing the dialogue and monitoring its performance, must be appreciated and planned for by developers (like false positives). Users of the bot must be educated and have their expectations set as soon as the bot is made available to them.

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Now, let’s get right into the chatbot development life cycle. 

Requirements for the Chatbot Development Lifecycle

You'll need to figure out who your stakeholders are and who THEIR audiences are throughout the requirements phase. You'll need to understand their expectations for a bot throughout this step. It's important to remember that you won't be getting any solutions at this time. The goal is to have a better understanding of the business process that a new chatbot will be used to improve or optimize.

At this point, the stakeholders are the individuals or groups who will help champion the use of the chatbot.

The majority of chatbot teams overlook the fact that there are users. Ascertain that the stakeholder provides you with a list of potential audiences or users so that you can develop and solve the problem for those end-users who can also address the business challenge for the people or teams.

If your stakeholder is Human Resources, and their issue is that people can't find the onboarding documents they've generated, you'll need to ask them for the end-users who are the most frustrated with the present procedure. After that, you can ask the end-users questions to help you create a self-service system for them.

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In this case, you may discover that Human Resources onboarding materials do not utilize the same vocabulary as employees, making it impossible for anybody to locate the documentation they require. Instead of designing a bot based on the ideas of HR stakeholders, you'll build a better bot from the start if you design with the end-user in mind while also knowing the pain points that Human Resources is aiming to solve.

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Specifications for the Chatbot Development Lifecycle

During this stage, the chatbot product management team's solution designer creates solutions based on the user stories obtained during the requirements phase. A product definition paper should be created at this phase, listing the features and benefits of the chatbot.

You'll go through the same lifecycle for each skill if you already have a chatbot platform and are only adding small skills and apps.

A specification document, for example, is a wiki, intranet, or Microsoft Word document that lists all of the chatbot's features.

Conversational Flow: Chatbot Development Lifecycle

The solution designer usually collaborates with a human factors or user experience designer to create the conversational flow utilizing a workflow-based technology. The team will be able to observe how the discussion dialogue will flow to the end user graphically. Having an engineering technical lead present during these discussions can help ensure that the wireframes and design aren't too far out of reach from a development standpoint, saving time later if re-design is required because something isn't doable.

Within the chatbot, there is also a discussion with the data scientist or whoever is in charge of your natural language processing capabilities. The ability to build with NLP in mind, taking into account utterances, intents, and entities, will also aid in wireframing the discussion flow in a way that allows the chatbot to transition contexts as needed. All workflows must examine how error management will be handled during this step.

Designing a good workflow but recognizing that it is not attainable owing to technical, cost, or scope constraints, we've had to settle for less than the best of designs. To avoid this, make sure you have multiple discussion flows prepared. This will allow you to be more flexible during this phase.

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Entity and Intent Models in Chatbot Development

Entity and intent models must be used to handle utterances (what a user says to a chatbot). Most chatbot platforms, in general, will make it simple to handle these entities, intentions, and utterances. However, if your team has a data scientist on staff, they are most prepared to assist with how the chatbot will handle a wide range of user inputs, especially if the chatbot platform already exists and the development team is simply adding apps/skills to it.

Architecture of the Chatbot Development Lifecycle

When it comes to creating a chatbot for the first time, the architecture and documentation of that architecture are critical. Both the front-end and back-end engineering designs must be sound. The user will view the front-end, which is a conversational interface. The many online services, interfaces, and hooks into other systems that draw back information are referred to as the back-end.

The Development Lifecycle of a Chatbot

The chatbot is developed and code is written during the development phase. Engineers will examine the needs and specifications before constructing a structure based on those designs and specifications. They'll also collaborate closely with data scientists to ensure that the entity and intent models are implemented correctly.

The front-end chatbot user interface and back-end services are frequently developed concurrently, allowing features to be provided more quickly.

Automated Testing is an important part of the chatbot development lifecycle.

Testing is always part of the development lifecycle, but it's highlighted in the chatbot development lifecycle since the way messages are presented varies across platforms and apps. The trick here is to figure out which apps and platforms the chatbot will be available on and advertised for, and to make sure that testing are conducted on each of them.

Because this is a time-consuming operation, being able to build code for automated testing should be part of the bot development lifecycle to ensure that regression testing is carried out without the need for manual testing.

Prior to launching the bot or skill, the stakeholders and a few users they've designated should test it to guarantee that it works.

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Deployment is the final step in the development of a chatbot.

The bot must be deployed through a hosted environment after it has been created, constructed, and tested. The engineers ensure that the code is moved from the testing environment to the production environment during the deployment phase.

Meanwhile, the solution designer, stakeholders, and users are developing adoption and communication strategies to guarantee that the chatbot and its new capabilities are widely known.

The Lifecycle of a Chatbot: Publication

If the bot is a completely new chatbot, it will need to be published to app stores for approval after it has been deployed. To be authorized, submissions to various messaging platforms will need a wide range of documents, including a logo, short and long descriptions, photographs, videos, scripts, and so on.

This can take anywhere from a few days to virtually a few months for a new bot. This would most likely be a quick process for those who are implementing skills on an already approved chatbot.

Monitoring the Chatbot Development Lifecycle

Monitoring the operational/technical side of the house as well as the discussion side of the house is critical during this time.

Understanding what users are asking the bot, how long it takes to respond, how long it takes to complete a transaction, what types of missed intents are most common, and what types of error handling messages are most common can help you create a list of support and maintenance problems to priorities.

Adoption and Marketing of Chatbots in the Development Lifecycle

It is critical to be able to advertise your chatbot. Engaging a stakeholder or an influencer to assist champion the tool can help ensure that your chatbot is accepted.

If your chatbot is for business-to-business, it may be as simple as enlisting the support of the organization's CEO in promoting the bot through their marketing channels.

If your chatbot is for consumers, you'll want to be able to leverage social media ads, email marketing, and influencer marketing to spread the word. Furthermore, if you add proactive or push alerts to your chatbot as a talent, you'll be able to create your own advertising channel directly within your platform as you release new skills.

Evaluation of the Chatbot Development Lifecycle

Key performance metrics must be tracked when your chatbot is adopted and used to ensure that adoption does not diminish and performance does not dwindle.

The purpose of this step is to check over chat logs, usage analytics, document false positives, and figure out what intents were missed.

Your team will be able to get recommendations for the future roadmap as well as simple improvements to the bot for a better user experience throughout the evaluation phase and dialogue review.

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