AI Chatbot Development: a Complete Guide

Every modern business sector is directly or indirectly tied to communication with customers. There are always several links in the sales chain. It usually looks like this:

• Consumer.
• Manager.
• Warehouse employee.
• Logistics provider.

The communication process itself takes place in several formats: audio, video, text messages, or offline communication. All of this wastes company resources and distracts employees from crucial business processes. Virtual assistants and advanced chatbot development technologies are used to reduce the cost of staff and, at the same time, provide consumers with a top-notch service experience.

A modern chatbot is capable of combining several vital functions:

• Sales.
• Cross-selling.
• Communication.
• Analytics.
• Order processing.
• Automation.

The total value of the chatbot industry will reach $142 billion by 2024, and the fastest growing market will be the Far East (70% of the global number of chatbots) and China in particular, with 55% of the industry share (about $80 billion per year).

The high efficiency of chatbot development technologies makes them a significant acquisition for any company engaged in commercial activities or providing services to consumers. And the more progressive the IT solution, the higher the profit.

Chatbot of Today: the Ideal Model and its Components

The traditional notion of a “chatbot” is a set of programmed algorithms for code to respond to user requests. But what happens if you add a little freedom to it? We get a modern progressive AI chatbot that will not only respond to messages but also improve itself through active self-learning. This IT solution requires an operating core and machine learning technology connected to data lakes.

Top platforms for chatbot implementation

Successful businesses try to expand their presence on the Internet. To do this, they must maintain not only a personal brand resource (website, SPA, mobile app) but also build their influence on popular platforms through AI chatbot development. These include:

• Shopify.
• Skype.
• Facebook Messenger.
• Instagram.
• Telegram.
• WhatsApp.
• Twitter.
• WeChat.
• Slack.

Most modern messengers and marketplaces support the introduction of chatbots and give total carte blanche to developers of such software.

AI chatbot features

In order to provide 90% coverage of the communication tasks of the business, it is necessary to implement the company’s key functions in AI chatbot development as much as possible. Depending on the company’s business area, the set of assistant commands can be as large and branched out as desired.

Let’s look at a classic template for the functionality of a “smart” chatbot of a commercial company:

• clarification of assortments by warehouse databases, providers;
• providing product characteristics or features on request;
• comparison of similar items from a company catalog;
• selection of similar items from an assortment;
• forwarding of messages to a manager;
• receipt of purchase requests;
• issuance of payment details;
• shipping arrangements;
• cross-selling offers;
• informing about promotions and discounts;
• announcement of new goods.

Here we can also add various greeting phrases or dialogue wording based on the user’s geolocation, contact information, and other info received by the bot during communication. But this becomes possible only when extending the “intelligence” of the chatbot by introducing modern chatbot development technologies, such as AI, ML, and DL.

From Chatbot to AI: Lifecycle-long Transformation

The utopian image of a chatbot looks like a hologram of an attractive person, able to conduct a constructive dialogue with consumers through their voice. Such a program should analyze the mood, tone, needs, and requests of the user on the fly, process terabytes of information in the flow, connecting to servers with data. As a result, the digital consultant will be able to produce relevant results in a familiar form. But this is only a fantasy, which is not so far from reality.

Modern development languages, frameworks, and technologies, in general, already know how to process human speech. This is made possible by innovations in chatbot development technologies such as NLP and NLU (natural language processing and program perception). Bot development lasts throughout the entire lifecycle of an IT solution: from its planning to its disconnection from the servers.

Benefits of AI Chatbot with NLP and NLU, ML, DL

Every company wants to scale, but not every company succeeds. The secret of successful expansion lies in simple solutions. One of them is a full-featured chatbot with a self-development algorithm.

One of Microsoft’s smart assistant architecture options:

Usually, AI chatbot development technologies such as artificial intelligence, machine learning, and integration with data lakes are at the core. Together with NLP and NLU, this opens up new horizons for improving the communication algorithm. In addition to the classic functionality, the digital assistant can independently analyze the interlocutor and engage in a quite conscious constructive dialogue with them.

The tone of the dialogue

Joke-like phrases, friendly or strict communication – these are the distinctions by which it is easy to identify the interlocutor: a machine or a person. Modern technology blurs this distinction by training programs with innovative text and voice processing algorithms.

This allows the software to be wholly transformed and gives the chatbot unique features. With their help, it can adapt to the style and tone of the conversation, producing authentic phrases and even entire monologues.

Will it have an impact on sales? Definitely yes, and for the better. After all, as research by Insider Intelligence shows, 32% of survey respondents still prefer chatting with managers because the chatbot is not “communicative” enough. That said, 24% say chatbots process requests faster and save significant time in closing deals.

Variability in communication

When a consumer has a non-standard question, they have to go to a manager. The problem is that simple chatbots cannot handle complex or complicated requests. At best, they answer according to a template or redirect the user to a chat with a company representative.

To optimize the communication process, you can include slang phrases in the bot base, build a chain of dependencies and create a query processing architecture. As a result, the conversation will follow a variation scheme with ready-made answer templates for a number of typical questions.

Communication templates

Typically, chatbot algorithms have several options for developing a dialog with a client. These templates work according to a prescribed pattern of phrases and are processed according to parameters such as key phrases or commands prescribed in the bot itself. Modern digital assistants have some semblance of intelligence and can interpret simple communicative language into complex commands. For example, they can compare items, prices, or payment options.

This qualitative algorithm improvement makes it easier for users to communicate with bots. This reduces the time it takes to place an order and its processing in general, which ultimately leads to more sales and brand popularity among consumers.

Adaptability of information output

A chatbot is not just answering yes/no and providing links to relevant products or services. A modern chatbot is a flexible information processing system that takes the automation of commerce or other types of services to a new level!

With AI chatbot development, it is possible to realize virtual assistants competent in solving technical or financial questions. This reduces the cost of company resources for consulting services and redirects them to the organization’s core business processes.

Access to data arrays

Today’s chatbots can work in isolated or shared information repositories, learning or providing enhanced consumer service. Such digital assistants can easily analyze data according to algorithms and research the market or competitors’ solutions. That is why a chatbot is not only a commercial IT product but also a productive operating unit in a company’s staff, which can be used for a number of routine tasks.

How are Chatbots Developed: the Key Components and Subtleties of Creating Smart Assistants

Chatbots can be either created from scratch or implemented on one of the existing platforms. This variability exists for a reason: you need different solutions for different needs. For example, if you need a complex virtual assistant, it’s better to develop it yourself. Still, if you only need basic functionality, you should use a boxed solution with partial customization.

The platform for AI chatbot development (Telegram, Instagram, Facebook, etc.) also plays an important role when choosing key technologies for creating a digital assistant. There are peculiarities of programs and web solutions that need to be studied in advance and considered during development.

Analysis, planning, and design

The first stage of any development is always analytics. Competitors’ solutions, business specifics, the target audience, and their basic habitat platforms need to be thoroughly studied before you start working on the bot. The information obtained will give you a complete picture of the state of the market, trending solutions, and expectations of typical customers in your area of business.

Choosing a platform for creating a chatbot

If you decide to take the path of least resistance, then your choice is a platform for creating chatbots. They allow you to quickly set up communication templates, enter key phrases, connect assortments, etc. The main advantage of such solutions is the speed and cost of development. The disadvantage of ready-made platforms is some limitations. These include weak AI, a simple interface, and basic functionality without the ability to scale.

Choosing the best framework

As of 2023, no development languages or frameworks are designed to create advanced chatbots. Despite this, technologies such as Python, Java, PHP, Ruby, or Lisp are doing a great job. Python has built-in tools for native work with AI and ML, which is a definite plus in the case of intelligent chatbots.

Optimal frameworks for developing chatbots:

• Microsoft Bot Framework;
• Dialogflow;
• IBM Watson Assistant;
• Amazon Lex.

They all have support for NLP and NLU, as well as some features unique to each product. For example, Microsoft Bot Framework supports LUIS speech recognition service, and IBM Watson Assistant features an advanced self-learning algorithm.

Testing

Before releasing a chatbot, it is necessary to test its functionality comprehensively. To do this, you can use the mechanical method (manual or with the help of testers from among the clients) and the automatic one.

By automating the testing of chatbots using frameworks such as Botium, you can not only check the workability of the IT solution but also train it using ML algorithms. You may need the services of experts, such as Glorium Tech, to make the most of automation.

Support

Chatbots need support throughout their lifecycle. This doesn’t just mean fixing problems or implementing new functionality. Firstly, it is necessary for scaling and learning integrated AI. Secondly, to adapt the digital assistant to new market trends, which change so often that sometimes the bot becomes obsolete before the end of development.

To Summarize

Today’s AI chatbots are an example of self-learning algorithms, though very far from the utopian image. Even in this form, they provide better and faster customer service, increasing the quantity and intensity of sales. Now is the best time to launch your brand bot to market and integrate it into all popular messengers and platforms.

Entrust the task of AI chatbot development to industry experts – Glorium Tech. We will create the best virtual assistant on the market for your business and teach it to speak the language of profit. With our service, not a single client of yours will leave empty-handed!

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