Chatbots have been declared one of the best web development trends of 2020. Regardless of the fact they have been in existence for several years now, their potential is still unfolding. Nowadays, to stay afloat, companies can’t but always evolve by adopting new trends. Fortunately, there are numerous compelling examples of how chatbots can reap several types of companies. Thus, it’s no surprise that these conversational agents wind up being the technology a growing number of businesses are ready to implement.
In this guide, we will provide an extensive guide to chatbot development. We are going to discuss in detail exactly what a chatbot is, what kinds of chatbots are there available, and why a business should consider implementing this technology. We will also break down a chatbot development process into successive activities and how exactly one should take them to succeed.
Chatbots are artificial intelligence human-computer conversation systems that are based on natural language processing and, thus, can behave in a human-like method. These days, these interactive applications platforms are able to reside in apps, live chat, email, and SMS. The first conceptualization of chatbots goes back to the 1950s, but their adoption radically accelerated after the chatbot platforms’ launch by Facebook, Skype, WeChat, together with other notable industry players.
Currently, there are lots of specific features that create a chatbot a useful and effective tool in your business toolbox:
Natural language understanding and responses
User-friendly task-oriented functionality
BI and analytics
Multiple languages support
With that said, what every chatbot should effectively mimic the human-human interaction, to begin with, is natural language processing (NLP.)
NLP is a technology that permits chatbots to become more humane than they used to if their job was to replicate shared customer care scripts. In order to understand the user input, the chatbot must convert the unstructured conversational human language to coordinated information that computers will be able to interpret. This interpretation is made possible through the process known as natural language understanding (NLU.) The user sends a message to the bot, then it is processed by the algorithms trained to extract the meaning. This is a more complex and match perspective on how chatbot using NLP works:
A chatbot with NLP is capable of recognizing the significance and context of consumer input and, finally, the users’ intents. By building an NLP model, you expand the variety of your own chatbot’s possibilities. The user demands are getting to be just high, so a chatbot that can not offer you the value of Natural Language Processing might have no significance at all to get a few groups of people.
NLP-powered chatbots are a prime example of automation technologies. And even though chatbots are rather complex to grow, you’ll see an increasing number of business leaders and decision-makers turning into the technologies in an aspiration to grow their sales, marketing, and customer care. What is more, the chatbot market in 2018 has been valued at $1.17 billion and is predicted to reach around $10.08 billion by 2026, which implies the compound annual growth rate is predicted to be 30.9%. Fascinating, isn’t it? And there are some persuasive reasons why the demand keeps increasing and the main reason businesses, in response to this requirement, are readily developing advanced chatbots.
For simplification purposes, most categories single two main chatbot types: pattern-based chatbots and learning-based AI chatbots. The former is more primitive, while the latter is more complex and complex.
Early chatbots were the chatbots using pattern matching for text classification and reaction reproduction. ELIZA was the first chatbot of this kind released as early as 1966. Basically, such chatbots are made to follow conversation decision trees, which makes their replies predictable, repetitive, and deprived of the human touch. Ordinarily, a bot asks a user a question, and the answer is chosen from the available alternatives, or it has to contain a specific keyword explicitly fitting exactly what the bot was educated on so that the dialogue could move. Such chatbots are accurate only when the user input is exactly what the bot has been trained to answer. Pattern-based chatbots also don’t store previous replies, so the conversation can easily reach a deadlock.
AI-based chatbots are a lot more successful as they use the power of ML not merely to match the output with the user input but also to understand, contextualize, and prediction. This is the kind of chatbots that is nowadays utilised to effectively optimize the job of sales representatives, customer support, that is used in private aid, and much more. The algorithms in AI-based chatbots are trained with historical data from actual user responses. As a consequence of their capacity to comprehend the context of a message, they could more naturally engage in a dialogue without being explicitly trained and, consequently, can be further improved through ongoing consumer feedback.
However, all chatbots could be further classified into smaller groups with different parameters:
Open-domain and closed-domain chatbots – people who can respond appropriately, discussing general topics or are concentrated on a particular knowledge domain.
Chat-based and task-based chatbots – people who speak to an individual or assist them perform work.
Chatbots developed with open-source and closed platforms – people that are made using proprietary or open source code.
Societal , intrapersonal, and inter-agent chatbots – the chatbots that get and pass information without close sentimental proximity to the customer; the customers’ companions that understand them as another person would do; and the chatbots that can communicate seamlessly with one another.
Chatbots developed with rule-based, retrieval-based, and generative versions – the chatbot we earlier called pattern-based, it selects the answer based on a fixed predefined set of principles; the chatbot that uses APIs to retrieve answers from various sources; and the chatbot that uses ML and deep learning to produce responses based on the previous user inputs.
Why your business requires a chatbot
Firms are beginning to see the benefits of using chatbots for their consumer-facing products. According to a survey by Oracle, when asked which emerging technologies they think will improve their customer service most, 80 percent of brands surveyed said they had implemented or planned to start using chatbots by 2020. It is principally because these companies have concluded that the benefits of chatbots are worth investing in their own development. Thus, whether you want your own chatbot or not will depend entirely on your willingness and readiness to invest in and work towards the following positive outcomes:
It is possible to use a chatbot to turn your brand interactive and accessible 24/7. With chatbots, your customers will be more prone to turn into consistent and immediate answers to the questions they would have. This responsiveness allows for superior customer experience, improved customer satisfaction, and improved customer participation. Immediate messaging is one of the best ways to find reliable info, and chatbots can dramatically reduce or even eliminate wait time for the next available operator, allowing customers to get swift and reliable responses even through after-hours.
These chatbot-provided answers will most likely be customized and listed. Recordability, then, can benefit both a business and a customer. Upon the client’s approval, the business will discover some important insights, while the customer will be able to take screenshots of a conversation to act upon them later on.
A deeper understanding of your customers
Chatbots have benefits to offer to both companies and customers beyond cost savings. Thus, by means of example, companies that have implemented chatbots can locate a better comprehension of customers’ needs and desires. But this may only be possible when a chatbot is complex enough to understand and serve client inquiries perfectly.
Specifically, the recordability we’ve mentioned in the previous stage is instrumental in obtaining actionable insights. Chatbots can capture the needed information not simply to respond immediately and correctly but also provide your firm with detailed records of customers’ pain points. This information could be used to handle them one by one in the future. A chatbot is the best tool for learning about customer expectations. It takes some time to train it to get the required data. But once this stage is completed, you will be able to get feedback through customer-chatbot interactions and plan your organizational improvements accordingly.
Optimized backend surgeries
Not only does a chatbot replace effective human support when it can not possibly be provided, but also conversational AI can further enhance customer support capacity. It is accomplished through the automation of high-volume customer relations, allowing a more consistent information flow and improvement of human support staff.
It’s been repeatedly demonstrated that client service functionality gets better when augmented by AI. Continuously active chatbot platforms can help companies become improved operational effects, for example shorter wait times that we’ve previously mentioned, institution of better methods for app or website users to find information they desire, or automation of leads acquisition. Especially, chatbots can help mine customer information and drive new prospects by harvesting prospects’ email addresses or prompting them to offer feedback.
Possible multiple economies brought on by chatbot implementation is only one strong argument that firms should recall.
Conversational marketing solutions are reported to improve a customer travel saving time and money.
Although complete automation of the customer care and sales workforce is not attainable, you’re going to attain appreciable savings even partially automating these positions. By placing chatbots responsible for customer conversations, you can save your employees’ labor costs and devote these resources better. Thus, according to Public Tableau, 36 percent of sales agents’ and 29 percent of customer service representatives’ work time and effort from the usa are automatable, which means this work can be automated through chatbots and other tech creations.
Having said that, we can not deny the implementation of a chatbot requires initial investments. But, except for this, chatbot maintenance extra costs will be relatively low. And given that chatbots will also drive savings on client care department labor costs, the technology adoption promises the conversational-AI-powered businesses a dramatic drop in their everyday expenses in the long run.
If you are feeling confident that custom chatbot development is the perfect thing to do, let’s now find how to commit management time and engineering effort best in order to build a chatbot solution perfect for you and your organization.
The question that often arises when an organization arrives at the idea of chatbot development is what exactly they have to do and in what order to turn this idea into a real feature. For your convenience, we’ve prepared a step-by-step guide regarding the best way best to make a chatbot. Let us examine each of the seven stages — by choosing the chatbot type to chatbot setup and maintenance.
1. ) Choose the perfect Sort of chatbot for your organization
Earlier in the report, we’ve discussed what chatbot types are there and briefly described the differences between those. So, identifying which is excellent for you need to be the first step on your chatbot development process.
Basically, here, your potential bot conceptualization begins. As different chatbots will require unique approaches to understanding a user query, it’s a must for your company to define from the start what you want to achieve with the chatbot. Today’s hottest chatbot use cases are customer sales and service, so make the objective definition your starting point. Then try to define what specific positive results that the consumer is expected to obtain through using a chatbot, what your chatbot strategy is going to be, and what the bot will have to do to assist the consumer achieve the desired outcome. Could it be a FAQ bot bringing answers to clients’ questions or the 1 gathering data, providing consultations, and even entirely substituting human customer support or sales representative?
Your organization should specify which kind of chatbot you will start developing based on your business goals and customers’ demands. When it is clear what your chatbot would do, it’s also going to be troublesome to experience the rest of the stages.
2. ) Pick a communication channel
Following the conceptualization phase is completed, you have to proceed to choose a suitable communication channel. The moderate the chatbot uses is another important element to take into account.
Chatbot reflects the brand identity. Therefore, you should select the channel that will seamlessly complement your brand experience. In any case, the use of different media will highlight different aspects of the service. Thus, keeping in mind All the insights you have obtained when taking the previous step, you need to further clarify these points:
Voice-based or text-based communication
The way your chatbot is discovered and supported
The Kind of connection between the customer and the bot
The target audience of your posts
Whether or not a conversation with a bot is switchable to a human
This information has to supply you a better idea of how people will interact with your chat bot. In order to create an adventure that converts, you will need to understand what user needs you need to meet and what opinion you want to capitalize on during the interaction. You’ll have to discover the very best way for people to locate your chatbot and reach out to you. Then pick the most acceptable deployment station — a web widget on your website, messaging apps like Facebook Messenger or Telegram, cloud networks, SMS, or email.
Chatbots are flexible enough to integrate with several platforms but creating your own chat bot hosted on your site or as a standalone mobile app has its own perks.
Besides, selecting a multi-channel plan, you have the ability to bring even more benefits of a chatbot to the table. The only problem is that you should rather use more or less the exact same technology stack across the platforms.
It’s not an very simple job to select technology for automating human conversations. However, it’s been some time since chatbots took off, so the development stack has, such as AI and ML technologies themselves, has evolved to become more recognized.
To begin with, any chatbot service is powered by rules and workflows automated using a chatbot interface. Therefore, the chatbot ecosystem is still quickly expanding over the last few years to now enable interaction with an individual utilizing technical tried and tested chatbot development tools and chatbot development platforms.
This ecosystem of the underlying technology and platforms is composed of installation stations, third party chatbots, technology enabling chatbot development (APIs, NLP platforms, etc.,) and native robots.
For example, the basic technologies and components of the chatbot ecosystem were categorized by O’Reilly as after:
4. ) Layout the dialogue
All the work that has been done up to this point will most likely be moot if you’re unable to generate a smooth chatbot conversation stream. Normally, the main objective of chatbot growth is customer support optimization. In light of this, you want to be cautious that the development of queries and replies in a chatbot-human conversation is straightforward.
Making your chatbot character might become the first step towards designing an superb conversation. Giving your bot a name and a tone of voice when writing a script that flows is an important part of the design process. If you’ve done all the preparations well and defined how customers will interact with the сhatbot, then it will be easier to align interactions with the brand identity you’ve come up with.
Just the bot that talks and performs well can relieve the workload of your customer support representatives and enhance brand awareness. These tips should help you make it so:
Begin with the most necessary elements of your chatbot conversation
First, write the main flow to your end-to-end perfect experience
Insert as many appropriate offshoot flows to the flow map as possible
Give your bot a personality together with a tone of voice
Create a thorough conversation diagram: greeting, asking, notifying, clarifying, apologizing, signaling, failing to understand, and completing the dialogue
Keep your bot’s purpose in mind when composing conversation scenarios
Teach your chat bot and analyze its dialogue flow
5. Train your chatbot
Obviously, it’s challenging to predict all the questions coming to the chatbot. Because of this, once the conversation scenarios are ready, it’s time to train the chatbot. It will be rewarding to stop imagining what the customers will say or write and instead start using the data you will need to train your bot.
For many businesses, a chatbot that’s based on a script and an API link to a website will suffice. But if you would like your chatbot to perform a wide range of tasks in a fashion that is cohesive, it must be trained. So, how exactly are chatbots trained, and how much training is adequate?
To set your dialog flow to confirm and evaluate if your chatbot does exactly what its designers intended, you can do it using a model or a production-ready chatbot. Irrespective of which option you choose, you will find just two plenty of approaches to assess your bot before it is deployed and released.
To start with, the chatbot could be examined manually. By means of example, you can start it in Messenger and start analyzing the bot’s behaviour during the conversation flow by sending different queries intended to create the chatbot respond in a specific way. Additionally it’s important to check such regions of the workflow as intent fitting, fallbacks, navigational scenarios, tone of voice, entity recognition, and consumer’s request gratification. The next measures are usability testing and user feedback acquisition. The moment you are happy with the experience, it’s a wonderful idea to start assessing the chatbot with a small group of customers and keep scaling up until the merchandise is available to everyone.
7. Deploy and maintain
As soon as you’ve successfully completed all the preceding steps, you are all set to set up and launch your own chatbot. Even though you ought to be certain the chatbot experience will be satisfying and enjoyable for clients, actually, the continuing journey of optimizing quality just begins. After you have found your chatbot’s voice, the possibilities for advancement are infinite.
To help the company reach its goals, the chatbot must keep progressing. There are tons of useful best practices that will help you perfect the quality of your chat bot. Amongst others, they include best practices for change management (which is inevitable when designing a chatbot,) source code management, and automation (automated testing, deployments, and even more.)
Chatbots are becoming instrumental in helping businesses reach out to broader audiences and more efficiently serve their requirements. They are at the heart of AI technology symbiosis with the business world, minimizing human interference in new experiences.
At the same time, chatbots have the capability to develop into a capable information-gathering tool. Their implementation on your company’s processes promises significant savings in customer sales and support operations. And the standard of chatbot interactions is only likely to grow with AI and ML advancement.
►►► ConnectPOS is a cloud-based POS software compatible with multiple platforms including Magento, Shopify & Shopify Plus, and BigCommerce. ConnectPOS is the first product in the ecosystem, making transactions in physical stores become easy and automatic. It enables consumers to click and collect, synchronize information across platforms and devices and have a seamless shopping experience. It also helps retailers digitalize customer behaviors, track data in real time at customer touchpoints and provide intelligent business recommendations.