A chatbot can cut your operational cost by 30%. It explains why companies are rushing to incorporate Chatbots into their operations.
Although building a chatbot will demand a comprehensive understanding and utilization of data science, it will test your systems and their ability to meet customer needs. It is also a chance to evaluate your systems, especially their digital-worthiness.
What Chatbots do for businesses
Chatbots account for the fastest-growing communication channels for businesses. Since 2019, the uptake of chatbots has grown by 92%. 24% of buyers used chatbots in 2020 compared to 13% in 2019.
Such figures indicate a doubling uptake rate. You can buy a college paper from experts online to make your work easier. The figures also show that customers are embracing the technology despite its challenges.
What are businesses doing with these chatbots or what potential do the chatbots portend for businesses?
- Engage customers- customers ask the same questions over and over. AI helps to customize the answers instead of having a customer care representative handling the same issue over and over.
- Cover for absent personnel- it is not always that your customer care representative will be available to answer questions. In the night, over weekends, during holidays, and such other moments, chatbots step in to cover for information deficit.
The customer does not experience the void, boosting your business profile in the process. For instance, 43% of customers are comfortable making reservations via chatbots anytime of the day or night.
- Enhance consistency- chatbots use AI to generate answers. If customers ask a question, the bot generates the same answer. Such consistency will enhance the profile of your brand, especially eliminating the hitches associated with human error.
- Save time and money- Chatbots use AI and do not require any human intervention. It cuts the cost of running your business by 30% since you do not employ personnel or facilitate their continued stay in your organization.
No employee will require overtime or a night shift allowance for answering customer queries in the middle of the night. A chatbot will, therefore, save time and money.
- Collect data- chatbots collect data that will help you to serve your customers better in the future. The collection process is so seamless that the customers do not notice. The data improves your delivery, resulting in better brand positioning.
Businesses are embracing many other benefits over the years. As the technology improves, the bots are taking more roles.
Requirements for building a Chatbot
Chatbots integrate the use of AI. You have to thoroughly scrutinize your internal operations, especially the elements facing clients. It is the clients that engage these bots by asking questions and providing information.
A chatbot depends on the incorporation of AI into your systems. You need several pre-trained tools to actualize the dream of an AI chatbot system. One of the most important aspects is speech recognition.
Speech recognition comes in two formats. It involves audible language and written language progressions. The AI system can recognize and decode the audible language when the bot or customer speaks.
On the other hand, it can recognize written language progressions to provide answers. It has given rise to Natural Language Processing technology or NLP.
Natural Language Processing or NLP
Natural Language Processing is a prerequisite of any AI chatbot system. NLP enables algorithms and computers to understand how humans interact through different languages.
The AI system will need to process multiple languages and the complicated nuances while maintaining the highest level of accuracy. It must understand the undertones used in human languages and how they elicit the need for different responses.
A chatbot will be effective and accurate if it can process language consistently and accurately. The conversations are either audio or text.
The customer should never feel as though he is chatting with a bot.Instead, the responses should be natural and seamless such that he does not miss interacting with human beings. The speed and accuracy of interaction cause customers to prefer chatbots over human agents.
Natural Language Processing system technology converts human speech or text into computer-actionable language. It uses rule-based modeling and computational linguistics to understand the logic of human interactions.
NLP requires intelligent algorithms like deep learning, statistics, and machine learning to create smart voices as well as responses that can be used in daily dialogues.
NLP must understand the human errors, differences, and intonations that characterize natural interactions. All this processing happens at breathtaking speed to enable the bots to process large volumes of data.
Some of the real-life examples of NLP at work include GPS Navigation, virtual assistants, and speech-to-text apps. Today, the technology is used to monitor productivity, streamline processes, and offer sales as well as after-sale services.
NLP tasks are responsible for breaking down human activities and interactions into a language that computers can decode and respond to. The Tasks must decode audio and text formats to be subjected to algorithms. Here are some of the tasks involved in NLP data ingestion.
- Speech recognition– speech-to-text conversion or speech recognition happens at the initial stages of AI processing. Some of the sub-processes include grammatical or speech tagging.
It means breaking down the speech and assigning recognizable codes understandable to the AI system. It must consider the accent, implied context, and speech definition point.
- Word sense disambiguation– human speech uses words with multiple meanings. The AI systems must analyze the context of a word to determine the specific meaning to take in each context. For instance, the disambiguation process will help the bot to determine whether a word is a verb or a pronoun.
- Named Entity Recognition or NEM- the system identifies the useful words in sentences. For instance, it can isolate the name of a person from that of a nation. It requires the input or linkage to dictionaries that guide technology and word recognition.
- Sentiment Analysis- the system must understand the undertones and sentiments to accurately decode the message. The sentiments carry such undertones as sarcasm, joy, fear, and attitude. These are the most difficult aspects to capture in an AI chatbot system.
It takes software to understand languages. The level of accuracy gives confidence to users that the chatbot will answer their questions. A diligent developer will produce the most consistent and accurate chatbot.
Types of chatbots
Diverse business environments and needs result in the development of dynamic chatbots. However, chatbots can be divided into two main categories, based on their operation mechanism. NLP is a major element when determining the type of chatbot to develop.
- Scripted Chatbot
As the name suggests, the bots work with predetermined answers. Once you type your question, the bot will respond using a scripted answer. The answers and questions are already stored in the library.
The chatbots work in situations requiring limited responses. They do not give room to NLP Tasks to analyze the nuances of context. The chatbots do not integrate AI technology, leaving them to only deal with limited issues.
The Scripted chatbots are the predecessors in the chatbot industry. They account for 87.2% neutral to a positive rating in customer care.
- Artificially intelligent chatbots
They form a part of the next-generation chatbots. The chatbots are created to mimic human language usage. The bots do not come with scripted responses.
The NLP or Natural Language Processing capability helps them to generate answers based on contextual and language analysis. Interestingly, the bots have achieved an 87.5% success rate, making them the preferred mode of answering customer queries.
AI chatbots are increasingly taking over customer care and sales positions. As technology improves, the bots become increasingly accurate. It is one of the ways to win the confidence of their users and increase deployment.
Challenges with AI Chatbots
The computation power of machines today remains commendable. The speed has further been devolved to phones and tablets, enabling the end-user to enjoy this incredible technology.
Through such advancements, a lot of human interaction needs have been met. However, there are still challenges that this technology needs to overcome.
Human interaction and communication is not straightforward affair. While AI bots report one of the highest satisfaction rates, accuracy in capturing nuances and attitudes remains a huge challenge. It remains difficult to create a perfect chatbot that will understand the differences in language respond accurately.
The evolution of chatbots needs to address the challenges emanating from such inaccuracies. The challenges chatbots face will give you an idea of the technological and algorithmic hurdles that developers face in the course of development.
- Slang, homonyms, and synonyms
- Punctuation rules
- Speech differences in nuances, dialects, and accents.
The human brain can process these challenges very easily. However, training the AI bot to understand and decode them is a huge challenge.
These challenges can only compare to learning a new language from scratch. It will take time and a lot of effort to catch up with the human brain through AI.
Available Materials for Chatbot development
The development of a chatbot does not have to take place from scratch. A lot of technology developers have created numerous platforms and interfaces to aid in the process.
Once you know the level of granularity you need for your chatbot, you can assemble the most sophisticated bot for your work. Here are components already available for you to develop chatbots.
- The Framework- it is a platform that helps you to choose the response the bot gives to your client. Some of the developers with these platforms include Amazon Lex, Rasa, and Dialogue Flow by Google.
These systems have a higher API flow. It means that you require less work compared to developing your system from scratch using Python.
The developers have already done a lot of the background work. It saves you time but does not give a lot of room for customization.
- Dialogue Management- the component requires a bit of customization. It determines the answers sent to clients based on the queries raised. You can ask for information that will help your bot to provide better or more personalized answers.
- Development interface- 68% of customers prefer bots because of the quick response. This is the component that has brought many of them to love AI chatbots. It is incorporated into messaging apps with unique APIs to meet diverse customer needs.
The development interface is the most common component of WhatsApp Business and is making the app more popular by the day. It is also deployed on business websites and social media accounts. Facebook alone has more than 300,000 chatbots serving different needs.
Defining the goal of a chatbot framework
Having decided to develop your framework, you must decide the role you will assign it. This role will determine the features and the character the framework will adapt to. The role is probably the first element to figure out if you have to produce a functional chatbot.
Here are two functions the framework will perform, helping you to define your goal during development.
- Entity Extraction
Entities are categories already defined by your organization or business. They include the name of the organization, expressions of time, quantity, and such general groups that define interactions. They act as gateways defining the mode of interaction henceforth.
Each chatbot comes with different entities. An example is a pizza delivery chatbot which would capture the name of the pizzas, sizes, and delivery locations. You would, therefore, find entities like Peperoni, Hawaii, and cheese, among others.
Such categories are crucial aspects of visualization that will make your chatbot more accurate when handling customer queries. The entities indicate other categories falling under them.
- Intent Classification
The components decipher what the customer wants from the main category. They help the bot to figure out whether the customer is saluting you, ordering, inquiring about a previous transaction, or updating his profile, among other aspects.
The intent classification depends on the utterances given by the client. For instance, it will recognize Hi! as a greeting, I forgot my login details as an inquiry for help, and Can I speak to XYZ as a request for a customer care representative, among other classifications.
Intent and entities help you to understand customer inquiries. If you have to give the right answers, your intentions must be sent to the right entities. You must granulate your intention accurately to direct the customer to the right pocket for answers. With a response rate of 35%-40%, you need the highest level of accuracy.
Developing a Chatbot, step-by-step
The steps in the development of a chatbot are not done linearly. For instance, you will be processing data throughout the development process to make your chatbot more effective.
You will also be generating and looking for data throughout the process. Further, there is not completing a chatbot. You will constantly improve the chatbot based on customer feedback.
- Process data to guide your development
The process involves understanding your data flow and procedures. What information will go into the chatbot and what questions can it answer? What scenarios can your chatbot handle and what kind of information will be required to enable it to handle these scenarios?
Each scenario is built with a sizable number of examples. The examples should be natural and guided by the intentions. If you have no data to use, it is time to hit the field and generate it.
You may also ask people interacting with your business to guide you on the areas that can successfully be handled by a bot. The Data Processing phase should help you to get as close as possible to human interactions. It is a modeling phase where you develop the concept and generates a pipeline to guide your development process.
- Generate and sort the data
Data, in this case, is viewed from the IT perspective. You need encoding techniques to generate data that will guide your bot in making decisions. The data you incorporate into your chatbot will determine its effectiveness and role.
For instance, abandoned cart data has helped businesses to raise their revenues by 7%-25%. Such statistics mean that adding the right data will deliver the perfect results.
- Model your chatbot
What type of chatbot would you like to develop? Can you draw its operational chart? Can you see its place in your organization and the solution it will provide?
Modeling helps you to visualize the intentions and classifications. You will figure out the place of everyone in the business and operations affected by the chatbot.
Test the models to see whether they cater to all imagined needs from your operations. You may start with a few tasks before graduating based on the effectiveness of the chatbot system.
- Test and tighten your chatbot system
Create exploration avenues for your keywords and the model parameters. It is a chance to test the actual words and responses your bot will give.
Notice that 85% of customer interactions will be devoid of human contact. The chatbot must, therefore, achieve the highest accuracy level.
It takes intense testing and adjustments at the iteration phase to achieve this level of accuracy.
- Develop and deploy the chatbot
Having ascertained that the chatbot will deliver to your expectations, it is time to develop the system fully. It will be a part of your overall business strategy.
Development requires you to build a UI for EVE. It is an ongoing process with a lot of testing and adjustments.
Chatbots are expected to cost businesses more than $8 billion in 2022. However, they will result in up to 30% saving in operational costs. Identify the tasks that can be achieved using chatbots and incorporate this technology into your daily operations.