What is a chatbot and what is it used for? Chatbots are programs that are designed to communicate with humans through text or (sometimes) speech. The human user usually relies on a chat application with a text box to input a question or request, and the chatbot gives a response that simulates how a human might respond to the same query. Chatbots can serve a variety of functions such as:
- Interfaces for accessing information
- Task Automation - Handle repetitive tasks efficiently
- Virtual Assistant - Acts as an AI agent for Sales & Service representatives The sophistication of chatbot responses can vary. Some programs look for specific inputs, like certain words or phrases, and select the best response from a series of scripts. Others can use natural language processing and artificial intelligence to evaluate inputs in-depth and offer more detailed and personalized responses.
Types of chatbots
Chatbots use different approaches to process inputs, provide information and handle different tasks. Though the programs may have specific functions based on a business's needs, most chatbots fit into two categories: rule-based and artificial intelligence (AI). Companies that have predictable and consistent interactions with customers or clients might opt for a rule-based chatbot. This system uses established rules and decision trees to figure out how to respond to customer queries. For example, an airline's rule-based chatbot might answer questions about baggage weight restrictions or flight schedule updates, or a medical clinic might aid patients as they try to find an appointment time. AI-powered chatbots use machine learning (ML) and natural language processing (NLP) to process inputs at a deeper level. They use algorithms to try to understand user questions and context and respond to specific details within their questions more flexibly and dynamically. For example, Virtual assistants or Agents help call center agents with dynamic responses to questions from the customer on specific plans, promos, or products.
How do chatbots work?
While all chatbots have some similarities, they work in different ways depending on their design. Whether rule-based or reliant on AI, they follow a similar basic approach: analyzing user inputs and using this analysis to select the appropriate response. The programs rely on decision trees to map out conversations and find the best responses to each query in the interaction. Rule-based models use a collection of scripted responses to answer customer queries throughout the conversation, and NLP-powered chatbots calculate the responses using real-time data they've collected throughout the interaction. In other words, an NLP application fine-tunes responses based on past data it has collected. While basic chatbots are sufficient and efficient for answering frequently asked questions, NLP and other AI/ machine learning (ML) models are becoming more common as their level of sophistication increases. AI-based chatbots can help improve customer satisfaction by providing specific responses using the knowledge base in close to real-time while it would have needed a service ticket to get responses without the AI chatbot.
Understanding natural language processing
NLP plays a critical role in enabling chatbots to handle more complex interactions. The programs use algorithms to continuously analyze and collect data on the conversation. This information helps the chatbot grasp the context and nuances of the conversation better than a rule-based model. For example, an e-commerce customer might ask a website chatbot, "Are these blue shoes available in my size?" The chatbot can sift through past interactions and data to find the user's shoe size and look at their browsing history to find out which page they are looking at. The chatbot may also analyze data to find similar shoes to recommend. Some algorithms track users' uses of idioms and various expressions to enhance their understanding.
The role of machine learning in chatbots
Developers can use machine learning to train chatbots. Training involves showing machine learning models a wide range of data sets so that they can collect information and gain an understanding of the best responses in a wide range of interactions. This in-depth analysis allows models to fine-tune responses based on subtle nuances in each conversation. As it is exposed to more of these nuances, the chatbot will become more adept at handling complex questions and be better able to deal with common chatbot problems, like understanding idioms, regional slang or sarcasm, customer emotions and near real time sentiment analysis.
Professional applications of chatbots
Chatbots are useful in a wide range of sectors. In addition to basic functions, businesses can combine them with advanced ML models and AI-powered pattern recognition for increased accuracy. For example, banks deploy chatbots to answer customer queries about account balances and transactions. The healthcare industry is also embracing chatbots, with AI virtual assistants guiding patients through a triage process. In both these instances, accuracy is essential to provide proper services. Chatbots are very common for customer service applications, where they answer basic questions and collect initial information to better route customer queries. AI-enhanced chatbots can also be customized to automate marketing tasks and handle basic sales interactions.
Chatbots in customer service
Chatbots can provide customer service 24/7. They can offer an immediate response to customer queries in seconds without requiring users to wait on hold for a human agent or email response. Also, chatbots can handle many routine tasks or interactions without human intervention. Many customers can get what they need from the apps without waiting on hold, enhancing the customer experience and reducing churn. With chatbots handling simple but time-consuming tasks, human customer service teams can focus on dealing with complex issues.
Chatbots for sales and marketing
Sales and marketing chatbots can help personalize experiences for customers. By analyzing customer data and past interactions, these programs can tailor product recommendations, suggest relevant promotions and answer questions about promotions or products. This personalized approach fosters a strong connection with customers, making them feel valued and understood. These chatbots also help sales and marketing leaders with guided selling and marketing by facilitating information on the customer's sentiments, customer browsing, and other competitors. Chatbots also can generate sales pitch decks and schedule meeting invites reducing the delay caused by manually creating and converting leads. Chatbots can give personalized responses and can guide customers through various stages of the sales process, freeing human sales reps to answer advanced queries or, in some industries, close sales.
Personal applications of chatbots
Chatbots can benefit businesses, but they also have personal uses. Here are some of the most common examples of such personal applications:
- Provide information, such as news headlines or weather reports
- Control household functions, such as lights or speaker volume
- Offer personal recommendations for entertainment Chatbots can also be developed to offer support and information for specific tasks, such as exercise, diet or hygiene.
Benefits of implementing chatbots
Efficiency is a major advantage of chatbots. They can answer queries and perform basic functions automatically and quickly, reducing customer wait times and reducing employee hours spent dealing with simple tasks. With mundane processes automated, employees are free to handle tasks that have a larger impact on the company's bottom line. Finally, chatbots are highly scalable. They can handle customer interactions in the same way, regardless of volume or the size of the company. As a company grows or its traffic increases, it can still rely on the same chatbots to automate interactions.
Challenges and considerations
While they provide benefits, current chatbot capabilities have limitations. A National Science Foundation-funded study found that even advanced ML chatbots got fooled by queries with mistakes or mistook nonsensical sentences for actual queries. In a commercial setting, such problems can lead to user frustration. Chatbots' inability to gauge frustration and other emotions can further exasperate customers. Because of these possible shortcomings, humans still need to test and monitor chatbots to ensure they provide the necessary functions.
Concerns about chatbots
The increasing use of chatbots has caused worry in several areas. The biggest concerns have to do with privacy, security and information access. Here's a closer look at these issues.
- Students and employees can use advanced chatbots to access information and present it as their own work, potentially influencing their grades or work performance.
- Chatbots can potentially collect sensitive information from users. For instance, a health chatbot might ask for private medical details.
- Chatbots need to meet strict cybersecurity standards if they collect sensitive data. If they do not, they could be a target for data breaches. Companies can help manage security and privacy issues with data encryption and by testing the programs to ensure they do not ask for unnecessary private information. However, these companies will have to apply guardrails, both at the data and operational level, for example, by monitoring conversations between the customer and bots.
Flaws in chatbots
Other concerns and challenges are related to the flaws inherent in today's chatbots. Here is a look at some common problems for the programs.
- A lack of emotional intelligence means chatbots can't read feelings or offer a personal touch when needed.
- Biases of developers or ML trainers could limit the program's effectiveness for certain demographics or understanding of regional dialects.
- Unexpected questions or changes in the subject could confuse chatbots.
- Maintaining the context across conversations
- Switching from generic responses to specific responses when the questions from customers swap from generic to specific questions. While ML chatbots can improve by crunching data related to these flaws, they currently lack the intuitive communication abilities of humans.
Limitations of chatbots
Chatbots are effective within the context for which they were designed. For instance, a chatbot developed to help customers with IT troubleshooting will be able to offer tips for dealing with common problems. However, these programs may struggle to understand the nuances and context of interactions with users. Even advanced NLP algorithms may find it hard to detect and respond to frustration or sarcasm, which would immediately be obvious to humans. As AI and NLP advance, developers are trying to address these shortcomings during design and development.
Designing and deploying a chatbot
Chatbots are most effective when tailored for a specific purpose. The first step in designing these applications is to define the purpose and the characteristics of the audience interacting with the program. These pre-development definitions are also important for selecting between simpler rule-based programs and more sophisticated AI software. Regardless of this choice, the user experience (UX) must meet customers' expectations. One way to achieve this is to continuously test the chatbot and collect feedback to continue perfecting the program so that it delivers the UX the audience expects.
Future of AI chatbots
Advances in AI and natural language processing have already led to more sophisticated chatbots capable of human-like interactions. Chatbots are increasingly capable of understanding natural language, which will allow them to understand the broader context of conversations and tailor their outputs to include these big-picture insights. This will help them to produce more relevant information and personalize responses. NLP advances could also include capabilities like identifying tone and emotion based on word choice and other factors. Ethics and bias are also important aspects of the future development of chatbots. Developers need to find ways to protect their intellectual property, such as proprietary algorithms, while also providing transparency so that third parties can verify that chatbots are unbiased and not engaged in potentially unethical data collection or breaches of privacy. With their current and future capabilities, chatbots will continue to be an integral part of companies' IT setup, alongside state-of-the-art connections, cybersecurity and hardware. Advances in AI and NLP ensure chatbots will continue to play an integral role in these business functions in the future.
