Businesses are constantly looking for opportunities to connect with customers and ensure smooth communication. With the increasing need of developing more prominent communication platforms, conversational chatbots are becoming companies’ priority.
The prime use of chatbot is to resolve customer queries, offering solutions to complex processes, and sharing relevant recommendations. But adopting smart chatbots that can understand human needs precisely is important and lead to the rise of NLP chatbots.
What is NLP? Natural language processing (NLP), in the simplest terms, refers to a behavioural technology that empowers AI to interact with humans using natural language. The aim is to read, decipher, understand, and analyse human languages to create valuable outcomes.
Let us briefly look at what NLP chatbot is and its benefits to understand better.
What is an NLP Chatbot?
NLP or Natural Language Processing is based on the deep learning concept that empowers the bots to decode the meaning from the input. The NLP chatbot utilises feedback to analyse the actual query of the customer and intent to offer more personalised and valuable support.
Adweek suggests that 68% of the customers prefer conversational chatbot with personalised marketing and NLP chatbots as the best way to stay connected with the business.
The key feature of the NLP chatbot is that one can ask questions in different ways rather than just following the keywords offered. Companies can train their AI-powered chatbot to understand a wide range of questions. These are queries that a system will receive from customers, thus offering a broader basis to access the queries effectively.
Key differences between NLP chatbot and rule-based chatbot
The rule-based chatbot is one of the modest and primary types of chatbots that communicate on the pre-set rules. However, customers are looking for a more interactive chatbot to engage with a business.
To improve the customer experience and connectivity, NLP chatbots are great tools for customer engagement.
According to Gartner, 15% of the consumer interactions will be handled entirely by the AI, including in the chatbot. While both the chatbots operate in quite a similar manner, we list the key differences between NLP chatbot and rule-based chatbot below:
- Most evidently, the rule-based chatbot function on keywords. In contrast, the NLP one is based on deep learning that offers scalability.
- The NLP chatbots operate on various questions explicitly shared by the customers, while the rule-based chatbots depend on the predefined set of rules.
- The rule-based bot finds it hard to identify the words not listed in the keywords. On the other hand, NLP can resolve the queries even when there are spelling mistakes or partial sentences.
- Another important differentiator is the language. In general, keyword-based chatbots can speak only one language. But a Conversational chatbot can speak multiple languages based on customer’s need.
- The NLP chatbots are built on real-life examples and can adapt. In comparison, the rule-based ones cannot get insights from the surrounding environment.
- Lastly, the optimisation of the rule-based chatbot is dependent on the particular brand they are installed for. On the other hand, for NLP chatbots, the bots self-update based on historical data.
How is NLP used in chatbots?
Natural Language Processing (NLP) allows chatbots to understand the messages entered by the customer appropriately and respond accordingly.
The message entered first reaches to leverage the AI capabilities that compare the responses to come up with the most appropriate one. Therefore, by understanding the context and meaning of the text, the chatbots use NLP to respond to the customer.
Training AI with the help of entity and intent while implementing the NLP in the chatbots is highly helpful. It allows the platform to differentiate the statements and adjust the conversation by understanding the nature of the statement in the user response.
Benefits of NLP chatbots
The key benefits or utility of the NLP chatbots are:
1. Enable natural conversations
While conversing with customer support, people wish to have a natural, human-like conversation rather than a robotic one. While the rule-based chatbot is excellent, they lack the human touch. Using the NLP chatbots, the business can offer natural conversations resulting in better interpretation.
The NLP can understand and interpret the information fed by the customer and accordingly offer the appropriate answer. Also, the NLP gains experience based on the past instances and record, enabling smoother conversations that imitate natural conversations.
2. Instant customer support
Using the NLP while designing the chatbots ensure that customer support is assured instantly 24*7, avoiding a long wait period. Oracle suggests that 50% of the customers with the business be open for 24*7, facilitated by chatbots.
3. Reduced agent transfer rate
One of the customers’ biggest concerns is getting transferred from one agent to another to resolve the query. They end up frustrated as they have to repeat themselves. But using the NLP based chatbots, there is less chance of this happening as they are accurate in answering up to 80% of questions. Thus, they decrease the transfer rate and improving efficiency.
4. Improved user experience
A few points differentiate the customer experience of using the NLP chatbots compared to a rule-based chatbot. The most prominent ones include instant resolution, high-quality content, and better moderation, elevating the user experience resulting in an improved experience.
5. Better conversion rate
The quicker the query resolved, the higher the chances of customer conversion/retention. Using conversational chatbots, with the option of customisation and optimisation, assure that the visitors get access to the details they are looking for. Creating a higher satisfaction rate results in a better conversion rate.
6. Better employee satisfaction
The NLP chatbots are, in short, assistants to employees. It allows the employees to avoid repetitive questions that are mundane and boring. When the staff move to more efficient jobs, the staff satisfaction automatically increases.
7. Better accuracy
Customer support is all about customer satisfaction and query resolution. Using Conversational AI chatbots, the chances of miscommunication and errors reduce, thus leading to improved accuracy and better results. The top industries using chatbots are real estate, travel, education, healthcare, and finance, suggested Chatbots Life.
8. Time and cost-efficient
Chatbot help reduce the overall operational cost, cost of customer support, and workforce cost by 30%. As per Juniper Research, chatbot conversations would offer a reduction in business cost by $8 billion by 2022. A time and cost-savvy tool helps redirect the usual queries and automate the processes, thus improving the human-effort cost.
NLP Chatbot use cases
The best NLP chatbot examples or the use cases are:
- Banking and Insurance
The customer’s most common questions w.r.t. to the banking and insurance industry are linked to the balance enquiry, account status, refund status, and card associated queries. Using the NLP chatbots, the business can assure better and quicker resolution of such questions offering higher engagement.
- Customer Care
One of the most common use cases of chatbots is for customer support. AI-powered chatbots work based on intent detection that facilitates better customer service by resolving the queries focusing on the customer need and status. As per Chatbots Magazine, 69% of consumers prefer using chatbots due to the ability to get quick responses.
Mainly used to secure the feedback from the patient, maintain the review, and assist in the root cause analysis by iterating the questions and obtaining the solutions, the NLP chatbots help the healthcare industry perform actively.
- Hospitality and Travel
NLP chatbots are pretty beneficial for the hospitality and travel industry. Using the chatbots to offer information about the schedule, flight status, and booking confirmation offers faster customer service and improved customer satisfaction.
- Supply Chain and Logistics
NLP can comprehend, extract and translate valuable insights from any input given to it, growing above the linguistics barriers and understanding the dynamic working of the processes. Offering the suggestions by analysing the data, NLP plays a pivotal role in the success of the logistics channel.
NLP chatbot: a win for customers and companies
NLP chatbots can help to improve the business processes and overall business productivity. Focusing on the technological advancements to stay in the competitive environment and ensuring better engagement and lead generation, AI-powered chatbots have a reasonable level of understanding.
The NLP market is expected to reach $26.4 billion by 2024 from $10.2 billion in 2019, at a CAGR of 21%. Also, businesses enjoy a higher rate of success when implementing conversational AI. Statistically, when using the bot, 72% of customers developed higher trust in business, 71% shared positive feedback to others, and 64% offered better ratings to brands on social media.
So, if you are looking for effective and efficient NLP chatbots, Verloop.io is one of the best partners that offers seamless integration and smartly designed bots for the customer support needs of the business. Though chatbots cannot replace human support, incorporating the NLP technology can provide better assistance by creating human-like interactions as customer relationships are crucial for every business.