How is Natural Language Processing (NLP) used in business?
In Natural Language Processing, a computer can comprehend spoken and written language. Using Artificial Intelligence, NLP processes and interprets input from the real world in a way that a computer can understand. It allows to efficiently process and analyse large amounts of unstructured, text-based information.
NLP: Artificial Intelligence and human language working together
The process of Natural Language Processing involves two main phases: data preprocessing and algorithm development. In data preprocessing, text data is prepared and cleaned. Algorithm development involves the design, validation, and optimisation of machine learning methods to find patterns in the data. ML algorithms predict output values from the input data and are tuned in order to be more accurate.
Data preprocessing consists of various techniques, such as stop word removal, lemmatisation and stemming, tokenisation, and part-of-speech tagging. NLP algorithms can be either rule-based or machine-learning-based, where machine learning algorithms use statistical methods to learn and adjust their methods based on training data.
By combining Artificial Intelligence (AI) and human language, the integration of Natural Language Processing in business settings creates a powerful synergy between technology and communication. The implementation of NLP systems driven by AI facilitates more intuitive interactions between computers and humans and it is revolutionising how organisations communicate, operate, and innovate.
What are the benefits of NLP for business?
The customer’s chat message left management, marketing, and sales departments puzzled. Was the client polite or sarcastic? Misunderstanding text analytics can come at a high cost. For business success in today’s world, it’s essential to process textual data efficiently.
Understanding the exact intentions of a customer who left a message in the company chat can result either in a better understanding of the client’s needs or, in the worst-case scenario, a reputational problem. Let’s examine some NLP use cases to check how an NLP-based approach can increase productivity, customer satisfaction, and business profitability.
It won’t be an exaggeration to say that by enhancing customer interactions, Natural Language Processing revolutionises business communication. With NLP algorithms, meaning and sentiment are extracted from text, allowing customer needs to be met in a personalised manner. Automation improves communication by reducing waiting times and providing round-the-clock support.
Some benefits of NLP in business communication include:
- Improved customer interactions: By utilising NLP systems, businesses can achieve seamless communication resulting in increased customer satisfaction.
- Text analysis and summarisation tools: NLP enables businesses to optimise workflows thanks to quick and accurate analyses and summaries.
- Real-time language translation: NLP also promotes collaboration and knowledge sharing by breaking down language barriers.
Efficient Data Analysis
NLP can offer businesses the advantage of effective data analysis. As a result of leveraging Natural Language Processing, data scientists can automatically classify and categorise unstructured data to find valuable patterns, trends, and customer preferences.
Three major benefits are provided by NLP in terms of efficient data analysis:
- Informed business decisions: NLP’s analytical capability empowers sales, marketing, and corporate boards to make more data-driven decisions.
- Deeper customer insights: NLP enables businesses to extract valuable insights from large volumes of data, helping them gain a deeper understanding of customer behaviour.
- Optimised marketing strategies: Organisations can optimise their marketing strategies and deliver targeted campaigns.
The integration of NLP applications in business processes goes beyond communication and data analysis. Take a closer look at how NLP can streamline business operations:
- Automation of tasks: NLP automates data entry, document processing, and information retrieval, reducing manual effort and minimising errors. Human employees can then focus on more strategic and creative endeavours.
- Operational efficiency and cost reduction: Streamlining operations improves overall performance and reduces costs. Automation replaces manual processes, resulting in improved productivity.
- Accelerated information extraction and decision-making: NLP-powered systems extract relevant information from documents, organise and classify data, and retrieve information rapidly.
Personalised User Experience
Natural Language Processing empowers businesses to gain insights into individual user preferences and behaviours, allowing them to deliver highly personalised recommendations, content, social media reviews, and advertisements.
Let’s look at how NLP facilitates personalised user experience:
- Tailored offerings: NLP algorithms analyse user data to identify patterns, trends, and customer interests, enabling businesses to personalise their offerings to meet specific needs.
- Personalised recommendations: NLP-driven recommendations from search engines suggest products or services based on users’ previous interactions, resulting in a more satisfying customer experience.
- Enhanced customer satisfaction: Businesses provide experiences that align with individual preferences, improving customer engagement and loyalty. Providing effective customer service builds trust and supports building long-term relationships.
The number of NLP use cases for analysing sentiments expressed in customer reviews, social media posts, and online feedback is almost endless. This invaluable feature enables businesses to monitor their brand reputation effectively, identify potential issues or areas of improvement, and address customer concerns promptly.
NLP can be used for reputation management in business in the following ways:
- Customer sentiment analysis: NLP provides valuable insights into customer sentiments, enabling companies to engage with customers and ensure their satisfaction in a proactive manner.
- Real-time sentiment insights: Leveraging NLP for sentiment analysis allows companies to gain real-time insights into customer sentiments, whether positive, negative, or neutral, and react accordingly.
- Proactive reputation management: Companies can gauge the overall perception of their brand and products, identify emerging trends and risks, and take proactive measures to enhance customer satisfaction.
What other applications NLP may offer?
An NLP-powered email filter could play a critical role in streamlining business communications. Based on the content and context of emails, NLP-based filters can categorise and prioritise incoming messages.
With such filters, organisations can enhance productivity by:
- speeding up response times;
- ensuring that critical information is delivered effectively to the right recipients;
- detecting patterns, keywords, and sentiments;
- minimise distractions caused by irrelevant or spam emails;
- promptly responding to important messages or preparing automatic replies when necessary.
As a key application of NLP, voice recognition has revolutionised the way we interact with technology. With the aid of NLP-based voice recognition systems, spoken language can be accurately transcribed, converted into a structured form (such as text), and interpreted in real time.
The use of voice recognition technology in business settings facilitates tasks such as voice commands, voice-to-text transcription, and voice-activated virtual assistants. Consequently, the company is capable of:
- enabling seamless multitasking and saving time by dictating notes or whole documents;
- improving accessibility for people with disabilities;
- streamlining workflows and reducing repetitive tasks;
- automating processes;
- providing more intuitive user experience.
Applications of NLP across various business sectors
Artificial intelligence and NLP can be integrated across a wide range of business sectors. With the power of NLP, businesses can unlock new opportunities, drive innovation, and stay ahead of the competition.
NLP for Insurance
Natural Language Processing is revolutionising the insurance industry. Operators can process and manage claims faster and more efficiently than before, as well as optimise and implement NLP-based fraud detection, underwriting automation, risk assessment, and more.
As a result, insurers can streamline operations and decision-making processes, and provide personalised service to policyholders. From intelligent chatbots to advanced predictive analytics, NLP for the insurance industry holds a lot of promise. Read more about digital transformation in the insurance in The future of vehicle claims, based on the Future Processing’s InsurTalk interview with Bill Brower from Solera.
NLP for Business Intelligence (BI)
The use of Natural Language Processing in Business Intelligence (BI) has become essential for unlocking valuable insights in large and unstructured datasets, also ones coming from different sources.
By utilising NLP techniques, organisations can extract meaningful information without much technical knowledge. NLP improves the accuracy of results, personalises analytics, may provide valuable follow-ups and perform topic modelling. Consequently, businesses can make better data-driven decisions, identify emerging trends, and gain an advantage over their competitors.
NLP for FinTech
Using NLP techniques, financial institutions can gain valuable insights from unstructured financial documents, news articles, social media monitoring, and market data.
Consequently, they can make informed investment decisions, detect fraudulent activities, and improve their risk management strategies. By implementing NLP tools for finance, organisations will be able to increase the efficiency, accuracy, and profitability of their financial operations.
NLP for LegalTech
NLP has emerged as a game-changer in the field of legal technology – LegalTech, transforming the way legal professionals work. With NLP, organisations are capable of automating document analysis, contract reviews, and legal research, resulting in time savings and improved accuracy.
NLP also allows the extraction of key information from legal documents, helping lawyers make informed decisions much quicker. Furthermore, NLP-powered chatbots can provide legal guidance 24/7 and answer common legal questions.
NLP for Sales
In the field of sales, NLP empowers organisations to gain a deeper understanding of customer interactions, preferences, and buying behaviour. The possibilities for using NLP for sales here are almost endless: from analysing sales-related data, such as emails, call transcripts, and customer feedback, to identifying patterns, sentiment analysis, and key insights that enable sales teams to personalise their approach and optimise strategies.
NLP for Customer Service
NLP-based solutions enhance customer service by enabling businesses to understand and respond to customers’ queries and feedback quicker and more efficiently. Through the analysis of customer conversations, chat logs, and support tickets, NLP algorithms can extract valuable insights, sentiments, and intents, which allows responding to the clients’ needs promptly and accurately.
Virtual assistants and self-service chatbots powered by NLP improve customer service by offering instant responses, 24/7 availability, and seamless interactions, reducing customer wait times and improving efficiency.
The future of NLP in business: trends and predictions
The future of Natural Language Processing in business holds immense potential. A prevalent trend in NLP is the integration of other emerging technologies, such as Machine Learning, which enable more sophisticated language understanding and context-aware analysis. This empowers companies to make data-driven decisions, optimise processes, and gain a competitive edge in the market.
Soon, multilingual NLP solutions may play a crucial role in global business operations, enabling companies to effectively communicate and analyse data across different languages and cultures. Consequently, international expansion will be facilitated, market reach enhanced, and customer preferences understood even better.
Why should you invest in NLP solutions?
If you are unsure whether your company should invest in NLP solutions – have no doubts. The future of business in most industries is related to the NLP application.
Using advanced NLP applications, companies will soon gain an even deeper understanding of their customers. It may still sound like science fiction, but the AI-powered virtual assistants are already able to provide customers with personalised experiences. In addition to understanding and accurate responses to queries, we can expect NLP to deliver tailored recommendations and support, increasing customer engagement and satisfaction.
Many applications of NLP technology in business are still in the research stage. However, NLP-based business solutions are sure to revolutionise many industries over the next few years. Ask us for more details and take advantage of NLP technology to boost customer satisfaction and engagement while gaining a competitive edge.