![]() A single statement said in a natural language holds an incredible amount of data, from standalone keywords to sentence structure, from underlying sentiment to customer metadata. Typically, companies are held back by the lack of adequate in-house infrastructure and access to data science skills when it comes to NLP adoption. Document classification, name recognition, sentiment analysis, and correlation mapping or knowledge graphs are four of the most popular applications of NLP today.65% of companies are currently using at least one of these four cloud NLP solutions Interestingly, cloud-based NLP like the ones provided by Google, AWS, Azure, and IBM have been vital in driving adoption.More than half use Spark NLP and spaCy, while the former is used by nearly 1 in 3 companies There are three popular NLP libraries in use (i.e., libraries of algorithms and datasets prebuilt for text recognition).In this segment, NLP budgets increased by 10-31% in one year Industry leaders and large companies with 5000+ employees are significantly ahead of the curve.Here are a few other key findings from the report: There is still a lot of room for adoption, with plenty of use cases in the CX discipline. 2019, according to a 2020 NLP survey report. That’s why NLP budgets are growing slowly but steadily, with most companies spending 10% more in 2020 vs. Second, in order to store and process such vast amounts of data, you need substantial computing power. It is very difficult to pre-program an NLP library that can keep up with the dynamic evolution of how people communicate. Human language is extremely nuanced, and it evolves every day. Self-service and automated support using virtual customer service assistants powered by AI and NLPĭespite its incredible potential, NLP is yet to become a CX staple due to two challenges – accuracy issues and computing demand.CX personalisation by checking for specific keywords in written/telephonic communication and automatically triggering promo emails.Paperless processing by extracting data from images, PDFs, and screenshots to populate electronic forms and fields (helpful for banking and the public sector).Customer feedback analysis by collecting unstructured, descriptive feedback to identify keywords, dominant sentiment, and trends.Transcription tasks, like transcribing recorded calls in a contact centre or automated audio captioning in product tutorial videos.some of the key use cases for NLP in CX are: NLP lets you convert this largely unstructured practice into a structured, formalised format – ready to pass through analytics, use as a trigger for automated events, etc. Why is NLP Crucial for CX Professionals?ĬX management has always involved interacting with customers at scale and making sense of these communications, across multiple channels. Computer science – How can systems be properly configured to accept human input? How do you optimise computing resources for consistent performance?Īs you can see, NLP is a complex interdisciplinary area of study, often involving technologies like speech recognition and text analytics to uncover its full potential.Artificial intelligence – How can previous interactions improve future understanding? What are the different options when it comes to choosing a response? How does the computer decide?.Linguistics – How do different characters and character combinations form meaning? What are the modalities of meaning in different languages?.To achieve this, it utilises theories from the following disciplines: Therefore, the functionality of an NLP engine can be segmented into three steps – understand, process, and respond. ![]() You can define natural language processing as a technology that allows humans and computers to interact, enabling computers to understand human beings, process and identify a response, and return this response in a form that is comprehensible to the human participant. What is NLP? Definition and Functionality From conversational interfaces to automated transcriptions of call recordings in a contact centre, there is an NLP technology powering most new-age CX systems. In the last few years, advancements in artificial intelligence (AI) and NLP have paved the way towards newer use cases for human-machine interactions in CX management. It would enable automation at a previously unachievable level, allowing machines and humans to actually communicate. It would allow machines to read and understand natural languages like English and respond with a meaningful reply. Natural language processing or NLP has the potential to dramatically transform customer experiences.
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