Data, natural language processing, and sentiment analysis – customer experience game changers

The increasing usage of artificial intelligence (AI) is made possible by data, as AI solutions are trained on data to master language and tasks. The more data AI software is trained on, the better. Subsequently, natural language processing (NLP) engines, which help programs process and analyze large amounts of language, enable AI to comprehend and communicate at high levels of proficiency without human assistance.

Data makes sentiment analysis possible through NLP

What do customers want, expect, and need? These are three simple questions that all corporate leaders know will ultimately determine the effectiveness of a marketing campaign, ceiling of a sales drive, or success of a company. Given the cutting-edge tools on the market today, all companies have a treasure trove of valuable information ready to be capitalized on. Leveraging data and sentiment analysis is instrumental in seizing the opportunities of modern customer experiences. Data provides the facts, sentiment analysis the feelings.

Properly designed, AI is able to carry out sentiment analysis, a type of data mining that has the ability to analyze language and perceive the tone of the speaker/writer, through NLP. Essentially, sentiment analysis gives AI the competence to not only understand words, but also the emotions and nuances behind them.

Trained on data, AI-enhanced NLP software can be deployed in data collection, storage, and utilization. Aware of what they need to look for, these programs can automate the data gathering process, precisely retrieving the data needed to address the issue at hand. Through AI, and more specifically sentiment analysis, data can be operationalized to deliver the highest value to brands. In short, data mining through NLP empowers AI, and AI enriches data.

Data and sentiment analysis enhance operational processes

C-suites across all sectors are turning to AI to optimize efficiency and automate tasks. Beyond maximizing AI-driven solutions to do more for less, C-suites understand the value of data in the decision-making process. Gartner points out that by 2022, “more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decision making.”

Combining various methods such as augmented analytics, event stream processing, and business rule management deliver up-to-the-minute data that is essential for making timely and prudent decisions. Are the problems with a specific product? Is a service proving to be problematic? Do agents need to be re-trained or supported? Most issues that contact centers face can be solved through data analysis. Obtaining hard facts is one side of the coin, understanding the emotions (or sentiment) behind these figures is the other. What is the context? How do customers feel? Why are they reacting a certain way? Is there a way to change how they feel?

In an era when language is condensed to fit into a tweet or a short message, extracting meaning and tone is vital to providing excellent CX. Some trained agents are able to do this, but not each and every time. Moreover, AI can help in analyzing and interpreting language, making for the ideal support for training new employees and upgrading skills.

NLP and sentiment analysis deliver greater CX insight

The first breakthrough in sentiment analysis was training AI to understand and recognize the positive and negative connotations of words – which words indicated satisfaction (great, happy, super) and which conveyed displeasure (bad, disappointed, terrible). Advances in NLP mean that not only can longer sentences be interpreted, but the tone can be grasped as well. Indeed, humans’ use of sarcasm can turn the positive ‘great, thanks a lot’ into a biting ‘great, thanks a lot’. Using NLP to understand the tone of language can assist in gathering data that has a monumental impact on a brand’s messaging, customer service, and agent performance as well as experience.

Sentiment analysis – what’s in store?

C-suites that are striving to instill a data-driven approach appreciate the opportunities of sentiment analysis and NLP. Augmenting agents and accomplishing advanced real-time and predictive analytics are two of the biggest functions, but far from the only advantages. The dynamic nature of AI development shows that advances occur every week, meaning sentiment analysis is getting stronger all the time. According to Daniel Newman, principal analyst of Futurum Research, “[s]entiment analysis is capable of 90% accuracy. That’s not a technology in early stages – that’s a technology in a state of maturity, ready to go to empower companies, employees, and customers all at once.”

AI-driven NLP tools are in place to ensure that sentiment analysis provides reliable, usable data. Going forward, corporate management must bridge the data and business sides of their organizations. Forrester reveals that “[d]espite continued investments in data and analytics, there is lack of alignment between business and operations teams’ accountabilities and the metrics they need to make more informed decisions. Closing this disconnect is imperative to improving business outcomes.”

Sentiment analysis elevates CX

The question every corporate board member needs to ask now is not if sentiment analysis through NLP is implemented, but when? The importance of data is well-established, and enriching data through sentiment analysis should be the next objective in a data-based strategy. 

Turning to providers who have a data-driven approach, like Odigo, allows companies to trust their digital transformations to a leader with a forward-thinking vision and a proven track record. The ultimate proof of this? Odigo is one of the first CCaaS providers to support Dialogflow CX, Google Cloud’s AI-based solution for contact centers. 

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