Steven Harris of Odigo discusses why he believes data, natural language processing (NLP) and sentiment analysis are customer experience game changers.
What do customers want, expect, and need? Three simple questions that all corporate leaders know will ultimately determine the effectiveness of a marketing campaign, revenue of a sales drive, and success of a company.
Given the cutting-edge tools on the market today, all companies have a treasure trove of valuable customer information ready to be utilised to answer these questions.
Leveraging data and sentiment analysis is instrumental in grasping the challenges and seizing the opportunities of modern customer experiences. Data provides the facts, sentiment analysis the feelings.
Data Makes Sentiment Analysis Possible Through NLP
The increasing use of 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, machine learning (ML) – the ability of programs to learn independently – and NLP engines – which help programs process and analyse large amounts of language – enable AI to comprehend and communicate at high levels of proficiency without human assistance.
Properly designed, AI is able to carry out sentiment analysis (also known as opinion mining or emotional AI), a type of data mining that has the ability to analyse language and perceive the tone of the speaker/writer, through NLP.
Essentially, sentiment analysis gives AI the competence to understand not only words, but the emotions behind them.
Trained on data, AI-enhanced NLP software can be deployed in data collection, storage, and utilisation. 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 operationalised to deliver the highest value to companies. 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 optimise efficiency and automate tasks. Beyond maximising AI-driven solutions to do more for less, C-suites understand the value of data on 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.”
Combining various methods such as augmented analytics, event stream processing, and business rule management delivers 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? All issues that contact centres face can be solved through data analysis.
Obtaining hard facts is one side of the coin, understanding the emotions (sentiment) behind these figures is the other.
- What is the context?
- How do customers feel?
- Why are they reacting this way and is there a chance to change their mood?
In an era when language is condensed to fit into a tweet or a short message, extrapolating the meaning and tone of the language is vital to providing excellent CX.
Some trained agents are able to do this, but not each and every time. Moreover, AI’s help in analysing and interpreting language is the ideal support for training new employees and upgrading skills.
Using NLP and Sentiment Analysis to Deliver Greater CX Insight
The first breakthrough in sentiment analysis was training AI to understand and recognise 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 which 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 instil a data-driven approach appreciate the opportunities of sentiment analysis and NLP. Augmenting agents and accomplishing advanced analytics (both real-time and predictive) are two of the biggest functions, but far from the only advantages.
The dynamic nature of AI development shows advances occur every week, meaning sentiment analysis is getting stronger all the time.
According to Daniel Newman, principal analyst of Futurum Research, sentiment 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,” says Daniel.
The 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 organisations.
Forrester reveals: “Despite 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 allows companies to trust their digital transformations to a leader with a forward-thinking vision and a proven track record.This blog post has been re-published by kind permission of Odigo – View the Original Article
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