NLU offers companies the power to improve quality management and to activate the best insights for a better overall customer experience.
The heart of your business is customer perception, and your contact center experience forms the basis of that perception. How your customers feel after a contact center interaction makes or breaks their customer experience. When 70% of consumers value speed, convenience, helpful employees, and friendly service, it behooves managers to use the right contact center analytics to serve as checks and balances on their system. Increasingly, Natural Language Understanding (NLU) plays a critical role.
Your checks and balances rest with your quality management teams. By tracking agent calls, emails, and chats against scoring criteria, they determine how to best tweak customer experience (CX). Despite their diligent work, much of the most useful data goes unscreened, leading to missed growth opportunities. NLU offers quality management teams a way to better scan large volumes of inquiries for issues, generate more perceptive insights and act confidently on intelligence while reducing human bias and error.
See also: Call Center Conundrum: The Future is Flexibility
NLP vs. NLU: A business approach
Companies have more commonly applied Natural Language Processing (NLP) when performing linguistic analysis. This model relies on linguistic and statistical algorithms to extract meaning from text, a process reflecting how the human brain understands language.
A major benefit of NLP is how quickly it can parse through data and produce results: for example, when 95% of customer data exists as unstructured text like emails, survey write-in answers, or online reviews, it makes reading through this information nearly impossible for humans. Seeing as how the average person can process 50 items of unstructured text per hour, a human would require nearly seven years to read through one million items. Comparatively, an NLP engine can do that in minutes.
NLP categorizes unstructured data, often organized into topics linked by keywords or phrases. Humans can better identify patterns from this structure, transforming a tedious review process into a simplified search for actionable insights.
Yet, while NLP has given businesses a powerful resource, it’s become more commoditized, especially so in the CX space. While CX teams successfully use NLP to categorize topics of conversation, topic analysis alone does not provide sufficient insights to enable true CX improvement.
The growing demand from business for better insights has led to the next step: Natural Language Understanding (NLU). It goes beyond what a customer’s individual words say and develops context around the language’s meaning. The linguistic elements and structures NLP has previously mapped form the basis, while NLU builds upon it by intuiting the connotations and implications innate in conversation. It analyzes the emotion, effort, intent, or goal behind a statement to unearth the deeper meaning.
A system must possess a matured NLP model before it can successfully deploy NLU. After building the linguistic foundation with an NLP engine, NLU can incorporate value-added features using insights from context and meaning.
Create objective quality measurements
Quality management teams are charged with analyzing contact center performance and determining process improvements, but they need good data to do so. Many teams still rely solely on Net Promoter Scores to inform their decisions, which do not scale well and can lead to biased results based on subjective responses. If biases are reduced or removed, companies will have access to more accurate insights to improve quality management, agent response, and overall CX. Yet, 80% of contact center data is unstructured, preventing quality management teams from using it as part of a more objective approach toward improvement.
NLU is geared to scale effectively while developing key insights for quality management teams. In a contact center, NLU can review every type of customer interaction, like calls, chat and messaging, and apply a human team’s own weighted evaluation criteria to automatically score interactions. Businesses can assess agents’ hard and soft skills, identify and prioritize issues across many inquiries, and empower teams to confidently act on objective information. NLU can power an intelligent scoring model that both operates consistently and transparently and reduces human bias.
With NLU, quality management transforms from manual, subjective scoring into an automated and efficient process, void of bias. QA managers no longer tied to manual tasks can reallocate their efforts toward other initiatives better suited to humans, like coaching, while leaning on a balanced, objective approach for improvement.
Improve CX with NLU in contact center analytics
Human language does pose additional challenges that NLU must overcome before it can operate at peak efficiency. A language’s fluid and complex nature lends itself to divergent interpretations: two people can read a passage and come up with wholly different meanings. Since this struggle persists with humans, it’s logical to assume machines will face it, too.
NLU has advantages that improve its efficiency on this front. It can apply rules and machine learning techniques to extract, tag, and score emotion, effort, intent, profanity, and more — concepts important to the customer experience. Users can personalize these elements to reflect their use case and industry. Along with the original text, as well as associated source and customer metadata, analysts can reveal what customers mean, not just what they say, to create truly actionable insights.
These analyses can lead not only to quality management improvements but also uncover answers to other business questions, like the root cause of problems, drivers of CSAT and loyalty, and the likelihood of churn or noncompliance. For example, the system can review a customer’s praise or criticism of an associate to assist CX teams in doling out rewards or modifying performance. It can also encourage associates to take proactive steps if a customer leaves an email address or phone number in the text. Even popular names like performers, politicians, and celebrities, who could be influencing your customers’ perceptions, can be tracked.
NLU can also capture data on larger market trends: for example, it can track mentions of events occurring alongside sales and promotions like Independence Day, Black Friday, or Cyber Monday to see which are generating buzz. Mentions of milestone events like weddings, baby showers, and graduations can produce insights on better marketing and pricing strategies for items targeting buyers in these groups. Perhaps customers use products in other ways and for other occasions; NLU can help identify those emerging opportunities.
Contact centers gather useful information every day, but companies need the right analytics to parse through it and surface insights. From more transparent agent evaluations to better-timed marketing strategies, NLU offers companies the power to improve quality management and to activate the best insights for a better overall customer experience.