Why NLP Should Be Your Contact Center MVP


Share on LinkedIn

Businesses need to transform the way they think about the contact center – it’s no longer a ‘cost center’ but instead a valuable, often untapped, resource teeming with key information about customer experience. Major advancements catalyzing this transformation lie in natural language processing (NLP) technology. Though companies have historically employed NLP to derive basic customer insights, most are now missing out on its latest capabilities, which ultimately have the power to revolutionize business and solve unique customer experience challenges from the bottom up. Brands can think of NLP as oil – what was once employed to power individual lamps, now has the power to fuel cars, jets and even spaceships. To unveil important trends and devise nimble solutions, brands need NLP as a vital part of the contact center and here’s why.

1. Stop Trying to Fit a Square Peg into a Round Hole. Current systems within the call center often only allow for the identification of one conversation topic, or pain point, as opposed to multiple and once established do not provide the flexibility to add new topics. Operating within an incomplete system and often handcuffed by an antiquated list of topics, center agents typically select either one of the first or an other or miscellaneous group, which provides little value to a company’s customer experience team. The application of NLP, however, enables automatic tagging of multiple topics to both reduce agent bias and ensure that companies comprehensively capture customer data, without letting important feedback slip through the cracks. This ultimately provides a more accurate and holistic view of customer feedback, equipping companies’ call centers with a dispositioning system that eliminates common agent biases and minimizes the time (and cost) allocated to agent notes.

2. Quickly Painting a Better Picture of Your Customer. In an effort to capture as much customer feedback as possible, call center agents are typically penalized for long interactions, and are conversely rewarded when completing as many customer cases in their shift as possible. This approach, though it might increase call volume, often results in both incomplete notes from agents and poor customer service, thus achieving the opposite outcome. Infusing NLP technology into the call center however, shortens call time by automatically transcribing conversations. This technique not only enables agents to more effectively utilize their time to actually help frustrated customers, but also provides companies with the tools to then analyze those robust notes to determine the root causes of customer dissatisfaction.

3. Tracking Emotion, Effort, Advice and More! NLP is a common application of machine learning techniques but don’t let the “machine” part fool you. NLP has the potential to lend a human ear by gleaning insights from not only structured data, such as spoken or written words, but more importantly, unstructured data, like emotion or sentiment. Within the call center NLP technology can help aptly identify multiple customer emotions, determine level of effort, flag distinct requests or suggestions and even spot cries for help. While these human elements of customer experience are often locked away as unstructured data, analyzing language using NLP techniques allows for a comprehensive analysis, ultimately empowering companies to design a more empathetic contact center experience for customers.

For example, if a customer complains of an untimely or damaged delivery but then also notes that this was the third instance of delivery woes – NLP technology can reveal that detail to elevate the level of frustration. Further, if this customer suggests that the brand include extra packaging to protect fragile items, NLP tools and technology can also highlights this suggestion in conjunction with the negative emotion. Automating all of this highly specific feedback ensures that brands initially preserve the voice of the customer and then are equipped to respond to frustrating issues in the most effective ways possible.

4. Breaking Down the Silos. The call center, as one prong of the contact center, harbors a ton of valuable information for brands. The application of NLP can be implemented across the entire contact center though, which includes chatbots, emails, surveys and other online communities. Insights gleaned across channels however, often live in isolation, from both complementary channels and customer experience teams. With NLP, feedback from different contact center channels is analyzed simultaneously and can be distributed to the customer experience teams who ultimately influence brands’ future business decisions. If brands choose not to implement NLP into the contact center, and instead only focus on archaic, very rigid disposition codes, they’re missing out on a huge opportunity to preserve a customers’ intended message in the truest form, which then enables them to provide empathetic, practical and impactful responses.

The contact center is the best place to capture the Voice of the Customer (VoC) and ultimately improve customer experience across all interactions with a brand. It contains tons of customer content across various feedback channels, identifies specific issues and maintains customer voice. If businesses implement NLP technology, they’re able to understand even the most complex customer feedback, in an organized and comprehensive manner, delivering valuable insights to key decision makers across the company. For businesses looking to win, it’s time to trade up for an MVP and put NLP in the game.

Ellen Falci Loeshelle
Ellen is a Senior Product Manager of NLP and Enrichment at Clarabridge. She leads strategy, design and development for Clarabridge's NLP and text analytics engines which are unique offerings within the CEM space. Ellen holds a B.A. from the University of Virginia and a Masters in Communication, Culture and Technology from Georgetown University.


Please use comments to add value to the discussion. Maximum one link to an educational blog post or article. We will NOT PUBLISH brief comments like "good post," comments that mainly promote links, or comments with links to companies, products, or services.

Please enter your comment!
Please enter your name here