February 2022SHARE
February 2022
SHARE
Summary

Natural Language Processing (NLP) led support saves up to 30% of customer service costs. While it is great for enterprises, is it living up to its hype? We address the use of NLP-based conversational AI solutions and their need for a structured deployment approach through this article. From using intelligent chatbots to responding to customer queries to choosing the best adoption practices, learn how NLP brings value to the enterprise.

If you transact online, you probably have first-hand experience with a chatbot. A small helpful pop-up that’s there to help you navigate your digital journeys with ease. It’s supposed to answer all your queries, eliminating the need for an expensive, human customer support agent. For companies, the savings are lucrative – some reports peg this as a 30% savings in customer service costs. But is it delivering all it promised for the customer? Customer frustration from Natural Language Processing (NLP)-led support is a reality today. I am quite positive that you’ve been frustrated with a chatbot that took so much of your time without giving any helpful resolution to your query. Most people feel that interacting with a chatbot is just going around in circles, wasting your time. And studies show that when customers are already angry or frustrated, interacting with a human-like bot can worsen their experience.

Hailed as the best thing since sliced bread, NLP was to be a technology that forever changed the game for customer experience. Then what went wrong? Why does it feel like it’s not living up to its promise? Why are more companies not able to realize the real value of NLP? It all boils down to the approach.

While businesses deploy NLP in various use cases, let’s explore this topic from the lens of NLP-based conversational AI solutions.

Technology is only as good as its deployment approach

There is no doubt that NLP is an essential part of any business’ customer support toolkit. Given the explosion in customer data streaming in from all sorts of channels, human agents can’t keep up with the need for personalized support.

All the social chatter that’s out there is a gold mine for businesses looking to understand and engage their customers. And NLP can help them make sense of all this unstructured data and respond effectively. There is tremendous potential in this technology, and businesses know it. That’s why the global Natural Language Processing (NLP) market is expected to be worth USD 48.46 billion by 2026, registering a CAGR of 26.84% during the forecast period (2021-2026).

Advancements in NLP technologies are happening every day. We see new ways of understanding contexts, people sentiments, and behaviors, focusing on understanding human minds from their actions in the digital world. Today NLP systems can understand nuances of human language like sentiment and intent and generate a human-like response. The latest R&D is in language generation models like question generation, abstractive summarization, and visual understanding. This helps in understanding and generating more accurate responses in search results, chatbot interactions and virtual assistant capabilities delivering better customer experiences and bringing value to businesses.

NLP brings value to the enterprise

  1. As intelligent chatbots acting as the first customer point of contact for quick resolution of queries
  2. As a conduit to tap into customer shared voices and support business decisions along with sentiment analysis
  3. Support human agents with right information based on NLP queries, improving productivity and effectiveness
  4. Intelligently route IVR and tickets to track and respond to customer issues faster

There are some great examples of how companies have used NLP to understand the underlying reasons for consumer behavior. For example, social and sentiment analytics helped beverage company Ocean Spray identify key consumer behaviors around cranberry juice. These insights helped them design the launch of two new product lines in line with customer preferences that exceeded expectations .

However, despite rapid evolution and lucrative use cases, NLP technologies are only as good as their approach to their deployment.

To deliver a human-like experience, conversational NLP solutions should be able to take a customer conversation to a logical conclusion. An outcome that is clearly missing in almost all the deployments out there. The reason? In a rush to digitize operations – no doubt necessitated by the pandemic – companies have implemented quick-fix NLP solutions in siloes.

These bots have been left to serve customers with limited training and are grossly unequipped to handle the job. Even as these solutions struggle with customer demands, companies have left human-in-the-loop on the sidelines, leaving consumers with no option if the bot is unable to service their request. Would you leave a fresh graduate in charge of your customer service without any training? No, right? Yet, that’s exactly what is happening to these bots.

A new approach to NLP

Companies need to think of them across three vectors: technology, process, and purpose to get value from their NLP investments.

NLP adoption best practices

  1. Start small, with simple use cases that are better suited for NLP adoption.
  2. Improve on the use case till the technology is able to deliver the desired outcomel process service requests thoroughly to customer satisfaction.
  3. Support human agents with right information based on NLP queries, improving productivity and effectiveness
  4. Ensure that the Al bot has access to all knowledge base handles, including a human expert it can consult or handover in case it isn’t able to determine a conclusive answer with proper dialog management.
  5. Create a comprehensive learning / reinforcement feedback mechanism for the Al bot to learn from each conversation.
  6. Evolve the bot to remember and recall previous conversations (dialog history) with the same customers and use it in current
    context to answer their latest concern which is normal when a human agent interacts with same customer again.
    • Technology

      Don’t look for out-of-the-box solutions: With NLP, an out-of-the-box approach will never work. Each enterprise must think through its products, possible service areas, validations and envision the end-to-end transaction needed completely before deploying AI chatbots. Unless the responses are solving customer issues, or serving the customer request, any AI solution as part of digital transformation will not be successful. Your approach should be listening to the customer, rightly interpreting what they are looking for, and answering the question sooner than asking too many questions to confirm the query itself.

    • Process
      Train the bot, keep human in the loop and re-train: While NLP is a trending topic and adoption rates are increasing, it has a long way to go to understand, interpret, and generate human-like responses. Therefore, just pushing an NLP solution to front-end customer service is a recipe for disaster. You need to train it to your customer needs, give it time to learn, and figure out the right response. NLP can’t be an entire solution for now – there is a need to monitor and train it and add human to the loop. Take it as a process and take it slow. Treat the AI bots as your new support agents trying to learn, gain more knowledge and expertise as they converse with more customers, and learn on the job.

    • Purpose

      Do what works for your business: Everyone is rapidly digitalizing. That doesn’t mean you should too. Don’t look at digital as a race but rather weigh in how relevant is it for your business today. Identify critical areas of impact and what aspects make the most sense to pass on to the bot and start with those.

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Course correct your NLP journey

COVID has accelerated the need for digital transformation, pushing companies for quick-fix digital solutions. But a haphazard implementation may actually be a major setback on the larger digital transformation journey. True, the pandemic pushed us all in a tight place, but now is the time for course correction. The need is to watch out for emerging research outcomes in NLP and embrace its benefits wisely, steadily, and with governance and AI Ethics.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies