A myriad of customer service channels exist today, such as social media, email, chat services, call centers, and voice mail. There are so many ways that a customer can interact with a business and it is important to take them all into account.
Customers or prospects who interact via chat may represent just one segment of the audience, while the people that engage via the call center represent another segment of the audience. The same might be said of social media channels like Twitter and Facebook.
Each channel may offer a unique perspective from customers – and may provide unique value for business leaders eager to improve their customer experience. Understanding and addressing all channels of unstructured text feedback is a major focus for natural language processing applications in business – and it’s a major focus for Luminoso.
Luminoso founder Catherine Havasi received her Master’s degree in natural language processing from MIT in 2004, and went on to graduate with a PhD in computer science from Brandeis before returning to MIT as a Research Scientist and Research Affiliate. She founded Luminoso in 2011.
In this article, we ask Catherine about the use cases of NLP for understanding customer voice – and the circumstances where this technology can be most valuable for companies.
Why Customer Voice Needs Artificial Intelligence
Making sense of the meaning in customer or user feedback (through phone calls, chat, email, social media, etc) is valuable for nearly any business. The challenge lies in finding this meaning at scale, and across so many different data formats.
Catherine tells us that, historically, businesses manage these different customer interactions by putting them into appropriate “buckets” or categories. For example, if there are 70,000 customer support email messages received in a particular month, the company might have a manual process of flagging each message as “refund request,” “billing inquiry,” “purchase request,” etc.
However, manual categorization becomes nearly impossibly challenging at scale, for a number of reasons:
- While all customer service emails and call center calls might be labelled manually by the customer support rep who handles then, other kinds of data (tweets, chat messages, comments on online forums) may never receive the same kind of labelling.
- A company with pre-determined “buckets” (categories) for customer service inquiries is unlikely to be able to pick up on new, emerging trends in the particular words, issues, or phrases used in customer requests. This inability to adapt and find new patterns could limit the company from seeing new opportunities for improvement, or new emerging issues for important customer segments.
- Human beings don’t categorize content in the same way – and discrepancies and misunderstandings in categorization can make customer feedback useless.
Many companies look to technology that can detect common patterns for these messages, create categories for each found pattern, and flag them appropriately for the attention of the business owners (which includes finding new patterns). This is a job for machine learning.
“Sentiment analysis” – the process of computationally identifying and categorizing opinions expressed in a piece of text – has become a somewhat familiar term. Catherine tells us that truly understanding customer voice involves much more than simply detecting emotions within text, and includes:
- Finding new “entities” (products, brands, people) which are gaining or losing frequency in customer feedback
- Determining customer sentiment – not just overall – but in relation to specific entities or types of customer issues
- Showing changes and trends in customer feedback over time
- Understanding the different patterns of feedback across unique channels (call center, chat messages, social media, etc)
The problem is many text analytics techniques in the past require a significant amount of data and effort in building rules and anthologies in the beginning, and still be unable to provide a true picture of what is actually being said by the customers.
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