Stemming: This process comes into play for interpreting miswritten or mispronounced words, as well as for reducing time needed to program a machine to understand intent. Consider the following: "I want to have funds transferred." The stem of "transferred" is "transfer." If you tune the machine to consider all forms of "transfer," then you can save time by not having to program every form of the word manually.
Named Entity Extraction: I touched on this earlier in my stock trading example for understanding intent. After the customer asks about the stock price for Alphabet, he might add, "What about Amazon?" The machine maintains the context and says: "Amazon is trading at $1522.32." Taken on its own, "What about Amazon?" might not refer to stock price, but rather be a request for information about the rain forest. Using named entity extraction, the machine is able to deliver a response that matches the context of the conversation.
Some NLP solutions come with detailed lists of publicly used named entities, such as company name as in my example, but others don't. Even with a provided list, the named entities in your business may be different from others so you'll need to pay special attention to building your list. Besides company names, the named entities list should include the names of products, cities, countries, vendors, and processes -- include any named entities that might come up and would help the machine deliver a faster, more accurate response during a customer interaction.
Topic Segmentation: Building a knowledge base traditionally has involved a manual process for curating content and determining topics and subtopics. NLP solutions can automate this work with a topic segmentation process that determines which sections of a document apply to a specific customer request. For the purposes of speed, it's best to execute topic segmentation in advance and generate tags on all knowledge content so that your machine can more quickly present the correct knowledge when customers request it.
Discern Sentiment (Emotion): Sentiment analysis has many uses in a contact center. As the linchpin in an offer management solution, it's hard to duplicate. If the customer is happy, make him an offer; if he's not happy, then conference in a supervisor to get the issue resolved. Analyzing words and punctuation can help determine sentiment in a text interaction. Voice interaction add pitch and volume for use in sentiment analysis, while video interfaces bring facial expression into the analysis. Some vendors offer all of the above, but many do not. If you're interested in sentiment analysis, then make sure you understand your selected vendor's abilities in this area.
Summarize Content: Summarization is an interesting tool in the customer engagement environment. For its Watson NLU engine, IBM charges $0.003 to summarize a 10,000-character document and the same price to summarize an eight-word utterance from a customer. NLP solutions aimed at customer interactions are typically tuned to these shorter utterances.
In customer service environments, summarization is more of a statistical tool. It can effectively take the place of call disposition processes, providing contact center operations managers near-real-time insight into why customers are calling. I know I would have found an NLU summarization tool useful back when I was running a pharmacy benefits operation. For example, one particular day at 4 p.m. the phones started ringing at triple the historic rate. It turns out that this is about the time retired people go to their mailboxes, and one of our brilliant marketing people had sent a mass mailing with the first line reading: "Your benefits may be in jeopardy." Once we figured this out -- two hours later -- we quickly pushed out a script so all agents could handle these calls more efficiently. If we'd had an NLU summarization tool, we could have identified the trend in minutes.
Tag Content: Tagging content is necessary when a contact center uses large volumes of unstructured data to support customer interactions. Searching on tags is much faster than having to search all content in a knowledge base, which means agents -- virtual or human -- can deliver the correct answers to customers more quickly than they can without content tagging.
Taxonomy (Classification): Classification is used to identify certain types of words. If you're in the mortgage servicing business, then Columbus probably refers to a place, not a person. Similar to named entities in purpose and functionality, NLP-based taxonomy can be useful for enterprises that have very broad product catalogs. These tools can reduce the time and confusion that a customer may engage when dealing with thousands of products.
Getting a Grip on NLG
In customer engagement, NLG is typically understood as an elaborate directed-dialog implementation. The responses or follow-on questions the interface exposes to the user are created using NLG. Specifically, an NLG processor exposes text to the user (as in Web chat) or to an intermediary technology like text-to-speech (as in IVR or voice computing) that generates speech the user can listen to.
If you intend to use a voice interface, then you should put some thought into the quality of the text-to-speech solution. Punctuation, gender, energy, stress, phoneme length, intonation, syllabification, and tone can all contribute to the quality of concatenation you expose to your customers. While you may not necessarily want to explore the algorithms behind each of these factors, you'll definitely want to listen to the interface and determine what sounds good to you and your team.
Options Galore
If you're still reading, then you're probably considering an NLP implementation in your contact center. You have a lot of options, with more than 1,000 companies now offering NLP services. Some have pre-built functions that support the features I described above, while others require you to build your own features. The good news is that as contact center technologies go, none are very expensive to use. However, some can be more expensive to implement and manage than others. Selecting the platform or product that best supports your business use cases is the key to success.
