Everything we do revolves around communication. On a daily basis, we send emails, texts, make phone calls, and take part in face-to-face conversations. It is now possible to use AI-based communication to understand customers, ask questions about data sets, and use analytics to understand the trends in these conversations. This is called conversational analytics.
What is Conversational Analytics?
Conversational analytics is a branch of analytics that involves, tracks, and transcribes language, converting it into qualitative data.
This type of analytics is used in many capacities. One avenue focuses on businesses and business intelligence platforms. These platforms are equipped with software that lets users question a particular data set and returns results in easy-to-understand terminology (like asking Siri what the stock market trends have been like for a particular month).
Conversational analytics also opens the door to inputting data and drawing insight from customer-chatbot interactions. Businesses can query these unstructured interactions, learn which consumer questions are most common, view the length of each interaction, or find out why customers end conversations. Most companies have large sets of data from chatbots or similar technologies, but might not be extracting the most insight from chatbot data. Conversational analytics supplies a way to condense large chatbot data sets into major trends and summaries of these interactions. This branch of analytics provides tools to identify common denominators to successful customer interactions, completed purchases, or a great customer experience.
How Does Conversational Analytics Relate to Natural Language Processing and Natural Language Generation?
Natural language processing (NLP) is the ability of computers to read text and turn that text into structured data. This includes tools like speech-to-text, machine learning (using natural language to train artificial intelligence), spell check, or related keywords on search engines.
Natural language generation (NLG) is the opposite— when computers transform structured data into text. AI software equipped for NLG can generate reports, summaries, or answer questions using common terms. Qlik Sense® is an example of a business intelligence platform with NLG functionality. The Qlik Insight Bot™ allows users to ask questions about data with natural language, promoting dialogue rather than the overuse of jargon or technical terms. The Insight Bot encourages employees to ask questions, check on company key performance indicators (KPI’s), and generally increase consideration of company data in decisions.
While separate processes, NLP and NLG are related. NLG is actually a subset of NLP. NLP takes the language it receives, processes it, and uses NLG to create a response. But it extends beyond this.
To understand text and create an appropriate response, computers must first employ sentiment analysis to determine message intent (positive, negative, or neutral) and be trained to understand grammar rules. This is where NLG takes over. NLG is responsible for answering questions in a way that makes sense. In order to do this, the program must peruse the data it is being asked to review, select summary words that convey the major trends, and decide which sentence structure to use. Finally, an articulate response is produced.
The same word can have several connotations depending on the context of a situation. This complicates training NLP and NLG processes, requiring the developer to possess a deep understanding of both machine learning and language to craft a successful solution. A computer could read a particular text but it may not correctly interpret the meaning. For example, TechTalks points out the different meanings of the word “reach.” Someone could be reaching for something that is far away from them, reaching (arriving at) a destination, or trying to reach (contact) someone on the phone. In order for NLP or NLG to be successful, it must be carefully trained to understand the context of a message.
Why is Conversational Analytics Becoming So Popular?
According to Gartner, experts predict that “By 2020, 50 percent of analytical queries will be generated via search, natural language processing (NLP) or voice, or will be automatically generated.” What is contributing to the rise in this technology?
The major factor driving the development and implementation of conversational analytics is the continuous advancements being made in the field of artificial intelligence (AI). AI is one of the most discussed areas of the current innovation landscape. Researchers are continually finding areas to expand the use of AI in business operations, marketing campaigns, manufacturing, transportation, etc. Conversational analytics is one area that innovators are seeking to refine. Specifically, how businesses of all kinds interact with databases and make information available to employees across an enterprise.
Another reason conversational analytics is becoming more common is due to a greater push for data literacy within enterprises. Business intelligence platforms equipped with conversational analytics technology have enabled employees at every level to engage with company data formerly only available to professional data scientists.
Implications for Businesses
Businesses are seeing influxes of data unlike ever before. While data scientists will always be essential for managing and interpreting data, organizations have realized the need for solutions that cater to employees who aren’t data experts. This is where NLP and NLG capabilities fit in. This democratization opens secure access to company data for employees and makes large, complicated sets of data available to those who want to engage with it to make informed decisions.
Conversational analytics grants businesses a greater understanding of who their customers are. Through collecting and quantifying this information, businesses can learn about the nature of customer interactions and where there’s room for improvement. By implementing chatbots, companies are able to emphasize customer centricity and offer an improved shopping experience.