If you want to know what someone thinks, just ask. Better yet, just listen. Unsolicited feedback is everywhere and oftentimes people will tell you what they’re thinking without any prompting.The world is a noisy place and the advent of modern communication technologies has resulted in nearly nonstop dialogue in countless locales. This oversharing of information might seem useless to some, but this “babble” actually has incredible hidden meaning, relationships, patterns and trends.Companies today would be shortsighted to ignore what their customers are saying about their products and services, in their own words. Those opinions — likes and dislikes — are essential nuggets and reveal much more insight than traditional demographic or transactional data.This is why organizations are now using text analytics to not only listen” to the voice of the customer, but to respond strategically. Text analytics allows organizations to analyze this unstructured data and harness the power of the written or spoken word by understanding what customers are thinking, expecting and demanding.

The Voice of the Customer Has Gotten Louder

The challenge in analyzing customer insights used to be gathering enough data to make informed decisions. The Internet, email, call center notes and Web 2.0 media, such as blogs, wikis and social networks, have created a new challenge: pulling out the rich content from endless streams of text data.In fact, businesses today have more opportunities to communicate with their customers, constituents and employees at a time when those customers expect and demand greater levels of intimacy from the companies with which they choose to do business. The goal of truly scalable one-to-one marketing is becoming more achievable every day.Additionally, there has been a fundamental shift in dynamics of customer opinion measurement. Whereas businesses traditionally controlled the timing and pace of conversations, Web 2.0 technologies have provided customers with a platform to express their views.People are expressing impromptu opinions on their own terms, just like they always have, but now their comments are permanently plastered in blogs, forums and wikis for everyone to see — including potential customers and competition. The Internet has essentially become a highly visible, very scalable focus group in the clouds.Nick Patience, managing analyst and research director of information management at The 451 Group, agrees. “With the rise of social media, marketing professionals can gain significant insight from brand or reputation management applications that spider the Web and analyze sentiment, registering the tone of individual consumer input and plumbing available public opinion,” he said. “Internally focused voice-of-the-customer applications use sentiment analysis with transcribed call center exchanges, providing insight into client issues on an individual or aggregate level.”

The Value of Text Analytics

Marketing researchers have long known the benefits of inserting open-ended questions into their survey questionnaires as a natural complement to traditional multiple choice and rating scale questions. However, the traditional workflow for processing this information was both labor-intensive and frequently provided results of questionable reliability. Survey research professionals scanned through thousands of responses and then allocated them to a predefined set of categories.In order to reduce data processing times, teams of coders would often worked together to categorize the same response. This caused problems of inter-rater reliability. The subtlety and nuance of language makes the analysis of text data a partly subjective exercise. Reasonable people may read the same text and have different interpretations.Text analytics provides a way to reduce both the processing time and ambiguity of measuring customer opinion. It also enables business users who aren’t survey research professionals to make use of their text data. By combing their domain expertise with sophisticated software technology, they can mine the depths of the text in their survey responses, call centers, social networks and emails and gain a crucial window in to the minds of their customers.

Natural Language Processing and Sentiment Analysis

There are various ways to analyze text, with the differences in each wide ranging. For instance, purely statistical techniques or search engine-derived approaches can yield some value, but will result in a bag-of-words output for problems of even modest complexity. Frequency counts of keywords also won’t provide the compelling, actionable information that’s desired.The science of numbers is mathematic, and math doesn’t add up with text. If it’s about words and concepts, then the appropriate science is linguistics.Statistical methods can’t properly address the ambiguity and complexity contained in human languages. Text analysis requires a process that yields to accuracy and traceability. The most comprehensive text analytics solution incorporates natural language processing, which analyzes text as spoken or written by human beings. By analyzing language syntax — understanding words in relation to each other — users can identify key concepts and how they are linked to each other within a sentence, paragraph or document. It’s the meaningful interpretation of the text that provides value, not summary statistics on the words themselves.NLP also enables more sophisticated sentiment analysis. Sentiment analysis discerns attitudes and opinions about persons, places or things. Knowing how one concept relates to another enables users to define patterns that can be detected within text. For example, in analyzing call center notes, such as “Your customer service is terrible,” sentiment analysis would identify a thing (customer service) and an associated qualifier (terrible). Creating a pattern such as “Customer service + ” would ensure that similar statements, though worded differently, would be detected.All industries have their own jargon and customized terminology. An integral part of both NLP and sentiment analysis is the ability to create and use libraries containing terms predominantly used within a given business domain. The ability to maintain these libraries or use them as starting points to derive similar but different libraries protects a user’s investment and helps deliver consistent results.The ability to understand what customers have said is valuable, but if the analysis ends there, it leaves businesses in a reactionary position. They can attempt to address a situation after the fact — a customer who is at risk for churn, for example — but it may be too late.The key to leveraging the most value from text analytics is using it not to merely report on what has happened, but to combine the information with other data sources to predict customer behavior.

Elaborate On Predictive Analytics with Text

Incorporating textual data into predictive analytics solutions enables businesses to get a truly comprehensive view of their customers.For example, it’s relatively easy for companies to analyze transactional data to learn which customers spend the most on their goods and services. Adding demographic and altitudinal data from a satisfaction survey can help further segment customers into meaningful groups and thus drive different responses or communication strategies.The icing on top is a result of text data — from call center notes, emails, survey questionnaires — that provides even richer insights into customers’ true feelings because they’re speaking in their own words and perhaps by their own initiative.By associating customer sentiment with event behavior, businesses can build models that allow them to predict future outcomes with a great deal of confidence. Knowing that customers who complained about customer service tend to leave for competitive offerings, businesses can preemptively act to retain those customers.Susan Feldman, IDC's VP for search and discovery technologies, said, “By extracting concepts, names, sentiment, and other data from unstructured information, text analytics applications can give organizations a more complete view of their customers, leading to reduced customer churn, improved productivity and increased marketing campaign results.”Incorporating text into an overall Predictive Analytics solution can greatly increase an organization’s ability to find and retain new customers, sell them additional services, assess risk and predict fraudulent activities.

What’s Next for Text?

Similar to statistical and data mining techniques, advances in computer processing technology are extending the scalability and accuracy of text analytics. However, the most exciting developments regarding text are its potential to enhance emerging types of analyses in voice and social networks. The voice of the customer now comes now comes in many forms and languages  As a result, organizations with multiple locations globally need a way to address all of those options.New advanced translation technologies help address this issue in this multisources and multilingual world.There have been improvements in voice-to-text technologies as organizations add emotion analysis (e.g., anger) and sentiment nuances (e.g., very satisfied) to their analysis of simple sentiments. Leading purveyors of voice-to-text are able to associate changes in vocal inflection with human emotions (sadness, fright, fear, grief, anger, joy, pensiveness). When combined with text analytics, businesses will begin to measure gradients of sentiment and richer emotive analysis.Although not new, there has been a proliferation of interest in social network analysis, which provides a means to detect communities, view viral marketing in real time and track the diffusion of promotions.This supplies incredible insight into customer behavior. The marriage of social network analysis and text analytics provides a remarkable new means to associate customer behavior and sentiment.Just how powerful is the insight that social networking sites provide?A recent study from LinkedIn, Anderson Analytics and SPSS Inc. showed that social networks offer a more focused population of respondents who are willing to participate in surveys and offer honest feedback on their preferences, motivations and intentions.  And, organizations can now reach millions of consumers who could never be found in focus groups or research panels.A majority of the LinkedIn respondents surveyed (66 percent) had decision-making power or influence over purchase decisions. These same members were also more active on LinkedIn and had the most “connections.” This shows that any marketing organization has the potential to efficiently reach the richest source of qualified opinions, and also reach the “influencers” of the group.Also, analyzing the profiles of 30 million LinkedIn users with text analytics identified four distinct categories of networkers. These segments — savvy networkers, senior executives, late adopters and exploring options — illustrate how marketers can identify disparate groups and then best communicate to these individuals, based on their participation levels on the networking site.

Get the Full Story

Organizations that ignore text data are missing out on incredible opportunities to initiate dialogues with their customers. Text analytics enables businesses to hear the voice of the customer more clearly and, when combined with other analytic techniques, allows them to more accurately predict their behavior.

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