Getting an idea of what customers are saying about brands is easier than ever, but mining through a plethora of online data can be a challenge. A text analytics tool automates tasks and accurately evaluates consumer sentiment.
What Is Text Analytics?
Text analytics transforms unstructured consumer data into insights that can fuel business strategies. It expands on findings from numerical data to add context to numbers such as customer service ratings, clicks, and sales. Text analytics tools evaluate user-generated data through opinion mining and interpret the passages according to sentiment detected by software and apps.
Text analytics works through NLP or Natural Language Processing that identifies key phrases and evaluates passages for emotional coloring. AI tools work through machine learning to recognize sentiment in certain phrases and detect context and connotation. Some tools send notifications of brand mentions, extract passages, and analyze them for clues to how customers feel about brands and products.
It may seem simple to keep track of reviews and mentions of brands on social media, but given the sheer amount of data generated on the internet, even if all could be readily located, retrieving data manually would be costly in terms of time and resources.
Companies like Uber receive thousands of mentions on social media daily. Although most companies are not as high profile as Uber, even a fraction of that amount would be a challenge to track manually. Sentiment analysis is easier with tools that send notifications when brands are mentioned. When notifications arrive, webs scraping tools retrieve data and prepare it for analysis.
Brand monitoring is an ongoing process since user-generated data is renewed moment to moment. Missing a mention could have significant consequences, especially regarding pricing. If a competitor suddenly lowers prices, pricing tools can alter prices automatically to avoid losing sales.
Reviews, like pricing changes, require immediate action. Although reviews are often written for other consumers, a majority want to see if the company is listening. According to a survey by Lithium Technologies, 70% of Twitter users expect to hear back from a brand, and 53% are waiting for a response within an hour. Even those who leave negative comments and reviews can react favorably if there is a prompt answer.
Brand monitoring not only makes interacting with customers easier, but it also alerts companies to user-generated content that is valuable for sentiment analysis. Updating data regularly ensures that all information is current and reflects the present situation. External events and internal changes can quickly alter the way people view brands.
Monitoring promotions allow quick fixes that can rescue a campaign from serious flaws that initially went unnoticed. Staying alert makes damage control and can also make the collection of data easier.
Text categorization makes machine learning even smarter. It is a way of updating the “knowledge” of a text analytics tool by providing examples that refine interpretation. Giving examples is simpler than manually creating new patterns and rules. It mimics intuition and picking up on connotations in content.
Text categorization presents examples illustrating principles that may be difficult to explain. For instance, examples of certain texts can model the practice of not focusing too heavily on words that aren’t germane to the discussion. A reviewer discussing how helpful the employee at the drive-thru was when her car was stalled is a comment about customer service and not car maintenance.
Text categorization can shape processes so they can focus on the correct emphasis and connotation by providing relevant examples. This can upgrade processes quickly and prevent misinterpretations of user-generated texts.
Before social media, the way companies initiated conversations about brands and products was through focus groups. Although focus groups still have a place in the marketing toolbox, particularly if there are questions businesses are seeking detailed answers for, social media is the main source of direct customer feedback.
Much of this feedback is about products, what they like about them, what they dislike about them, what they feel can be improved, and how they use them. Social media and review sites provide a wealth of detailed information on how customers interact with companies and how they use their products.
One advantage of consumer-generated data is that it is spontaneous and authentic. People are writing reviews or featuring a product on a Facebook post because they want to at that moment and the product is on their mind. There is no reason to pull any punches on their feelings about products. Consumers on review sites and social media are often quite frank.
Negative reviews and comments are as valuable as positive ones. First, they provide opportunities to respond and interact with dissatisfied customers and potentially win them over. Second, they help zero in on what needs to be improved and how. This is particularly true of reviewers who are connoisseurs and know the industry well. Incorporating feedback into product development increases the likelihood that customers will be satisfied with new offerings.
Sentiment Fuels Brands
Brands appeal to subjective factors, such as lifestyle, mood, and a sense of belonging. It is appropriate to measure brands by quantitative factors and to analyze user-generated texts for attitudes about products. The numbers can only go so far in telling a brand story. Customer behaviors may change if they feel they are listened to and if their opinions and sentiments are incorporated into brand development.