Structuring Your Text Data

Structuring Your Text Data

Imagine your company just launched a new product, a new feature or maybe a new marketing campaign. You surely want to know what's working and what's not.


Customers literally tell you what they loved or hated about your products and services and what they want — everywhere, from Amazon and Yelp reviews to Facebook, Instagram, and Twitter. But listening to customers, as they shop online, use social media and interact with contact centers, websites and apps, is not as easy as analyzing structured information, such as customer account details and employee wages.


Here's why: Customer reactions are scattered across various platforms and are not easy to quantify or to fit into neat tables with rows and columns. Also, massive amounts of data pour-in from various sources. Amazon, for example, has a monumental task in trying to sort through and analyze all the reviews and social media posts about the more than 175 million items it sold during this year's Prime Day event.


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AI and Automation

But automation and advanced text analytics are providing a way to harness big data. Organizations can now integrate unstructured data from various sources and use artificial intelligence (AI) to extract meaning from it. The process starts by automatically extracting and classifying information from text sources, such as online product reviews, social media posts, and contact center transcripts. Next, AI can be used to identify patterns and topics of interest, and finally to output actionable insights.


The potential benefits from using AI are immense. It can provide an early warning of trouble by showing what customers are complaining about or shed new light on why a particular product is not selling well. For example, structured data might show a slowdown in a particular product's sales, but text analytics can explain why.


No wonder that market research company Forrester expects a growth of 20 percent in text and speech analytics technologies as companies scramble to mine data from customers' digital and call-center interactions.


According to Gene Leganza, vice president and research director at Forrester, response to customer surveys will go down and several companies will go beyond these surveys. “These firms will add data science and analytics skills to their customer experience teams," he noted.


Retailers are not the only ones that can benefit from text analytics. Healthcare companies can analyze unstructured patient data from doctors' notes and patients' calls in order to understand how well a certain drug is working or what the likelihood is that a person will develop a certain disease. And human resource departments can get insights into employee experiences and expectations by analyzing what they say on company surveys and job portals.

Understanding Customer Sentiment

Customer feedback is great, but companies need to understand the meaning behind what customers say, and how they feel about their products.


Fortunately, advances in AI are making sentiment analysis possible. The science behind gauging customer mood is based on natural language processing (NLP) — a suite of techniques and algorithms that give computers the ability to read, understand and derive meaning from human languages.


Using NLP, sentiment analysis algorithms categorize pieces of writing as positive, negative or neutral. Understanding the tone of customer statements can open new possibilities, such as tuning your marketing campaigns to address negative mentions before they become a social media crisis or encouraging highly satisfied customers to become brand ambassadors.

No Data Scientist Required

To analyze customer reactions spread across platforms, organizations typically begin by scraping data — the process of importing information from a website into a spreadsheet or local file saved on your computer. They use tools and Python libraries, such as BeautifulSoup, to parse a web page to grab textual data and write the information to a structured format such as in a spreadsheet. Next, they clean the data and write an algorithm to collect all customer generated posts with previously defined keywords . Finally, their algorithm will be ready to analyze individual customer sentiment.


While organizations have depended on data scientists for years, advances in AI, NLP and analytics are now making it a breeze to analyze text data quickly. Several off-the-shelf tools allow — such as TalkWalker, Repustate, Lionbridge and Lexalytics — automate sentiment analysis in unstructured data and allow users to work without any knowledge of AI, analytics or even coding experts. Users simply choose keywords or hashtags for which they want to collect data and these tools identify all mentions as positive and negative, along with their numerical and percentile summary. These tools are capable of handling most, if not all, text analytics requirements.


Whether your product is sold in brick-and-mortar stores or online — or both — text analysis from unstructured sales data coming from a variety of platforms, including online reviews and social media posts, can provide new insights into what is driving customer behavior.


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