Decoding consumer sentiments through algorithms

by June 13, 2016 0 comments

A person’s emotions can be observed from his facial expressions, whether he is happy, angry, sad, or surprised. With the digital revolution and swelling user-base of social media, these emotions are now expressed virtually in the form of emails, surveys, reports, tweets, and comments on different online platforms. Given the explosion of information on the web, consumer data is now spewed in large quantities, and it becomes impossible to structure and analyze it without any tool or technique.

Text analytics is a practice of using technology (which applies natural language processing and/or computational linguistics techniques) to understand consumer sentiments emanating from various sources. It consists of algorithm-based tools to help marketers know what exactly their customers think, predict future and chalk out effective strategies. The algorithms track the unstructured textual data to enable organizations’ glean insights. The data might come in any form including documents archives, survey results, blog posts, social media comments, and customer reviews, etc.

The Business Value
Analyzing the information helps organizations to interpret hidden patterns, trends, and the factors influencing the consumer behavior. Text analytics has been used in different industries including hospitality, retail, airlines, BFSI, IT and healthcare to make smarter business decisions. However, understanding and using the text is a considerable challenge, and once it is converted into conventional data, any statistical method can be used for further analysis.

Let’s first understand the initial steps to transform the unstructured data into its structured form.

Extraction: Once the data is received, finding important terms within the text is the first step towards interpretation. These could be words, phrases or information to perform analysis. For example three customer responses in a data can be interpreted as follows. The first response says “the product should have XYZ characteristics. Second response expects the product is expensive, while the third response anticipates the price should be between a-b. The underlined words in above three responses are the consumer sentiments which form the basis of our analysis.

Grouping and Tagging: The next step after identifying the words is to group them into similar characteristics in order to reduce the data for easy interpretation. Further, the words are tagged with codes for statistical input and analysis.

The above two steps convert the raw data into categories which becomes the lifeblood of any analysis. The patterns coming out of the data offer important pieces of information about customers, products and organizations. According to a survey, the structured data represents only 20% of the information available to an organization and 80% of all the data is unstructured.

Market Size and Growth Potential

The text analytics software aid in monitoring social media, and hear the customer voices which in turn help in developing or revamping products. These tools can analyze million words per minute. Companies such as IBM, Microsoft, Oracle, HP, SAS and Tableau Software are the leading players in this arena offering text analytics tools. Moreover, the open-source programming language, R provides two packages-TM and Sentiment, for working on unstructured texts.

According to a Harvard Business Review study, 35% of the leading-edge companies favor text analytics in improving organizations’ customer experience, while 14% of the lagging companies evinced the importance in this continent. The use of text analytics is proliferating fast from delving the customer insights on a concept car on facebook or twitter to the feedback given by car owners after a service.

According to a research firm, the text analytics market is expected to reach US$6.5 billion by 2020. Region-wise, North America currently accounts for about 40% of the global market, owing to its early adoption. The United States occupies the largest share (Estimated above 77%) in the North American text analytics market.

Further, some of the major trends witnessed in market are:

1. Predictive analytics is the major application segment that uses text analytics in different industries, with a notable usage in retail and healthcare.

2. Text analytical software contributes one-third revenue amongst all industries. The market has registered a surge in demand on the back of cloud-based models, which are burgeoning globally.

3. Small scale enterprises mostly rely on on-premise model which is growing at a CAGR of 16.6% from 2014-2020.

The Road Ahead
The text analytics market is gaining prominence as organizations understand the importance of social and enterprise text in designing marketing strategies, improve campaign success and customer service. According to Gartner, by 2018 over 80% of organizations will leverage user-generated content as part of their corporate learning strategy.

Text analytics bridges the gap between organizations and customers by understanding sentiments. The patterns emerging from consumer emotions will play a significant role in analyzing brands, managing regular conversations with customers, amp up marketing efforts, and laying the foundation for bigger plans down the road.

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