Online content analysis is a form of research where online data or the data available on the internet and social media is analyzed to arrive at inferences about the behavior of people, social behavior, consumer behavior, competitors and much more.
The extensive usage of internet and social media by people has made it possible for the creation and collection of vast amounts of user-generated content which comes in the form of articles, blogs and social media conversations. Such user-generated content has led to huge opportunities for researchers to conduct content analysis or research on the easily accessible content on the web. Instead of spending a lot of time and money on traditional data collection methods such as interviews and surveys, researchers are now able to download the online content for further analysis without actually talking or engaging with the audience.
The data for online content analysis comes in various forms and is mostly unstructured. Availability of large volume of online content makes it possible to examine trends and patterns with higher degree of accuracy when compared to smaller amount of data collected using traditional methods.
New content analysis tools have emerged which can automatically analyze huge amount of content extracted from the web using computer programs. The technology allows companies and brands to extract online content relating to their company and competitors, making it possible to analyze trends, patterns, audience demographics, consumer purchase patterns, competitor strategies and much more.
Online content analysis provides a rich opportunity to study consumer’s profile, their preferences, their influencers, their buying patterns and much more without any intervention of a research agency.
In today’s world customers are expressing their opinions on multiple online platforms such as the social media, forums, customer review sites and much more. This online data can be extracted and analyzed real time, with high accuracy and transparency. These customer conversations are unbiased and contain valuable insights on market trends and expectations.
Online content analysis can be used to throw light on interesting aspects such as the top drivers that make customers adopt a particular product/services of a brand, discover trends from customer conversations, must-have-features in a product, competitors and their strengths, etc.
The birth of world wide web and the computers has led to the creation of user-generated data. Online content is available in the form of a blog, social media posts, articles, interviews, podcasts, customer feedback, etc. This vast amount of user-generated data, if available to be accessed and analyzed, is likely to provide huge opportunities for researchers to conduct research and arrive at valid conclusions.
Earlier, a lot of time and money was invested in collecting data in traditional methods such as the surveys, interviews, focus group discussions, etc. This data will be available only after actively engaging with the users who can provide information.
But with the advent of the internet, a lot of online data started to become easily available and could be downloaded from the web without engaging with the users. Data began to be highly available in the form of opinions, attitudes, behavior, feeling, preferences, and feedback of the online users relating to their experience with a particular product or an event.
But the data was unstructured and came in a variety of formats. This data needed further cleansing and organizing, thereby leading to the need for content analysis.
Internet is a vast ocean that offers a rich mixture of content thereby offering numerous opportunities for the application of content analysis to the online data. Online content has also evolved from simple text and messages to graphics, videos, animation, audio clips, etc. that are available in various structures such as decentralized and hyperlinked structures.
Numerous studies have been conducted in the past using online content analysis.
For example, Singh and Baack (2004) conducted content analysis to study the cultural values reflected in American and Mexican Websites. From the content analysis, it became clear factors such as the use of local terminology, family theme, tradition theme, the use of titles and rank/prestige of the company displayed on the American Web pages differed from that on the Mexican Web pages. They did not make any differentiation to cater to different audience in America.
The analysis concluded that the Mexican Websites depicted content that is more collectivist, masculine, and showed higher power. The content analysis results concluded that content on websites of two different countries strongly reflects its own culture and the values of its country.
Applying Online Content Analysis to Social Media
Online content available on social media networks has become an important part of social life. It influences the attitude, beliefs and values of people, and their behavior as well. Social media has taken an important role in Government and business organizations to engage with their customers. It is seen as an important medium that helps people to take informed decisions. Hence social media data, when converted into intelligent information, offers knowledge that can be used for designing business and marketing strategies.
Few years back, the application of content analysis on social media data had barriers as there were no proper methods to collect, select, process and analyze the data obtained from social media platforms. But in the recent years social media also has undergone changes and is now widely accessible, most up-to-date and available in electronic format. With this, many companies have designed proprietary text mining systems and sentiment analysis systems. There has been an increase in topic modeling tools, text mining tools and document clustering.
The topic model method uses the well-known Bayesian model for text document collection. In this method, the tool automatically analyses and learns the thematic topics from the data collected. The it assigns the topics to the data collected. Sentiment analysis is also used to study social media data. With the help of tools and techniques, the data collected can be classified as positive or negative,
Brands and businesses are focusing heavily on mining social media data to analyze it for deep insights. Brands are interested in increasing brand awareness and brand building by using the analysis of online social media data. The social media data is further applied to predict the future by analyzing the historical and current data using statistical tools and techniques, algorithms, and machine learning.
For example, Delta Airlines is a major airline based in The United States. Delta uses social media data to analyze its performance and to make changes to improve customer service. It monitors tweets to get feedback and opinions from its customers about food, lost baggage, booking process, cancellation process, refunds, and much more. The customer support team uses this social media data to reach out to the customers and resolve their issues.
Thomson Reuters is a company that offers competitive advantage to its customers by analyzing the twitter sentiment data. It tracks particular tweets from different companies and people thereby offering the financial professionals an overview of the total positive and negative sentiments related to a particular company.
Online Content Analysis in an Era of Big Data
Big Data refers a massive volume of data, either structured or unstructured, that is so huge that it becomes impossible to process the data using traditional database and software. Though it is difficult to set a specific measurement, typically when data storage amount exceeds one terabyte (TB) it can be called as a big data. Big data is characterized by the four V’s, volume, variety, velocity, and veracity.
Analysis of big data is a complex process where a large and a variety of data sets are examined to uncover additional information that includes customer patterns, market trends, correlation, customer buying habits, customer preferences, competitor marketing strategies, etc. which is a nothing less than a goldmine that helps organizations to take marketing and business decisions.
Researchers, scientists, financial advisors, modelers, and many other professionals in the analytics field have started using big data analytics to break down large amounts of data that is gathered from variety of sources such as social media, online transactions, web servers, surveys, and emails.
The emphasis of big data is not just on the volume of the data, but revolves around how a company makes use of the collected data. A company uses big data analysis for various purposes such as decision making, exploring new opportunities, innovation, prediction, customer satisfaction, cost reduction, business expansion, etc.
Example of Big data used in companies:
Netflix uses big data analytics for targeted advertising. It has more than 100 million subscribers, whose data is huge enough to study and create targeted suggestions for each subscriber. Based on the past search and watch list, Netflix will be able to suggest the movies that should be watched next. This analysis of the big data is used to provide insights into the interest of the subscribers.
Content analysis is evolving as an important milestone for business intelligence. Online content analysis is gaining attention in the recent years, and many businesses have learnt the importance of online content analysis. With the increasing importance and need of online content analysis, better and improved technologies are emerging to perform analytics in an efficient manner.
With the continued advancement in digitization, people are spending more time with digital technology, thereby leaving behind a huge amount of data. Audience experience will be recorded as useful data indicating their preferences, attitude, behavior, buying habits, location, interests, etc. This data will be of interest to business who can analyze and interpret to arrive at important business decisions. It can go such an extent that online content analysis will become the primary and most importance source for business intelligence.
In the next few years, many tools and techniques to scrape data from millions of web pages will evolve. The analytical software has to be collaborative, proactive, insightful and highly equipped to handle big data. Along with the software tools, new networking structures need to emerge to handle such humongous data that will flow into the business systems. Advancement in network, will enable to build advanced tools and store large amount of data.
As the world continues to progress towards big data, online content analysis will take the centerstage for businesses. It is likely to be automated to a greater extent and will be utilized aggressively by modern businesses.