How Predictive Analytics Is Used To Win Elections Campaigns

What Is Predictive Analytics?

The use of statistics and modeling approaches to forecast future results and performance is known as predictive analytics. With predictive analytics, data trends in the past and present are examined to see if they are likely to recur. This enables companies and investors to change where they allocate their resources in order to profit from potential future occurrences. Additionally, operational savings and risk reduction can be increased through predictive analysis.

Understanding Predictive Analytics

A type of technology called predictive analytics generates forecasts regarding some future unknowns. It uses a variety of methodologies, including artificial intelligence (AI), data mining, machine learning, modeling, and statistics to arrive at these conclusions.1 For instance, data mining is analyzing big data sets to find patterns in them. The same is done using text analysis, but not for lengthy passages of text.

Weather forecasts, video game development, voice-to-text conversion, customer support, and investment portfolio techniques are just a few examples of the many uses for predictive models. All of these applications forecast future data using descriptive statistical models of current data.

Businesses may utilize predictive analytics to manage their inventories, create marketing plans, and forecast their sales. Additionally, it aids in company survival, particularly in sectors like healthcare and retail which are characterized by intense competition.

 This technology may be used by investors and financial experts to create investment portfolios and lower risk.

Uses of Predictive Analytics

Predictive analytics is a decision-making tool in a variety of industries.

·       Forecasting

·       Credit

·       Underwriting

·       Marketing

·       Fraud Detection

·       Supply Chain

·       Human Resources 

 

Predictive Analytics in Elections Campaigns

The Lok Sabha election of 2019 changed the game for the BJP. The Saffron party earned 37.36% of all votes cast, which is the greatest percentage ever since 1989 for a political party. The BJP won 31% of the vote share in 2014 as well. Why has this winning run continued? a confluence of the "Modi wave," strategy in politics, big business, and big data.

Barack Obama, a former US president, was the first to employ data analytics extensively in an election campaign. Project Narwhal, the computer software he utilized, had quick iterations, little boundaries between developers and operational employees, extensive use of cloud technologies, and continuous testing. With the use of such tools, he was able to communicate with his voter base on Reddit, launch advertising campaigns on nontraditional platforms, and monitor their attitudes and movements.

In India, BJP relied heavily on Big Data and political analysts, using these to maximize numbers. 

Modi made a controversial assertion that "build toilets before temples" seven months before to the 2014 general election. 45% of internet users agreed with the assertion, the BJP IT team discovered. The same group that made up their potential voter base was this one. The BJP's media team utilized the data to transform that remark into "Swachh Bharat," which was enthusiastically embraced by 68% of respondents and became one of the most popular slogans India would hear in the following ten years. Because to the application of predictive analytics, everything was made feasible.

Political parties may use predictive analytics as a method to assess their potential voting base. Essentially, it uses statistical and mathematical techniques to build a model that forecasts the future based on historical trends.

The foundation of predictive analytics in political campaigns is SBD (Social Big Data). Voter engagement, spam and social impact prediction, content segmentation and categorization, and voter modeling and personalization are all applications of this technology. It is a well-known fact that Cambridge Analytica gave opposing political parties in the 2016 US election analytical support using information from more than 87 million Facebook user profiles.

One strategy for gathering data is the "user API" technique. It is completed in two steps: the first includes mining the voter's past voting history, and the second is acquiring the voter's most recent voting information. It is obvious that historical data is utilized to predict both users' current and future political opinions.

Before entering the dataset into the prediction module, pre-processing steps including data cleaning and quality enhancement are completed. The evaluation of a person's political preferences happens only after that. It is measured using two criteria: continuity and knowledgeability.

Continuity is calculated as the number of political entities that can be determined from a user's tweets throughout a certain time frame. Knowledgeability refers to a user's familiarity with politics and the extent of their work or research in the field. The major elements in establishing a user's knowledgeability are political entities highlighted from their profile and tweets.

Regular counting of political organizations is also done, mostly due to interest fluctuations. A person's ideology might shift due to influence from another party, or their interest in politics might just be seasonal.