Data Analytics for Political Elections

The application of data science in predicting electoral behavior is not a new concept. Political campaigns of today are strategised and executed based on advanced forecasting models. Political candidates have used data science and predictive systems to forecast electoral polls and drive winning campaigns. In addition to analysing voting habits, advanced analytics helps in targeting voters with the right messages on the right channels.

Statistical models and advanced analytics were used in the 2016 political advertising campaigns by both Hillary and Trump. Even Barack Obama had a strong data analytics team to support his campaigns back in 2008. The 2020 US elections were no different. Advanced analytics has helped political parties to go beyond just basic polling research.

1. Analysis of complex electoral data to gauge voter sentiment

Voting habits of citizens are influenced by multiple variables. To predict voter behavior, parties must analyze voter data from multiple sources such as Census, social media channels, and third-party platforms. By leveraging artificial intelligence, political parties can gather and analyze large volumes of behavioral data to gauge the popular sentiment and probable response of voters to important issues such as immigration, healthcare, economic crisis, pandemic, and racism. This information helps in crafting campaign messages most suited to a specific type of demographic.

2. Microtargeting to attract floating voters

Elections are won by majority votes. In addition to retaining their supporters, political parties have to woo floating voters or votes of people who have not yet decided their votes. Algorithms help in microtargeting of Ad campaigns to attract these voters. Political candidates can also get the early visibility of the regions/areas that need more campaigning effort. 

3. Social media sentiment analysis

People today are more likely to express their political opinions freely on social media. Political parties can leverage advanced analytics to monitor rapidly changing engagement metrics such as popular hashtags, relevant discussions from several social media websites on the internet to strategically respond to the information. In real-time, candidates can monitor the changes in sentiment to know how voters might respond to a specific campaign message. To gain the support of young voters.

4. Data analytics for digital Ad strategies

More political parties today are moving from traditional advertising channels to digital ones that offer the option of personalization. In fact, digital ads form a major part of the campaign budget. Just the Facebook Ad spends by Trump and Biden ($ 107 Million and $94 Million) individually is more than the combined Ad spends by the candidates in 2016 election ($81 Million). Analytics enable political parties to autonomously ingest, monitor, and analyze streaming ad data from popular social media channels such as Facebook and Instagram. By tracking key metrics in real-time such as impressions and click-through rates for ads, parties can optimize their advertising spend and reach more supporters online. Campaigners can go beyond the basic demographic metrics such as location, age, etc to target highly personalized social media ads. Data Analysis enables parties to run different variations of the digital ad based on the viewer. 

Data science helps political parties in gauging their popularity, the attractiveness of their campaign message, and in choosing the right channels for targeting voters. Analysis can help political candidates in understanding voter engagement metrics and popular sentiment. It essentially helps in understanding the voter sentiment across regions/areas.