Abstract
The 2024 Indian general election marked a fundamental transformation in political campaigning, where data analytics emerged as perhaps the most decisive factor in electoral strategy. This article examines how political parties in India have evolved from traditional grassroots mobilization to sophisticated data-driven campaigning, leveraging analytics to understand voter behavior and craft hyper-local messaging that moves beyond broad demographics to specific constituencies and even ward-level campaigns. Drawing on the comprehensive Lokniti-Centre for the Study of Developing Societies (CSDS) study of digital campaign strategies, alongside platform transparency data from Meta and Google, the analysis reveals how the Bharatiya Janata Party (BJP) has operationalized data analytics through its SARAL (Sangathan Reporting and Analysis) app and integrated digital infrastructure to conduct what amounts to 543 distinct constituency-level campaigns. The article further examines the emerging ecosystem of political analytics firms, the application of artificial intelligence and machine learning to voter sentiment analysis, and the privacy implications of this unprecedented data collection. The findings suggest that data has become the new battleground of Indian elections, with profound implications for electoral fairness, democratic deliberation, and the future of political communication in the world's largest democracy.
Keywords: data analytics, political campaigning, Indian elections, micro-targeting, voter behavior, SARAL app, artificial intelligence
1. Introduction
If the 2014 election was India's "Facebook election" and 2019 its "WhatsApp election," then 2024 will be remembered as the year when data became the decisive battleground. A comprehensive study by the Lokniti-Centre for the Study of Developing Societies, published in The Hindu in August 2025, has shown how political battles have moved from dusty rally grounds to data dashboards, with digital advertising and analytics becoming absolutely essential in shaping voter behaviour (Lokniti-CSDS, 2025).
The numbers reveal more than a spending gap; they signify the arrival of a new political era, where a war of capital and data has nearly overtaken traditional door-to-door campaigning (Lokniti-CSDS, 2025). The data shows a stark strategic divide due to the vast financial disparity between political parties, pitting the Bharatiya Janata Party's (BJP) industrial-scale, micro-targeted saturation campaign against the Congress's focused, narrative-driven approach (Lokniti-CSDS, 2025).
This transformation is rooted in the recognition that the Indian voter is no longer a citizen to be persuaded through reasoned public debate but a consumer to be targeted and a demographic to be managed (Lokniti-CSDS, 2025). The fundamental shift is that political communication has been torn from public debate and grafted onto the logic of the marketplace (Lokniti-CSDS, 2025). This article examines the role of data analytics in modern Indian campaigns, exploring how parties collect, analyze, and deploy voter data to craft hyper-local messaging strategies that operate at unprecedented levels of granularity.
2. The Evolution from Panna Pramukh to Data Analytics
2.1 The Traditional Grassroots Model
The foundation of Indian political organization has long been the booth-level structure, epitomized by the BJP's "panna pramukh" (page chief) system. Under this model, one dedicated worker is assigned responsibility for every page of the voter list containing 30 to 60 voters, with complete accountability for safeguarding the booth (Daily Pioneer, 2026). Each panna samiti maintains regular contact with voters on their page, categorizing them as supporters, undecided, or opponents (Category A, B, and C respectively), and works to persuade, cajole, and mobilize them (Deccan Chronicle, 2026).
This system represents the gold standard of grassroots political organization, first successfully deployed in Gujarat and later replicated across Uttar Pradesh, Karnataka, and West Bengal (Daily Pioneer, 2026). Panna pramukhs meet voters personally, attend to their civic grievances, and form WhatsApp groups to maintain year-round contact (Deccan Chronicle, 2026). They use electricity consumption data to identify socioeconomic clusters—a household with three air conditioners receives messages about GST; a poorer household hears about welfare schemes. This personalized, human-centric approach has been the bedrock of successful Indian political campaigns for decades.
2.2 The Digital Transformation
However, this grassroots system has been digitally supercharged. The BJP's SARAL (Sangathan Reporting and Analysis) app, which in January 2024 had more than 2.9 million downloads from the Google Play store, collects granular voter data—mobile numbers, addresses, age, gender, religion, caste, parliamentary constituency, voter identity numbers, and professional and educational details (Campana, 2025; Pulitzer Center, 2024). Users can also upload their photographs (Campana, 2025). The app was used actively by the party to identify booths where support for the BJP was lowest and thus to drive all efforts in a geographically specific way through rallies and targeted campaigns (Campana, 2025).
The SARAL app represents a significant evolution in political data collection. Developed by the party's IT cell, the application allows the central leadership to monitor and analyze grassroots data uploaded by booth-level workers in real time—giving the BJP an edge over its rivals in booth management (Times of India, 2022). The party's head of information technology and social media, Amit Malviya, reportedly referred to SARAL as an "election-winning machine" at a 2023 tech conference in Delhi (Pulitzer Center, 2024).
The app has been deployed with remarkable sophistication. In Uttar Pradesh, the party focused primarily on 35,000-40,000 booths—of the total 1.75 lakh booths across the state—where it didn't perform well in past polls. Each MP was tasked with taking care of 200 booths, while each MLA was allocated 50 booths to collate data and feed it into the mobile app (Times of India, 2022). Public representatives were asked to collate and upload data from "weaker" booths under specific heads: reasons for the party's poor show, the demographic profile of the booth, and influential residents whose appeal could be of electoral use (Times of India, 2022).
The party has further institutionalised this digital evolution by appointing "WhatsApp Pramukhs" and "Mann Ki Baat Pramukhs" at the booth level to disseminate party messages and maintain direct contact with beneficiaries of government schemes (NDTV, 2024). This integration of traditional organisational structures with digital tools represents a new paradigm in Indian political campaigning.
3. The Data Infrastructure: Collection, Analysis, and Deployment
3.1 The SARAL App and Voter Data Collection
The SARAL app's data collection practices have raised significant concerns among privacy advocates and electoral experts. When first opened, the app asked users to provide all their personal information, including phone number, address, age, gender, religion, caste, social categories such as scheduled tribes and castes, parliamentary constituency, voter identity number, and professional and educational details (Campana, 2025; Pulitzer Center, 2024).
The app also has the function of recording people's presence at BJP events, providing the party and its workers with a "big brother" power over its voters (Campana, 2025). This is part of a broader global context in which the power of media and their owners is exponentially growing through the emergence of new technologies that are being shaped around the individual both as a data source and as a data destination (Campana, 2025).
The scale of this data collection is unprecedented. The BJP, which has claimed to have at least 180 million members, told The Times of India that the app's aim is to digitize some of the party's operations and better communicate with its workers across India by "conveying the policies and the programmes of the party" (Pulitzer Center, 2024). Party workers launched door-to-door campaigns to register ordinary citizens on the app, often combining SARAL registration with voter registration and other government welfare schemes (Pulitzer Center, 2024).
In Uttar Pradesh, one of the key states with 80 parliamentary constituencies, the BJP set registration targets for local leaders, warning that failing to meet their targets could impact their position within the party (Pulitzer Center, 2024). By July 2023, nearly 214,775 people had downloaded the app in Uttar Pradesh, and 192,749 had entered their registration details (Pulitzer Center, 2024).
3.2 Booth-Level Verification and Quality Control
The BJP has demonstrated remarkable sophistication in ensuring the quality and authenticity of its data. In Bengal, the party launched a cross-checking process using the SARAL app to weed out "ghost members"—fictitious booth committee members who existed only on paper (Telegraph India, 2025).
The process is remarkably thorough. The party has deployed 10,000 senior leaders, called "e-bistaraks," tasked with cross-checking booth committee members outside their own jurisdictions. Each e-bistarak verifies about 100 members among the approximately one million booth committee members and presidents. The verification process involves meeting a member in person, asking them to blink their eyes, and taking a photograph directly through the SARAL app. Once the photograph is taken, a one-time password is automatically sent to the member's phone. After the OTP is fed into the app, the exact location where the photograph was taken, including longitude and latitude, is recorded (Telegraph India, 2025).
This "eye-blinking verification" system serves a dual purpose: it ensures that only genuine party workers are listed as members, and it records their precise location to ensure they are assigned to the correct booths. "At least a ghost can't blink its eyes!" a BJP leader remarked (Telegraph India, 2025). The party aims to form committees in 70,000 of Bengal's 80,000-odd booths, recognizing that booth committee members are the backbone of any party's electoral success (Telegraph India, 2025).
3.3 Political Analytics Firms
The demand for sophisticated data analytics has spawned a growing ecosystem of political analytics firms. One such firm, People's Insight Pvt Ltd, co-founded in 2024 in Mohali, has distinguished itself in India's crowded polling space (Indian Express, 2025).
The firm's analytical framework is built on Microsoft's Power BI, allowing political parties to view real-time updates and assess their vote share instantly—something that earlier took 15-20 days in long printed reports (Indian Express, 2025). The live dashboards became a game-changer, letting clients tweak parameters and track thousands of variables with unprecedented speed (Indian Express, 2025).
What began as a bootstrap venture quickly stood out in India's crowded polling space. While traditional agencies relied on sample sizes of 10,000-15,000 per state, People's Insight scaled up to millions of responses—sometimes as high as 10 million in large states. The firm factored in caste, age, gender, category, and past voting turnout to develop projections that proved repeatedly accurate—precise enough at times to spark controversy (Indian Express, 2025).
Beyond just numbers, the firm offers strategic insights to political parties—helping them identify vote banks, track voter behaviour, and focus campaign resources more effectively (Indian Express, 2025). From predicting the Ludhiana bypoll with 95 per cent round-wise accuracy to covering elections in Bihar and Tarn Taran, the firm is steadily expanding its reach (Indian Express, 2025).
3.4 AI-Powered Sentiment Analysis
Artificial intelligence has emerged as a critical tool in data analytics for political campaigns. During the 2024 Lok Sabha elections, AI-powered tools were used not only to connect with voters and engage them but also to get data-based details on whether a candidate's content had performed well in a particular area that day (Indian Express, 2024).
The BJP's social media team used AI for sentiment analysis of Facebook and X comments, further analyzing feedback on any content put out among voters to make it more engaging and identify what "clicked" with citizens (Indian Express, 2024). One AI-powered tool was used even to locate voters' pictures with candidates—creating a link where anyone could scan their face and receive their pictures automatically if they had been photographed with the candidate. The AI recognized faces "in a sea of lakhs of photos" (Indian Express, 2024).
The Congress's social media team similarly used AI to analyze suggestions and feedback they received, mapping their "5 Nyayas" (five principles) by assigning coefficients to every issue based on keywords. This allowed them to categorize every issue into the five overarching Nyayas, then carry out sentiment analysis of Facebook and X comments on various issues to see what was "clicking with the citizens" and what ground reality they were facing (Indian Express, 2024).
AI tools were also used to delegate work and coordinate between teams, based on availability and core competence of team members, ensuring tasks reached a logical conclusion at the earliest (Indian Express, 2024).
4. The Advertising Divide: Volume, Targeting, and Impact
4.1 The Volume Chasm
The Lokniti-CSDS data first reveals a chasm in advertising volume. On Meta platforms (Facebook and Instagram), the BJP ran 41,127 ad campaigns to the Congress's 1,041—a 40:1 ratio. On Google and its affiliates like YouTube, the ratio was a massive 24:1, with the BJP running 225,695 ad campaigns to the Congress's 9,251 (Lokniti-CSDS, 2025).
Yet spending tells a different story. The BJP's Google spend of Rs 56 crore was roughly 2.7 times the Congress's Rs 21 crore (Lokniti-CSDS, 2025). This gulf between the ad volume ratio (24:1) and the spending ratio (2.7:1) is the key to understanding their different strategies. The BJP ran a campaign built on low-cost, high-volume ads; Meta data shows that 65% of the BJP's ads cost less than Rs 1,000 (Lokniti-CSDS, 2025). This was not just cost-efficiency on the BJP's part; their goal was to optimise for broader visibility—the digital equivalent of painting every wall with party symbols to create an overwhelming sense of inevitability (Lokniti-CSDS, 2025).
The Congress's strategy, on the other hand, was born of scarcity. It invested in fewer, more expensive ads. On Meta, 34% of its ads were in the high-cost bracket of over Rs 1,00,000. On Google, 27% of Congress ads were budgeted above Rs 1,00,000, while 98% of the BJP's ads were budgeted below that threshold (Lokniti-CSDS, 2025). Unable to saturate the environment, Congress had to be more creative in how it used its limited resources. To some extent, this worked: 21% of its ads surpassed 10 lakh impressions, compared to just 3% for the BJP (Lokniti-CSDS, 2025).
4.2 Micro-Targeting and the Atomised Voter
The BJP's campaign was intensely micro-targeted. In the crucial first phase, it micro-targeted 96% of its ads, while Congress micro-targeted only 32% (Lokniti-CSDS, 2025). The saffron party digitally replicated its "panna pramukh" ground game, effectively fighting 543 distinct elections (Lokniti-CSDS, 2025). Congress, by contrast, fought one national election.
The BJP also targeted a younger demographic by focusing 53% of its ads on Instagram, while Congress ran 86% of its ads across both Instagram and Facebook simultaneously (Lokniti-CSDS, 2025). This turns the voter into a consumer; a collection of data points to be targeted with bespoke messages, not a member of a public to be addressed with common arguments (Lokniti-CSDS, 2025).
The BJP's geographical and linguistic reach was notably expansive. The party targeted voters in 35 different states and Union Territories, essentially spanning the entire country, with ads appearing in 12 languages (Dailyhunt, 2025). Hindi did dominate the BJP's ad language mix (around 70%), but the remaining 30% were spread across a dozen other languages, from Odia and Bengali to Kannada and Gujarati (Dailyhunt, 2025). The Congress, on the other hand, had a far more limited regional focus, targeting only 20 states/UTs, with about 92% of its Google ads in Hindi alone (Dailyhunt, 2025).
The BJP's Google ads frequently drilled down to specific locales, even targeting individual cities, districts, or pin codes within states. Only a negligible fraction of BJP's ads were generic all-India appeals (the party targeted "India as a whole" in just three ads), preferring to customise messaging locality by locality (Dailyhunt, 2025). By contrast, 66% of Congress's Google ads were blanket nationwide campaigns, with far less granularity (Dailyhunt, 2025).
4.3 The Role of Micro-Influencers
The data analytics revolution has also transformed the influencer economy. Political parties now rely on decentralized messaging through "micro-influencers" from small towns using Instagram and YouTube, both highly popular with youth and first-time voters (RT International, 2024).
The shift is significant. According to a social media manager interviewed by RT International, WhatsApp was previously associated with negative messaging, while Instagram Reels are positive by their very nature, especially those by young women (RT International, 2024). Micro-influencers offer a cost-effective alternative to traditional advertising media, allowing parties to maximize their outreach while optimizing their marketing budget. With relatable personalities, personal connection, and hyper-localized appeal, these influencers can effectively disseminate party messaging and sway public opinion in a way that resonates with target voters in their respective communities (RT International, 2024).
Social media vendors report that over 60% of the market is now captured by Instagram, with only a tiny portion left for X (formerly Twitter). X's frequent algorithm changes have made it difficult, and this is a reason political parties want to go with micro-influencers on Instagram (RT International, 2024). Influencers have hiked their rates as demand grows, now charging at least 30% more compared to their per-Reel rates in December 2023 (RT International, 2024).
The micro-influencer model operates in a regulatory grey area. Work often comes from a party supporter rather than the party itself, so the funds spent are not counted in the budget of either the candidate or their party and do not violate the Election Commission's spending limits (RT International, 2024). Furthermore, when politicians or parties promote content in print or digital media, it's typically disclosed if it's paid content. However, influencers often don't reveal if their content is paid for, leading to potential bias (RT International, 2024).
5. Institutional Capture and Electoral Fairness
5.1 The Funding Gap
The BJP's ability to mount such a massive digital campaign stems from its capture of the country's economic and institutional resources (Lokniti-CSDS, 2025). Data from the invalidated Electoral Bonds scheme shows the BJP encashed Rs 6,060.5 crore, representing nearly 48% of total funds—more than four times the Rs 1,421.9 crore received by the Congress (Lokniti-CSDS, 2025).
This financial disparity translates directly into digital capacity. "Like money, data can also have undue influence on voters, which can lead to manipulation of elections, including profiling of voters, targeted campaigns, and potential deletion of voters," said Srinivas Kodali, an independent privacy researcher. "Privacy shields voters from undue influence and manipulation. The secret ballots are there to ensure that no one knows for whom voters have voted. Without the privacy of voters, there is a question mark on free and fair elections" (Pulitzer Center, 2024).
5.2 The Silent Period Violations
This dominance extends to the rules of the game. During the 48-hour "silent period" before polling—a window intended to ensure free and fair voting by prohibiting campaigning—the BJP out-advertised the Congress 22 to 1 on Google, running 179,070 ads to the Congress's 8,149 (Lokniti-CSDS, 2025).
An analysis of a sample of these ads found direct violations of the Model Code of Conduct (MCC). Of the 958 sampled Congress ads, 698 were in violation (73%). For the BJP, out of 2,996 ads sampled, 492 were in violation (16.4%) (Lokniti-CSDS, 2025). While a higher percentage of Congress ads broke the rules, the BJP's immense volume means it committed a far greater absolute number of violations (Lokniti-CSDS, 2025). These repeated violations reveal gaps in current election regulations concerning digital platforms (Lokniti-CSDS, 2025).
5.3 The Government-Party Nexus
The BJP's data advantage is further amplified by its use of government resources. The Saral app's privacy policy states that it collects personal information provided voluntarily, alongside information about users' devices and, with consent, geographic location (Pulitzer Center, 2024). It says it uses this information to maintain and improve the app, and that it may use aggregated and anonymized data "for analytical purposes, marketing, or to improve our services." It may also provide data confidentially to third-party services (Pulitzer Center, 2024).
"This gives the ruling party an unfair advantage by virtue of two things," said Prashant Bhushan, a public interest attorney at India's Supreme Court. "Only the prime minister's office can use the national broadcaster for his talk show, certainly with much more visibility and advantages. Secondly, BJP has much more money through electoral bonds and through government patronage" (Pulitzer Center, 2024).
"This data, gathered through collaboration between government and party machinery, provides the BJP with insights into perceived voters and non-voters," said Kodali. "It enables the party to strategically allocate resources to sway opinions in critical constituencies. As incumbents, they also have government resources as a part of election strategy" (Pulitzer Center, 2024).
6. Academic Perspectives on Election Analytics
6.1 Machine Learning and Predictive Modeling
The academic community has devoted increasing attention to the application of data science to Indian elections. One study, "Decoding the Ballot: Predicting Indian General Elections with Machine Learning," leveraged historical voting data and socio-economic indicators from the Socioeconomic High-resolution Rural-Urban Geographic (SHRUG) dataset and the Lok Dhaba database to forecast electoral results (Zenodo, 2024).
The study employed several models, including Random Forest, Gradient Boosting, and Decision Tree. With an accuracy of 99.89%, the Random Forest model outperformed the rest, attributed to its ensemble learning strategy, which reduces overfitting and increases predictive power. The Decision Tree and Gradient Boosting models achieved accuracies of 98.75% and 99.78%, respectively (Zenodo, 2024).
The study faced challenges such as computational complexity and potential bias introduced by the dataset, particularly due to the historical dominance of the Indian National Congress party. Despite these challenges, the models provided valuable insights into voter behaviour and electoral trends. The implications of this study are significant for political analysts and campaign strategists, as accurate predictions can guide the development of targeted campaign strategies and enhance understanding of electoral dynamics (Zenodo, 2024).
6.2 Markov Chain Analysis of Voter Behavior
Another study, "Markov Chain and Data Analysis on Elections in India," explored data analysis and Markov chains applied to Indian election data to uncover trends in voter behavior, investigate how caste and geography affect voting behavior, and offer perceptions of how politics are changing (IEEE Xplore, 2024).
The results showed essential trends in voter behavior and emphasized how party preferences are influenced by caste, geography, and incumbents. The study also revealed differences in voter turnout between caste and regional groups, raising important issues regarding representation and equity in Indian democracy. The study offered a data-driven forecast of the electoral environment for the 2024 election by utilizing insights obtained from examining previous polls (IEEE Xplore, 2024).
6.3 Voter Turnout and Electoral Statistics
Research has also demonstrated that voter turnouts contain crucial information that can be leveraged to predict several key electoral statistics with remarkable accuracy. Using the recently proposed random voting model, researchers analytically derived the scaled distributions of votes secured by winners, runner-ups, and margins of victory, demonstrating strong correlation with turnout distributions (Harvard University, 2025).
By analyzing Indian election data—spanning multiple decades and electoral scales—researchers validated these predictions empirically across all scales, from large parliamentary constituencies to polling booths. Further, they uncovered a surprising scale-invariant behavior in the distributions of scaled margins of victory, a characteristic signature of Indian elections (Harvard University, 2025).
7. Privacy Concerns and Democratic Implications
7.1 Data Privacy as a Democratic Concern
The scale of data collection by political parties has drawn criticism from privacy advocates and opposition parties. Congress president Mallikarjun Kharge has alleged that the government's various digital initiatives represent "snooping, surveilling, scanning and peeping to confiscate, control, command and monetise citizen's rights" (The New Indian Express, 2025). Congress spokesperson Pawan Khera argued that "safety is an excuse, the target is privacy," alleging that the government sought to create "a surveillance state" (Hindustan Times, 2025).
"The major concern is micro-targeting based on caste and religion," Kodali told Rest of World. "The more any political party knows about the voter, the more powerful they get. It disturbs the playing field when one political party has such a huge amount of data" (Pulitzer Center, 2024).
7.2 The Threat to Democratic Deliberation
The CSDS-Lokniti data describes a new political paradigm. The BJP has perfected a digital machine of volume, velocity, and granularity. Congress was forced to counter with high-impact critiques that struggled to pierce reality bubbles designed to be immune to facts (Lokniti-CSDS, 2025).
The fundamental shift is that political communication has been torn from public debate and grafted onto the logic of the marketplace (Lokniti-CSDS, 2025). This triumph comes at a terrible price. The strategy of deepening social divisions to win elections creates a "political vocabulary" of polarisation. As information warfare experts warn, this same vocabulary becomes a "technical grammar" for foreign adversaries, who can easily weaponise the divisions sown at home (Lokniti-CSDS, 2025).
The 2024 election has created a chilling trade-off: short-term electoral gain for long-term national vulnerability (Lokniti-CSDS, 2025). The challenge ahead is not merely to win the next election, but to reclaim the very meaning of what an election is supposed to be: a free, collective, and conscious choice about our shared future (Lokniti-CSDS, 2025).
8. Conclusion
The 2024 Indian general election marked a fundamental transformation in political campaigning, where data analytics emerged as perhaps the most decisive factor in electoral strategy. The BJP's operationalization of data through its SARAL app, integrated digital infrastructure, and vast financial resources has allowed the party to conduct what amounts to 543 distinct constituency-level campaigns, moving from a "one-size-fits-all" approach to hyper-local messaging tailored to specific demographics, castes, and even individual booth clusters.
The Congress, constrained by financial limitations, adopted a high-impact strategy that achieved notable success in terms of engagement per ad but could not compete with the BJP's industrial-scale saturation campaign. This disparity reflects a broader asymmetry in the Indian electoral landscape, where the ruling party's access to financial resources and government data provides a significant—and potentially decisive—advantage.
The emergence of political analytics firms, AI-powered sentiment analysis, and machine learning predictive models further demonstrates how data has become the new battleground of Indian elections. These tools offer unprecedented insights into voter behavior, enabling campaigns to target voters with surgical precision and craft messages that resonate at the individual level.
However, this transformation carries profound implications for democratic deliberation. The shift from treating voters as citizens to be persuaded through reasoned argument to treating them as consumers to be targeted through algorithmic optimization raises fundamental questions about electoral fairness, privacy, and the long-term health of India's democracy. The lack of regulatory frameworks governing political data collection, the absence of transparency in micro-targeting, and the blurring of lines between government and party resources all demand urgent attention.
The 2024 election has created a chilling trade-off: short-term electoral gain for long-term national vulnerability. The challenge ahead is not merely to win the next election, but to reclaim the very meaning of what an election is supposed to be: a free, collective, and conscious choice about our shared future. This requires addressing the fundamental asymmetry in digital capacity, regulating the unregulated data marketplace, ensuring transparency in data collection and micro-targeting, and holding platforms accountable for their role in shaping democratic outcomes.
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