Is Social Media Analytics Redundant Today?
Over the last decade, there has been an explosion in social media data, and at the same time, AI/ML models are also getting better at predicting people’s interests and purchasing habits. For example, social media data can be processed and analysed using AI models to find meaningful correlations to precisely target products and services to specific users. Businesses can also leverage analytics models on data collected from social networks and use computational frameworks like Apache Hadoop for analysing large volumes of data.  A lot of companies have been using social media data from Twitter, Facebook, Instagram LinkedIn, Snapchat to improve their marketing ROI and target consumers by analysing users’ platform behaviour in relation to demographics. But, there’s a caveat! While social media data provides great insights into the behaviour of users, they may also face user privacy-related issues. Social Media Analytics Vs Privacy Violations Ever since the Cambridge Analytical scandal and the introduction of regulations such as GDPR, it has become more stringent for companies to leverage social media analytics for targeted advertising. The impact of the Cambridge Analytica scandal was a catalyst in this regard when it was found that third-party applications on Facebook were mining users’ data for political campaign profiling. In the past, third-party data aggregators scraped social media sites and collected personal sensitive data, which was then resold to companies. Now, with the introduction of regulations that prohibit that practice, will social media analytics become redundant or less effective for companies? Events such as the Cambridge Analytica/Facebook scandal, massive security incidents like Equifax breach, and later on GDPR paved the way to tighten the norms on how personal data is collected, stored, and processed. In the past few years, the lawsuits against these tech companies on privacy norms have only strengthened the trend. Companies around are therefore preparing to become more compliant with the regulations and what data they collect of users. Facebook, for example, has now become more transparent to users specifying what data they collect and what information they provide third-parties for their advertising campaigns. Social media profiles have personally identifiable information and other sensitive data that can be used by data scientists to create models for specific products, which can generate more sales. The challenges with collecting social media data can depend on the kind of data collected (non-personal social data or personal data) and how the data is utilised. It also depends on the applicable laws and regulations in geography. For instance, in Europe, It will be more difficult for social media analytics companies to execute to their full potential using the data, versus Asian countries where privacy laws are less stringent. Privacy Compliant Social Data Analytics Without having to profile users using digital identifiers like IP or Mac addresses and cookies, there is still a lot which can be done. Companies are looking at GDPR compliant data processing on social media data, with proper consent and transparency for how personal data is collected and utilised for analytics. Here non-sensitive social media data is used for increasing sales or generating marketing insights with CRM integration, which is an appropriate use of social media ‘likes’ to achieve specific business goals. On the other hand, if sensitive personal data is mined to track users or survey their purchase habits, then that could be a violation at least in some global geographies. For companies, it is important to be careful about the nature of data collected for analytics and ensure it’s not personal in nature, as it can attract penalties. To counter this, proper data governance programs have been put in place for analysing social media trends and making sure that there is no violation. Even if there are fewer datasets available for social media analytics on personally identifiable data, social media analytics companies are expected to keep utilising non-personal data for sentiment analysis, sales trends, visualisation, and acquiring sales leads, all within the boundaries of regulations. For example, by monitoring social media, one can determine customer sentiment analysis on non-personal data, which can be converted into actionable insights. The Roadmap For Social Media Analytics The bottom line is that the collection and utilisation of social media data are complex, involving multiple sources and data management challenges. This is confusing for analytics professionals and social data analytics companies to identify the legality of collecting such kind of social media data. This means that companies will continue to use popular products such as Google Analytics to track social media campaign performance, conversions, and ultimately understand the return on investment from social media marketing efforts. Other large companies like Salesforce, IBM, SAS have products for social media data analytics. While social media analytics will continue to play a role in sales and marketing, other areas like risk management and fraud detection are also becoming more prominent. Here, law enforcement companies are leveraging social media analytics to extract and analyse the data generated from various data sources.
Over the last decade, there has been an explosion in social media data, and at the same time, AI/ML models are also getting better at predicting people’s interests and purchasing habits. For example, social media data can be processed and analysed using AI models to find meaningful correlations to precisely target products and services to […]
Over the last decade, there has been an explosion in social media data, and at the same time, AI/ML models are also getting better at predicting people’s
["Privacy", "privacy regulations", "social media", "social media analytics"]
["Go", "Privacy", "AWS", "AI", "sentiment analysis", "social media analytics", "ML", "privacy regulations", "social media", "Git", "RAG", "analytics", "R", "fraud detection"]
["AI", "ML", "analytics", "RAG", "fraud detection", "sentiment analysis", "AWS", "R", "Go", "Git"]
Vishal Chawla is a senior tech journalist at Analytics India Magazine and writes about AI, data analytics, cybersecurity, cloud computing, and blockchain. Vishal also hosts AIM's video podcast called Simulated Reality- featuring tech leaders, AI experts, and innovative startups of India.