Role of predictive analytics in social media to make more informed business decisions

Today is World Social Media Day 2020. Social Media has transformed how businesses sell or communicate with customers. For every business that has a social media presence, there is a treasure trove of audience data. Predictive Analytics can enable decision-makers to make informed business decisions based on that data. The ‘instantaneous’ nature of Social Media helps marketers in assessing real-time audience behavior and feedback.

With 3.8 billion users of social media worldwide, we can infer that about half of the global world population is on social networking platforms. About 80% of the population in the US is also on social media. This global audience is connecting, interacting, and making purchase decisions on social media every minute, even when you are reading this blog. Real-time information in the form of status updates, stories, tweets, hashtags, images, and videos are flooding the internet. Based on the industry and target market, a predictive analytics model for social media can be built to mine through and analyze the massive flow of unstructured data from social networks.

The output: Actionable Business Insights

Measuring KPIs

Measuring Content Performance

Apart from tracking the chatter on social media, predictive analytics can also analyze the performance of the content on each social media platform. Feeding native analytics data from tools, such as Facebook Insights, Twitter Analytics, Instagram Business Insights, and Google Analytics, into a predictive analytics model can identify content type is more likely to engage and influence your audience’s purchase decisions.

Feedback-based Product Enhancement

At this point, we would like to highlight that social media marketing is always customer-centric. Customer feedback of your business’ products or services on social media in the form of hits, comments, and reviews is readily available; but is highly unstructured. Through Machine Learning, Artificial Intelligence, and Predictive Analytics modeling, making sense of all that data has become possible. Many times, businesses have taken insights from this data and have made changes to their existing products or have launched a new product with specifications that originated from feedbacks. In this way, businesses can let their customer community guide them into creating products that will sell better.

Unlike other verticals, Predictive Analytics in Social Media is highly cost-effective as there are no requirements for any state of the art technology pieces to support the inflow of data. Native analytics tools of any social media platform can provide almost all of the relevant data required for the process.

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