4 Ways Big Data Analytics is Revolutionizing Social Media Marketing Social media remains one of the best ways of capturing business leads, but how can big data analytics revolutionise social media marketing?

Social media remains the single most powerful location on the World Wide Web for marketers to unlock the potential of their leads. Building brands, attracting users, and retaining customers can all be amplified through social networks, and big data plays a crucial role in empowering brands to do more with the insights available to them.

Big data is expected to grow into a $473.6 billion market by 2030, representing a CAGR of 12.7%. This underlines the far-reaching application of the technology which is likely to make a significant impact in the world of marketing—here in Europe and virtually everywhere.

But just because big data can improve the performance of social media marketing, it doesn't mean that brands will automatically be capable of utilizing it. Data can be vastly unstructured and difficult for humans to interpret. However, when accessed in the right way, the quality of insights can be invaluable for driving successful targeted campaigns and generating more leads.

With this in mind, let's explore four essential ways in which big data analytics is revolutionizing social media marketing:

1. Enhanced social listening.

Social listening remains one of the most effective ways for marketers to understand how consumers feel about their brand and where to make improvements to their strategy.

By utilizing a strong customer relationship management (CRM) solution, it's you can utilize social listening tools in a way that can tap into AI to interpret consumer sentiment across vast swathes of brand mentions and cross-channel posts featuring a specific brand or product.

Setting your CRM to monitor for specific brand, product, or service mentions can help you to track what people are saying about both your business and your rivals too. This high volume of unstructured data can then be analyzed through artificial intelligence to learn exactly what products your consumers are more interested in and how they're likely to engage with your brand over new launches or news.

2. Embracing microtargeting.

Artificial intelligence plays an imperative role in optimizing social media marketing, and its utility of machine learning algorithms can aid marketers in targeting customers based on metadata surrounding their age, gender, nationality, education, profession, and interests across different platforms.

This is an invaluable tool in audience segmentation and can help you to advertise on a more personalized level in a way that ensures that you appeal directly to your most valuable leads in a way that's more likely to resonate with them in a strategy that's known as microtargeting.

The beauty of microtargeting is that it paves the way for insights that can help marketers reach consumers who are more likely to engage in their products or services in a more impactful way without running up higher advertising costs in larger, untargeted campaigns.

This helps marketers to connect more directly with their audience based on ML insights and generate more profits provided that the advertisements target users who are actively interested in products. For this to work, sufficient audience research is essential.

3. Hyper-personalization.

These improved audience segmentation capabilities pave the way for enhanced personalization for marketers to reach their target audience in the most impactful way.

As much as 76% of customers claim that personalized messages are essential in enhancing their consideration of a brand, with 78% stating that this form of communication can help them become recurring customers.

With the help of both big data and artificial intelligence, brands can communicate on a hyper-personalized level and display ads that their target audience can directly relate to.

Because AI can analyze data at scale, it's capable of leveraging personalized ads in a way that curates recommendations. The burgeoning age of generative AI is even helping to leverage campaigns where ad content can be produced on the fly and where the language and CTAs used can adapt on a personal level to the specific lead and their online behavior.

4. Next-generation influencer marketing.

Big data analytics can also bolster influencer marketing campaigns and their success rates. Through analytical insights, brands can look at an influencer's historical performance in a more holistic manner, and make selections based on the type of content, audience specifications, level of engagement, and personality of the influencer online.

This can open the door for more targeted campaigns that can accurately identify strong micro-influencers who have fewer followers but engage with them more effectively.

Analytical insights can also be useful for fake follower detection, and identifying higher volumes of fake or inactive followers to ensure the best possible strategy for collaborating with the right influencers.

Big data analytics can also help to leverage AP automation among brands and help marketers manage payouts to influencers based on their performance. Innovations in AP automation mean that marketers can gain access to real-time spend visibility, alongside essential influencer campaign metrics to ensure that partnerships remain budget-friendly throughout.

The age of anticipating behavior.

Uniting big data and social media paves the way for an age where marketers are capable of analyzing and predicting consumer behavior. This means that it's possible to pinpoint wants and needs and offer your products and services as the perfect solution to perceived pain points.

As the big data industry continues to grow, brands will have the capacity to make more strategic decisions in engaging with consumers on a personal level.

This can help brands to gain an edge in competitive industries and create valuable, more sustainable connections with their target audience that can continue to pay dividends long into the future.