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Social media is the single most powerful place on the World Wide Web for marketers to tap into potential customers. Brand building, user attraction, and customer retention can all be enhanced through social networks. And big data plays a key role in helping brands achieve more by leveraging available insights.
Big data is expected to become a $473.6 billion market by 2030, with a compound annual growth rate (CAGR) of 12.7%, indicating that the widespread application of this technology is likely to have a major impact on the marketing world not just in Europe, but virtually everywhere.
But just because big data can improve social media marketing performance doesn't mean brands can automatically leverage it. The data is highly unstructured and can be difficult for humans to interpret. However, when accessed in the right way, the quality of the insights can be invaluable for running successful targeted campaigns and generating more leads.
With this in mind, let’s look at four key ways that big data analytics is revolutionizing social media marketing.
1. Strengthen social listening.
Social listening is one of the most effective ways for marketers to understand how consumers feel about their brand and understand where to improve their strategy.
Leveraging a strong customer relationship management (CRM) solution can help you take advantage of social listening tools that leverage AI to interpret consumer sentiment across millions of brand mentions and cross-channel posts showcasing a specific brand or product.
Setting up your CRM to monitor mentions of specific brands, products, or services can help you track what people are saying about both your company and your competitors. This vast amount of unstructured data can be analyzed by artificial intelligence to learn exactly which products consumers are most interested in and how they are likely to engage with your brand through new product launches and news.
2. Employ micro-targeting.
Artificial intelligence plays an integral role in optimizing social media marketing, and the utility of machine learning algorithms allows marketers to target customers across different platforms based on metadata related to their age, gender, nationality, education, profession, and interests.
This is an invaluable tool in audience segmentation, a strategy known as microtargeting, helping you advertise on a more personalized level so that you appeal directly to your most valuable leads and are more likely to resonate with them.
The beauty of microtargeting is that it paves the way for marketers to gain insights that help them effectively reach consumers who are likely to be interested in their products and services, without incurring high advertising costs on large, untargeted campaigns.
This allows marketers to connect more directly with their audiences based on ML insights and generate more revenue by targeting ads to users who are actively interested in their products. For this to work, thorough audience research is essential.
3. Hyper-personalization.
These improved audience segmentation capabilities allow marketers enhanced personalization to reach their target audience in the most effective way.
76% of customers claim that personalized messages are essential to increasing interest in a brand, and 78% say that this form of communication helps them become repeat 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.
AI can analyze data at scale, which allows it to leverage personalized advertising and curate recommendations. The booming era of generative AI also helps leverage campaigns that can instantly create ad content and adapt the language and CTAs used to specific leads and their online behavior on an individual level.
4. Next-generation influencer marketing.
Big data analytics can also boost influencer marketing campaigns and their success rates. Through analytical insights, brands can more comprehensively evaluate influencers’ past performance and make choices based on the type of content, audience specifications, level of engagement, and the influencer’s personality online.
This allows for more targeted campaigns that can pinpoint powerful micro-influencers who have smaller followings but can be engaged more effectively.
The insights can also help you detect fake followers, identify high numbers of fake or inactive followers, and ensure you implement the best strategy to work with the right influencers.
Big data analytics also leverages brand-to-brand AP automation to help marketers manage payments based on influencer performance. Innovations in AP automation give marketers real-time spend visibility and access to key influencer campaign metrics to stay budget-friendly throughout the partnership.
It's time to predict behavior.
The combination of big data and social media is enabling marketers to analyze and even predict consumer behavior, meaning they can understand exactly what their customers want and need, and offer products and services as the perfect solution to their perceived pain points.
As the big data industry continues to grow, brands will be able to make more strategic decisions when engaging with consumers on a personal level.
This gives your brand an edge in a competitive industry and allows you to build valuable, more sustainable relationships with your target audience that will continue to bring benefits well into the future.
