10 ways AI social listening tools help your brand

Staying relevant is crucial for a brand to succeed. It’s not just about being on top—it’s about keeping up with evolving market shifts. Brands need to keep tabs on audiences’ changing wants and needs, while creating memorable experiences through customer conversations and timely responses. According to The Sprout Social Index™, 51% of consumers think the most memorable thing a brand can do is be responsive and engage with customers.

Achieving all this simultaneously to catalyze your goals and objectives is no easy feat. To keep up, it takes AI-powered social listening giving you real-time insights on trends shaping market dynamics. This enables your team to proactively develop strategies for a competitive edge and pivot the course of your AI marketing campaigns on-the-go.

Read on to learn how AI and automation are transforming social listening. Plus, find strategic ways to use social listening for a stronger, more agile brand.

What is AI social listening?

AI social listening is the process of using AI technologies like machine learning (ML) and text analysis to monitor, analyze and understand social media conversations for actionable brand insights. This includes online mentions of a brand, product, industry or specific topics.

It enables you to keep track of customer opinions, new trends and brand sentiment by automating your social media analytics so you get all the insights you need at a moment’s notice. This means faster, data-driven decision-making and more efficient resource allocation for your teams.

How AI and automation are transforming social listening

Social listening started in the early 2000s. Social marketers would manually search customer forums, blogs and social media networks for brand mentions and discussions about their products, services or industry.

As social media networks grew and the number of users increased, basic tools like Google Alerts emerged to track mentions of certain words or phrases. But marketers still had to go through each comment manually, for context and sentiment. This was time-consuming and often inaccurate. It was especially difficult for brands with international or multilingual customers and those with a large customer base, whose sheer volume made manual data analysis impossible.

Social listening has become more sophisticated today, thanks to AI. It’s more advanced, accurate and robust, with capabilities like sentiment analysis, machine learning and natural language processing (NLP) changing how brands use social listening for business intelligence.

With AI-powered social listening, you can conduct market research by analyzing vast volumes of social data in a fraction of the time. You can scan several networks simultaneously to find relevant topics in real-time and get contextual insights to inform marketing and other departments like customer service or product development.

Take for example Sprout’s Social Listening solution, which uses AI to automatically analyze billions of data points in seconds. Our Queries by AI Assist tool helps focus your search to track and draw essential audience insights from feedback and social chatter quickly. You can also create Theme suggestions within your queries and apply them within your Listening Topics for more in-depth insights.

Plus,  Summarize by AI Assist generates summaries from any text that’s over 800 characters and pulls key information for your brand or campaign.

These AI-enabled enhancements increase your productivity while saving you time to focus on other critical tasks.

Using AI to monitor sentiment in social listening

Modern AI-powered tools like Sprout go beyond basic sentiment analysis to detect specific emotions and sentiments in social posts and comments. This gives brands real-time information on how consumers feel about their campaigns, products or services.

Sprout’s sentiment analysis model applies aspect-clustering to identify and extract relevant details from social listening data that can span millions of data points in real-time. It calculates the sentiment polarity in the ​​emotion expressed in this data using deep neural networks and LLMs based on Bidirectional Encoder Representations from Transformers (BERT) models.

This enables marketers to use social media sentiment analysis to spot emotions in social content, messages as well as in emojis to understand customers better. In turn, your teams can anticipate customer needs and optimize plans to improve customer satisfaction and brand loyalty.

Challenges of AI social listening

AI social listening is a game-changer, but it’s not an easy task. Social listening tools often face common challenges that may include:

Incorrect filtering

AI social listening tools need to gather and analyze millions of social conversations scattered across social networks. Refining this immense data can get challenging unless the tool has robust machine learning algorithms.

Inability to identify sentiment contextually

Social listening may not always correctly understand the sentiment of customer feedback. Unless the tool has been trained on phrases and idioms that mean sarcasm or irony, it can misinterpret messages.

Inaccurate insights

The complexity of human language and the way social messages are written often prove problematic for social listening tools. They’re often unable to understand double negatives or emojis. Plus, the tool may be limited due to data size and social network restrictions, resulting in the inability to give actionable results.

Overcoming AI social listening challenges

To overcome these problems, AI tools need strong NLP and ML algorithms, extensive data integration and powerful aggregation capabilities. What does this mean? Let’s dig in to understand.

Natural language processing

NLP algorithms combine several technologies such as sentiment analysis, named entity recognition (NER) and semantics to understand the context and nuances of social conversations, including slang and cultural references. This powers effective filtering, which enables a tool like Sprout to give accurate results through capabilities like our Query Builder.

Machine learning

Machine learning detects patterns and trends in big data to automatically identify recurring themes. That’s why it also helps in anomaly detection, such as unusual spikes or drops in activity, brand mentions or sentiment. This lets you monitor market trends, changing customer opinions and anticipate customer needs for proactive AI customer service. Plus, its neural networks (NNs) work to remember these patterns, making the tool smarter over time.

Data integration and aggregation

An AI social listening tool must be able to collect data from many sources to give a complete view of social conversations. This includes robust data cleaning and data normalization capabilities to ensure consistent and accurate data is used for analysis. This includes removing duplicate or irrelevant information. AI tools like Sprout are powered by vast data integrations, which enable us to provide granular, accurate social media listening.

10 ways AI social listening turns data into insights

Here are 10 ways you can use social listening to turn your social data into insights that’ll inform your competitive brand strategy.

1. Sentiment analysis

Use social listening to identify the sentiment behind social data—whether it’s positive, negative or neutral—based on specific topics, products and competitors. Also get an in-depth understanding of the reasons behind those sentiments. This helps you understand public perception and customer emotions toward your products, services and brand image.

Use these sentiment insights to influence your decision-making, such as identifying areas for improvement or measuring how well your marketing campaigns are performing.

2. Trend identification

AI social listening gives you insights on trending topics and emerging ones in social conversations within minutes compared to traditional research methods. This trendspotting helps marketers ​​inform campaigns as well as keep tabs on social network algorithms that are always in flux.

Use Sprout to get insights from social networks like Facebook or explore forum discussions with Reddit social listening to know what audiences are talking about in your industry. Apply these insights to build a proactive marketing strategy. Plus, explore potential co-branding collaborations to strengthen your market position and reach new audiences.

3. Finding conversation drivers

Use AI social listening to find people influencing social conversations and who are most impactful in your industry and target audience. Also, identify industry thought leaders to cultivate brand advocates and ambassadors.

Sprout’s influencer management tool, Tagger, further helps you listen in and analyze social conversations to determine influential individuals who resonate most with your target audiences and align with your brand’s values and objectives. Whether it’s Linkedin or YouTube, this strategic approach helps you find the right people who can effectively amplify your brand awareness efforts and drive meaningful engagement.

4. Competitor analysis

Compare brand mentions and sentiment against competitors to strengthen your market position and gain a competitive edge. AI social listening tools like Sprout give you comprehensive insights into competitor activity, hashtag usage, industry trends and market sentiment in a centralized dashboard for ease-of-use. ​​

Deep dive into competitor intelligence to establish competitive benchmarks to grow your social presence and strengthen your brand. Also use social listening to keep track of how your target audience is responding to other companies’ campaigns or new ideas. This competitive monitoring helps you future-proof and be ready to pivot strategies when the need arises.

5. Market research

Social listening provides a huge, fast and inexpensive way to do market research on the spot. This can help you uncover new revenue streams such as new audiences, product lines, services or customer loyalty programs.

Market research can also facilitate personalized customer engagement by analyzing individual user data and providing insights into audience preferences and behaviors. This helps you tailor consumer interactions and improve customer relationships.

6. Customer care

Use AI ​​social listening to provide proactive, quick and efficient customer care. For example, Sprout’s Listening tool can help you find customers who are having issues even if they aren’t directly talking to you or tagging you on social. It can also help track key customer care issues on a high-volume scale.

Our tool’s NER algorithms identify brand names, product names, topics and other keywords you’ve determined in social listening data, while sentiment mining segregates messages by priority. This dual mechanism helps you sort through millions of data points to refine the listening data further and understand what your customers need. Thus, helping you provide prompt, efficient care.

Watch this video to understand more.

7. Surprising and delighting customers

Social listening enables you to surprise and delight customers to build memorable moments that foster brand loyalty. Use social listening to dig into conversations and online mentions as they happen to capitalize on opportunities to get creative in enhancing customer delight. For example, planning surprise gifts or exclusive events, based on your customers’ ​​wants and expectations.

AI social listening tools help you gather insights on common topics and engage with important conversations across your social accounts from a centralized dashboard. Thus, enabling you to scan profiles and communities discussing topics on your radar or about your brand or competitors, without missing a beat. This thoughtful approach to a customer-centric marketing strategy will enrich your brand advocacy and gain you customers for life.

8. Analytics and reporting

Social Listening analytics gives you a visual representation of your data through dashboards, graphs and charts that make your insights digestible and actionable.

AI tools like Sprout use listening data to produce detailed reports on brand mentions, brand sentiments, engagement rates and audience demographics. These reports help you and your team make smart decisions based on data. They also enable you to share your social ROI with leadership and other stakeholders to influence executive buy-in for your future projects.

9. Crisis management

Social listening insights help with crisis management by providing early detection of negative trends, spikes in negative sentiment and unusual activity related to your brand. Be ready for a crisis with special alerts and detailed sentiment analysis to observe changing conversations to maintain brand health.

For instance, Sprout’s Listening tool continuously monitors online conversations and alerts you asap so you’re able to quickly identify potential crises before they escalate. This enables you to respond swiftly and put out the fire with effective crisis communication. This proactive approach helps you maintain brand reputation and customer trust during critical situations.

10. Campaign analysis

Use social listening to capture audience reactions to marketing campaigns and get the right metrics to measure success. Social listening insights help you understand what people think about your campaign, content and ads while they’re running, so you can optimize them quickly.

This data also provides valuable feedback on what aspects of the campaign resonate with the audience and which areas need improvement. These insights can help measure how well your marketing efforts work and in shaping your future campaigns.

Build a stronger brand with AI social listening insights

AI social listening has changed how brands understand and talk to their customers. This has led to better strategies, customer experiences and brand connections. The future of social listening will only see more automation, where AI will play an even stronger role in content creation, intuitive workflows and driving strategic adjustments to brand initiatives.

Inform your strategies with AI to build a stronger, more successful brand. Begin by exploring social listening tools that meet your business needs.