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 jordan business email list 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.
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.