Five Ways Artificial Intelligence Supercharge Your Social Insights

Advances in machine learning and data science techniques have made social data more valuable and actionable for marketers and insights pros.

The author(s)

  • Emma Huff Synthesio, Social Intelligence and Analytics, USA
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Advances in machine learning and data science have unlocked the possibilities of social intelligence and consumer-generated data. At the beginning, we needed linguistic experts to build cumbersome natural language processing (NLP) models to understand consumer conversations. Not only were the models hard to scale, they were slow to run and not trained on large datasets, so insights were at best spotty, at worst completely inaccurate. But with a new generation of data scientists, we can now build and scale machine learning models capable of understanding human conversation, uncovering patterns and trends, and “reading” more than just text.

How AI can help you convert data into actionable insights

In a new blog article, Synthesio’s Emma Huff discusses how AI enables insights, innovation, and brand health pros to make consumer-centric decisions about product launches, go-to-market strategy, crisis response, and more. With troves of consumer data at their fingertips, it’s impossible to extract actionable insights about consumer preferences and behaviors without the help of AI. Now, cutting-edge data science capabilities can:

  1. Clean data and reduce noise so your insights are accurate
    Low quality data = low quality insights. Online consumer data is filled with noise: from traditional spam to bots on social sites, advertisements, and games, dirty data not only wastes resources, it muddles your view of what customers are actually saying and thinking. Synthesio’s Noise Reducer automatically removes irrelevant mentions from your data set – and filters out things like fraud, job postings, and vulgar content – so you can zero in on genuine consumer expressions.
  2. Determine sentiment and emotion to tell you how customers feel
    Advanced sentiment analysis capabilities use natural language processing (NLP) techniques to understand the feeling, tone, and emotion of online conversations. This means understanding how consumers feel – at scale – about your brand, recent product launch, PR crisis, or competitors. Plus, advanced algorithms like Google BERT provide the foundation for building AI models that are optimized to understand human language in social data – which means brands can trust their insights and use them to make data-driven decisions about campaigns and content, crisis communications, or how to improve CX
  3. Detect themes, patterns, and shifts hidden in big datasets
    As brands outgrow their social listening tools and move to AI-enabled consumer intelligence, the ability to spot and even predict trends becomes more important. Bottom-up machine learning techniques like Topic Modeling not only remove human bias, they speed time to insights by spotting themes in online conversations you may not know to look for. AI-powered tools like Signals use data science algorithms to surface insights like viral media, spikes in engagement or sentiment (plus the semantic drivers behind spikes), and correlations between topics or themes, making it easier for marketers and insights pros to quickly understand why, where, and how shifts are happening.
    Conversation clusters by Synthesio
    Synthesio Topic Modeling identifies clusters of conversations, so you can dig deeper into what consumers are saying about a brand, product, or topic.
  4. Perform semantic analysis to understand what customers mean
    Advanced semantic capabilities have enabled brands to go beyond keyword tracking to identify grammatical elements like verbs and phrases, and entities like products or people. Named Entity Recognition (NER) can scan unstructured text to extract and categorize people, organizations, products, countries, events, and even identify groups like “Americans,” “Democrats,” or “French.” Parts of Speech (PoS) recognition pulls out grammatical elements like top adjectives, verbs, keywords, or noun phrases. But NER and PoS are not just for linguistics lovers; they can surface products, brands, or influencers that are impacting your market; or detect top phrases associated with positive or negative sentiment like “slow service” or “lack of diversity.”
  5. Analyze image and video content to get a complete view of consumer-generated content
    Most online conversations are not solely text-based; social media content today contains images, gifs, videos, and emojis. Often, traditional social listening tools that track keywords fail to capture relevant content because brand names are never explicitly mentioned; up to 98% of videos that include brand logos don’t mention the brand at all, and 80% of all brand-related images don’t reference brands in accompanying text. Data science-powered capabilities like logo detection, scene recognition, and image analysis help brands understand their visual online presence – and provide critical context about how and when consumers are using their product, what activities are associated with their brand (think Corona beer on the beach), and even help identify counterfeit products and fake logos.

To make social insights actionable and scalable for brands, we need the help of AI (and human teams – more on that here). To learn more about how AI-enabled consumer intelligence can help you get closer to consumers – and take the manual labor and guesswork out of your insights initiatives – request a demo with our team.

The author(s)

  • Emma Huff Synthesio, Social Intelligence and Analytics, USA

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