by Transformation of Consumer Research through Video Intelligence
Insights from Convosight
The COVID era revolutionized social media consumption, with users spending 6+ hours daily online, 70% on video content. With 200Bn Instagram reels and 70Bn YouTube shorts watched daily, and 95+ minutes spent on TikTok, social media has become the primary hub for entertainment and influence.
Today’s consumers access vast information from authentic sources, are vocal about preferences, and have shorter attention spans. Consumer companies struggle to extract actionable insights from unstructured video content across platforms like Instagram, YouTube, TikTok, and online communities – crucial sources for understanding consumer preferences and market trends.
Key challenges include: fragmented consumer conversations across platforms, traditional research methods’ slowness, difficulty integrating multiple data sources, and complexity in understanding multi-market consumer needs.
With influencer marketing projected to reach $30Bn globally, decoding video content is vital for tracking consumer trends. Companies must evolve beyond traditional research to understand consumer behavior through social trends and video content analysis.
Next-Gen AI + Social Data Access: convotrack.ai
Robust AI-Tech + Large Data
- Data Lake Architecture: A robust infrastructure that sits on over 200MM+ Video Content + 1Bn+ Conversations and organizes multi-modal content (video, audio, text) from platforms like Instagram, YT, TikTok, Reddit, FB Groups etc. This supports real-time data processing and retrieval.
- Generative AI Engine: The proprietary AI system employs a combination of a Mixture of Experts (MoE) and a Mixture of Judges (MoJ) models for domain-specific processing and validation, ensuring high-quality insights.
- Reinforcement Learning with Human Feedback (RLHF): Continuously refine AI models based on human inputs, improving trend detection accuracy.
- Advanced Vector Embedding and Search: Proprietary vector similarity search for semantic understanding and NLP-based querying, allowing users to efficiently analyze large datasets.
- Scalable Architecture: The system supports horizontal scaling and parallel processing to handle high-volume data and deliver real-time insights across multiple platforms.
Research Use Cases: From Trends to Action
- Personal Care: Informed AOP strategy for Indian brands through content analysis
- Baby Care: Identified new product opportunities across APAC & US markets
- FMCG & E-commerce: Mapped competitive landscape through influencer content on Instagram/YouTube Brand Launches: Guided influencer discovery and selection for major FMCG campaigns
- Category Growth: Uncovered challenges and opportunities for FMCG and Pharma brands
- Crisis Management: Tracked de-influencer sentiment and viral content impact for FMCG brands
The Future of Consumer Research
As social media continues to evolve, and AI capabilities expand the opportunity—and imperative—to shift from traditional research methods to real-time, AI-powered insights grow stronger. This transformation isn’t just about keeping pace with changing consumer behavior; it’s about unlocking deeper, more authentic consumer understanding that can drive more effective marketing strategies and product innovations.
For CMI leaders and CMOs, the message is clear: The future of consumer research lies in harnessing the power of AI to decode the vast landscape of social video content, delivering faster, deeper, and more actionable insights than ever before.
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