There’s over-dependence on unreliable methods like buzz and sentiment analysis in an effort to understand consumer emotions and behavior.
Existing solutions lack the automation, coverage, and integration needed to make accurate decisions quickly.
Traditional methodologies are myopic and exist in silos. They require longer turnaround time from data to actionable insights, thereby losing business relevance.
Platform-agnostic coverage across data formats – structured and unstructured, text and audio-visual – utilizing a rich ontology framework for social media monitoring
Identification of underlying tone, intention, and sentiment using proprietary Natural Language Processing (NLP) and advanced machine learning algorithms with automation and AI
Multi-language data coverage with exhaustive global languages support, including interpretation of dialectical nuances and transliterated phrases
A retail giant used digital conversations to map end-to-end consumer journey touchpoints to act on purchase triggers and blockers
A top-5 global CPG company improved their brand tracking and Share of Voice by integrating social insights
A top-3 food & beverage manufacturer identified sub-demand spaces and micro segments for better targeting using social media analysis
Identify and strategize on white spaces and unmet needs of consumers by leveraging insights from Social Media data sources