Our Sentiment Analytics tool empowers outbound call centres with deeper insights into customer intent from voice calls.
DoxaMeter, the sophisticated sentiment analytics engine, empowers customer-facing organizations to improve customer experience, boost conversions, and effectively manage customer perceptions by extracting actionable insights from call center conversations.
Gain intelligent emotional insights with over 90% accuracy.
Data-backed insights into customer sentiment and intent from customer interactions.
Analytics Engine leverages LLM, NLP and BERT for sentiment extraction and analytics.
A conversational analytics tool belonging to the Communication Platfom-as-a-Service suite.
The tool analyzes customer conversations to determine customer opinions on various dimensions.
A deeper understanding of customer intent leads to more satisfying interactions and potential sales growth.
Identify operational improvements and ensure consistent, effective delivery of key product benefits.
Equip agents with call flow guidance, key customer info to be solicited, and performance improvement tools.
Analyze customer conversations to reveal hidden trends and measure customer sentiment towards your brand.
Our software uses Aspect Matching and Opinion Mining for sentiment extraction. Customer Intent is classified using Bi-directional Encoder Representations from Transformers. The tool identifies and categorizes customer intent with more than 90% accuracy rate. It benchmarks agent performance, ensures call quality and compliance, and drives sales growth through enhanced conversations.
Our analytics system ingests audio WAV files and uses Azure for voice-to-text conversion, including language identification, transcription, and formatting optimized for sentiment analysis.
Following the planned process flow and KPIs, information is extracted and categorized into aspects using lexicon-based parsing and classifiers.
The RoBERTa model classifies aspects within configured dimensions, while an intent mining algorithm categorizes customer interactions.
Calculated transcript-level values are aggregated to create overall dimension metrics, which are then integrated into Elasticsearch for efficient indexing and retrieval.