Audio Analytics
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Audio Analytics
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Feeling Pulse: Text Reveals Emotion
Decoding opinions, understanding trends, and driving better decisions with sentiment analysis.
Goal - To identify high-level topics in the dataset, providing an understanding of subscriber feedback and their performance over time.
- To employ sentiment analysis on the verbatim responses associated with each identified topic to categorise sentiments as positive, neutral, or negative.
- To determine sentiment variations across different areas.
- To identify high-level topics in the dataset, providing an understanding of subscriber feedback and their performance over time.
- To employ sentiment analysis on the verbatim responses associated with each identified topic to categorise sentiments as positive, neutral, or negative.
- To determine sentiment variations across different areas.
Technique - Text Preprocessing, Topic Modelling, Sentiment Analysis, Time Series Visualization, Visualization.
- Text Preprocessing, Topic Modelling, Sentiment Analysis, Time Series Visualization, Visualization.
Impact - Enable strategic decision-making by providing a clear understanding of the key topics in subscriber feedback.
- Pinpoint specific areas to guide targeted efforts and enhance customer satisfaction.
- Provide insights into subscriber opinions through verbatim sentiment analysis.
- Enable strategic decision-making by providing a clear understanding of the key topics in subscriber feedback.
- Pinpoint specific areas to guide targeted efforts and enhance customer satisfaction.
- Provide insights into subscriber opinions through verbatim sentiment analysis.
Listening In: Insights with Audio Analytics
Beyond speech, audio reveals emotions, behaviors, and trends, driving better decisions.
Goal - To identify anomalies in customer-operator interactions.
- To identify factors affecting customer satisfaction .
- Behavioral analysis of Customer and Operator.
- To identify anomalies in customer-operator interactions.
- To identify factors affecting customer satisfaction .
- Behavioral analysis of Customer and Operator.
Technique - Audio processing, Audio Diarization, Speech to Text, Text translation, Sentiment Analysis, Topic Modelling.
- Audio processing, Audio Diarization, Speech to Text, Text translation, Sentiment Analysis, Topic Modelling.
Impact - Improvement in CSAT and reduced customer complains.
- Identify most frequently inquired new requirements by the customer.
- Early warning for customer issues and proactive corrective actions.
- Better operator performance
- Improvement in CSAT and reduced customer complains.
- Identify most frequently inquired new requirements by the customer.
- Early warning for customer issues and proactive corrective actions.
- Better operator performance
See-Through Repairs: AI Detects Damage
Pinpoint defects faster, save costs, and optimize production with intelligent damage detection.
Goal - To identify and classify damages such as scratches, dents or cracks in vehicles.
- To allow early identification of damages and timely maintenance interventions.
- To enable automated and accurate inspections for insurance claims.
- To identify and classify damages such as scratches, dents or cracks in vehicles.
- To allow early identification of damages and timely maintenance interventions.
- To enable automated and accurate inspections for insurance claims.
Technique - Image augmentation, Image processing, Image classification, Object detection, Visualization.
- Image augmentation, Image processing, Image classification, Object detection, Visualization.
Impact - Accelerated claims processing, reducing delays and administrative burden.
- Reduced post-purchase repair costs, to enhance ownership experience.
- Enhanced brand image, to attract and retain customers.
- Boosted customer satisfaction and loyalty.
- Accelerated claims processing, reducing delays and administrative burden.
- Reduced post-purchase repair costs, to enhance ownership experience.
- Enhanced brand image, to attract and retain customers.
- Boosted customer satisfaction and loyalty.
Watching Wisely: AI Detects Suspicious Activity
Early detection, faster response, enhanced safety with intelligent activity monitoring.
Goal - To detect any type of suspicious activity performed by a human.
- To classify activity into its particular class for effecting an immediate necessary action.
- To monitor high-level patterns of activity and identify early indicators.
- To detect any type of suspicious activity performed by a human.
- To classify activity into its particular class for effecting an immediate necessary action.
- To monitor high-level patterns of activity and identify early indicators.
Technique - Exploratory Data Analysis, Image pre-processing and augmentation, Object detection and Image classification algorithms, Visualization.
- Exploratory Data Analysis, Image pre-processing and augmentation, Object detection and Image classification algorithms, Visualization.
Impact - Quicker and better suspicious activity detection.
- Creation of a safer environment for people.
- Attention based system for a more aggressive detection.
- Lesser Human Intervention.
- Quicker and better suspicious activity detection.
- Creation of a safer environment for people.
- Attention based system for a more aggressive detection.
- Lesser Human Intervention.
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