Abstract: User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions.

Abstract: User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions.

Abstract: User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions.

Abstract: The propensity and intent of a user to make a purchase is predicted based on product search queries and chat streams. The contents of the data sources, including search queries and chat streams, are analyzed for product names and product attributes. The results of the analyses are used to predict user needs. Product names and attributes are extracted from the data sources. The extracted information is mapped onto abstract product categories. Based on the abstract product categories, offers for products and services are made to the user.

Abstract: The customer experience is enhanced by detecting leakage-to-voice from chats and providing recommendations to operations, chat agents, and customers. A chat is classified into leakage-to-voice or leakage-to-text chat and actionable recommendations are then provided to operations, chat agents, and customers based on the leakage information. Once leakage is identified, various other insights are extracted from chats and such insights are fed into the knowledge-base. Such insights also used in agent training and are provided to chat agents as recommendations. This results in a better customer experience.

Abstract: The customer experience is enhanced by detecting leakage-to-voice from chats and providing recommendations to operations, chat agents, and customers. A chat is classified into leakage-to-voice or leakage-to-text chat and actionable recommendations are then provided to operations, chat agents, and customers based on the leakage information. Once leakage is identified, various other insights are extracted from chats and such insights are fed into the knowledge-base. Such insights also used in agent training and are provided to chat agents as recommendations. This results in a better customer experience.

Abstract: The customer experience is enhanced by detecting leakage-to-voice from chats and providing recommendations to operations, chat agents, and customers. A chat is classified into leakage-to-voice or leakage-to-text chat and actionable recommendations are then provided to operations, chat agents, and customers based on the leakage information. Once leakage is identified, various other insights are extracted from chats and such insights are fed into the knowledge-base. Such insights also used in agent training and are provided to chat agents as recommendations. This results in a better customer experience.

Abstract: User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions.

Abstract: The customer experience is enhanced by detecting leakage-to-voice from chats and providing recommendations to operations, chat agents, and customers. A chat is classified into leakage-to-voice or leakage-to-text chat and actionable recommendations are then provided to operations, chat agents, and customers based on the leakage information. Once leakage is identified, various other insights are extracted from chats and such insights are fed into the knowledge-base. Such insights also used in agent training and are provided to chat agents as recommendations. This results in a better customer experience.

Abstract: The propensity and intent of a user to make a purchase is predicted based on product search queries and chat streams. The contents of the data sources, including search queries and chat streams, are analyzed for product names and product attributes. The results of the analyses are used to predict user needs. Product names and attributes are extracted from the data sources. The extracted information is mapped onto abstract product categories. Based on the abstract product categories, offers for products and services are made to the user.