Abstract
Keywords: conversational agent, brand-specific agent, lead qualification, search results integration, user intent, advertiser data aggregation, personalized user experience, dynamic call to action, AI agent training, lead prioritization
Abstract
Generating qualified sales leads for complex user journeys can be inefficient, often resulting in user drop-off and providing advertisers with little insight into user intent. This technique addresses these issues by integrating a brand-specific conversational agent directly into a content provider’s owned and operated surfaces, starting with the search results page. Trained on comprehensive advertiser data such as websites, business profiles, and user reviews, the agent provides detailed, real-time information, allowing users to interact with a business without leaving the platform. The system qualifies leads by analyzing conversational depth and historical user activity to identify and rank high-intent prospects. This streamlined process provides businesses with more relevant, pre-qualified leads while reducing effort for consumers.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Pattabhiraman, Balaji; Garg, Vijay; Isoka, Derrick; Yadav, Deepak; Ba, Paneendra; Batten, Teresa; Chen, Teresa; Trehan, Lohitaksh; Huang, Shen; Bhutada, Shruti; Jensen, Rina; Sahu, Sudheer; Amir-Abbassi, John; and Gupta, Ujjwal, "Brand-Specific Conversational Agent with Lead Qualification", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10173