Marchex, Inc.

Chris Kolbegger

Ziad Ismail

Jason S Flaks

Matthew Berk

Govindaraj Ramanathan

Bryan Rieger

Darren Spehr

Tim Graber

A method and system that performs cross-channel correlation of user identifiers with consumer telephone numbers. The system receives impression data characterizing the exposure of users to mechanisms for contacting businesses, the impression data including associated phone number and a date and time for each of the exposures. The system also receives call data describing telephone calls to businesses, the call data including a caller telephone number, a callee telephone number, and a date and time for each of the calls. Based on matching impression telephone numbers and call data callee numbers, the system forms correlations between corresponding user identifiers and caller telephone numbers. Correlations are also assigned a confidence level, based on the date and time of the corresponding exposures and telephone calls, that reflects the likelihood that a user associated with the correlated user identifier is the same as the user associated with the correlated caller telephone number.

Cross-channel correlation of consumer telephone numbers and user identifiers

A method and system that performs cross-channel advertisement targeting based on consumer telephone numbers. The system utilizes a targeting database to determine which consumers should be re-engaged on behalf of a business. The targeting database correlates a telephone number associated with a consumer with a masked user identifier associated with the tracking of online behavior of that consumer. The targeting database may include a confidence level representing the likelihood of correspondence between the telephone number and masked user identifier. The targeting database is used by the system to target advertisements to consumers. A telephone number of a consumer is used to identify a masked identifier of the consumer. Advertisements are then targeted to the consumer using the masked identifier. The stored confidence level may be used by the system to control the aggressiveness of re-targeting.

Correlated consumer telephone numbers and user identifiers for advertising retargeting

A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialog patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialog patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.

System and method for analyzing and classifying calls without transcription

A facility and method for analyzing and classifying calls without transcription via keyword spotting is disclosed. The facility uses a group of calls having known outcomes to generate one or more domain- or entity-specific grammars containing keywords and related information that are indicative of particular outcome. The facility monitors telephone calls by determining the domain or entity associated with the call, loading the appropriate grammar or grammars associated with the determined domain or entity, and tracking keywords contained in the loaded grammar or grammars that are spoken during the monitored call, along with additional information. The facility performs a statistical analysis on the tracked keywords and additional information to determine a classification for the monitored telephone call.

System and method for analyzing and classifying calls without transcription via keyword spotting

A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialogue patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialogue patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.

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Bainbridge Island, WA, United States of America

Chris Kolbegger