# US 2014/0195298 A1 · Tracking of near conversions in user engagements > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0195298 A1 - Document ID: US20140195298A1 - Status: Published application - Kind: A1 - Application number: 14/142,698 - Filed: 2013-12-27 - Published: 2014-07-10 - Earliest claimed: 2013-01-10 - Assignees: 24/7 Customer, Inc. - Applicants: 24/7 Customer, Inc. - Inventors: R. Mathangi SRI; Ravi VIJAYARAGHAVAN; Vaibhav SRIVASTAVA; Prashant V. ULLEGADDI - Web page: https://patentgrep.com/patent/US20140195298A1 ## CPC - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract A computing method and system is disclosed for analyzing interactions between a user and a customer support agent. Typical interactions include inquiries about a product or service, and a service call. When the user purchases a good or service, or successfully completes a service call, the customer converts, e.g. the sales pitch or service solution was successful. If the customer does not convert, then the interaction between user and agent is analyzed to determine why the user did not convert and whether the user should be categorized for potential retargeting. ## Claims ### Claim 1 A computer implemented method for user analysis, comprising: connecting a user and an agent to an interaction engine; tracking interactions between the user and the agent with the interaction engine; categorizing the interaction between the user and the agent into categories; flagging, based on the categories, the user for retargeting; and retargeting the user based on the flagging of the user. ### Claim 2 (depends on 1) The method of claim 1, wherein said retargeting attempts to convert a user who is potentially interested in a good or service into a user who actually purchases the good or service. ### Claim 3 (depends on 1) The method of claim 1, wherein said interactions comprise inquiries about a product or service, and a service call. ### Claim 4 (depends on 1) The method of claim 1, further comprising: analyzing interactions between said user and said agent to determine why the user did not convert, and whether the user should be categorized as a near convert for potential retargeting. ### Claim 5 (depends on 1) The method of claim 1, further comprising: analyzing interactions between said user and said agent to determine, based on a threshold, whether the user is a near-convert. ### Claim 6 (depends on 1) The method of claim 1, further comprising: analyzing said interactions based on transcripts between said user and said agent. ### Claim 7 (depends on 1) The method of claim 1, further comprising: selecting categories for retargeting users who are near-converts via any of a plurality of channels. ### Claim 8 A method for tracking near conversions in user engagements, comprising: providing an interaction engine by which a user and an agent interact with each other, said interaction engine using any of a plurality of available channels as a mode of interaction; wherein said interaction engine is configured for categorizing said user into a converted user or a non-converted user; wherein a converted user is a user with whom the agent has been able to complete a transaction; and wherein a non-converted user is a user with whom the agent was unable to complete the transaction. ### Claim 9 (depends on 8) The method of claim 8, wherein said analysis engine is configured for accessing transcripts of interactions between said agent and said non-converted user in a textual format; and wherein said analysis engines is configured for analyzing said transcripts to identify near converted users. ### Claim 10 (depends on 9) The method of claim 9, further comprising: providing a mechanism for transcribing a voice based interaction between said agent and said non-converted user into textual format. ### Claim 11 (depends on 8) The method of claim 8, wherein said analysis engine is configured for analyzing journeys of a sample set of users, wherein said sample set of users comprises converted users, non-converted users, and users who have been designated as near converted users; and based on said analysis, said analysis engine builds a model that creates a correlation between the journeys and the type of user. ### Claim 12 (depends on 11) The method of claim 11, wherein when said analysis engine detects user interaction, said analysis engine maps a type of user to one of a potential converted user, a potential non-converted user, or a potential near converted user by mapping a current journey of the user to said model; and wherein said analysis engine then modifies a user experience to promote said user becoming a converted user. ### Claim 13 (depends on 8) The method of claim 8, said analysis engine further comprising a text mining module that is configured for performing topic modeling to represent user and agent interaction transcripts in terms of a set of N topics, wherein a topic is a distribution over a vocabulary that comprises all words in the transcripts; and wherein said text mining module analyzes said transcripts of interactions over a period of time to obtain a topic model comprising said N topics. ### Claim 14 (depends on 13) The method of claim 13, wherein said analysis engine further comprises a control module that builds a model in which sales conversion is a response variable, and in which topics received from said text mining module are independent variables, wherein said control module then determines weights for each of the topics. ### Claim 15 (depends on 14) The method of claim 14, wherein on receiving a transcript, said text mining module analyzes said transcript and identifies the topics that are present in the transcript; wherein said text mining module identifies purchase and/or sale information that corresponds to the transcript by examining the disposition of the agent associated with the transcript, wherein the disposition of the agent comprises sale information in the transcript that indicates whether or not a sale was made. ### Claim 16 (depends on 15) The method of claim 15, wherein based on the identified topics present in the transcript, the control module scores the transcript; wherein based on the score, the control module classifies the transcripts as near convert users; wherein transcripts having a high score are those related to a near converted user; and whereas transcripts having a low score are classified as these related to users with a low chance of being converted. ### Claim 17 A method for identifying a user as a near-converted user, comprising: an analysis engine receiving one or more transcripts of user and agent interactions from an interaction engine; said analysis engine checking if a user corresponding to the transcript has been converted by checking information present in the disposition of an agent responsible for the user interaction; wherein if the user has not been converted, the analysis engine analyzes the transcripts and identifies topics present in the transcript; wherein based on the topics present in the transcript, the analysis engine scores the transcript; wherein the analysis engine checks if the score is above a predetermined threshold; wherein if the score is above a threshold, the analysis engine classifies the user corresponding to the transcript as a near converted user. ### Claim 18 (depends on 17) The method of claim 17, wherein users who have had no interaction with an agent, but who have undertaken a journey related to a product or a service by navigating across a Web site, are classified as near converted users, based on their journey.