Source: http://www.google.ca/patents/US8737595
Timestamp: 2017-09-23 16:28:29
Document Index: 769784124

Matched Legal Cases: ['Application No. 200980153730', 'Application No. 2010', 'Application No. 20098011060', 'Application No. 2008349500', 'Application No. 2009209317', 'Application No. 200980142771', 'Application No. 2010', 'Application No. 20088012833', 'Application No. 2011', 'Application No. 2008801283369']

Patent US8737595 - Systems and methods for routing callers to an agent in a contact center - Google Patents
Methods are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. One or more agents are graded on achieving an optimal interaction, such as increasing revenue, decreasing cost, or increasing customer satisfaction. Callers are then preferentially routed...http://www.google.ca/patents/US8737595?utm_source=gb-gplus-sharePatent US8737595 - Systems and methods for routing callers to an agent in a contact center
Publication number US8737595 B2
Application number US 13/854,825
Also published as CA2713526A1, CN101986801A, EP2235925A1, US8359219, US8433597, US8712821, US8731178, US9288325, US9288326, US9413894, US9426296, US9680997, US9712679, US20090190740, US20090190743, US20090190746, US20090190748, US20130101109, US20130216036, US20150237208, US20150237209, US20150237211, US20150237212, US20150237213, WO2009097018A1
Publication number 13854825, 854825, US 8737595 B2, US 8737595B2, US-B2-8737595, US8737595 B2, US8737595B2
Inventors Zia Chishti, S. James P. Spottiswoode, Chris W. Jones
Patent Citations (155), Non-Patent Citations (119), Referenced by (14), Classifications (13), Legal Events (3)
US 8737595 B2
determining, by one or more computers, agent data comprising agent performance data, demographic and psychographic data for an agent;
determining, by the one or more computers, caller data comprising multiple elements of demographic and/or psychographic data for each one from a set of callers; and
matching one or more of the agents to one or more of the callers, by the one or more computers, using a multi-element pattern matching algorithm, based at least in part on the agent data and the caller data and based at least in part on increasing the chance of revenue generation, to obtain one or more matching results; and
changing, by the one or more computers, a connection priority of one of the callers relative to the other callers in the set of callers or connecting one of the callers to one of the agents based at least in part on the one or more matching results from the matching step, to increase the chance of revenue generation.
2. The method as defined in claim 1, wherein the agent data comprises multiple elements of demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.
3. The method as defined in claim 1, wherein the caller data comprises multiple elements of demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.
4. The method as defined in claim 1, wherein the caller data comprises demographic data, and the demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.
5. The method as defined in claim 1, wherein the pattern matching algorithm comprises an adaptive correlation algorithm.
6. The method as defined in claim 1, further comprising training the pattern matching algorithm by correlating the chance of an optimal interaction against the agent data and the caller data, wherein the agent data and the caller data is determined from at least one historical contact between a caller and an agent.
7. The method as defined in claim 1, wherein the set of callers comprises a queue of callers.
8. The method as defined in claim 1, wherein the matching using the multi-element pattern matching algorithm is performed in a pair-wise fashion to obtain scores for a plurality of caller-agent pairs and wherein the matching results comprise the scores for the plurality of caller-agent pairs.
9. A system for routing callers to agents in a call-center routing environment, comprising:
one or more computers configured with computer-readable program code to perform, when executing the program code, the steps:
determining, by the one or more computers, agent data comprising agent performance data, demographic and psychographic data for an agent;
10. The system as defined in claim 9, wherein the agent data comprises multiple elements of demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.
11. The system as defined in claim 9, wherein the caller data comprises multiple elements of demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.
12. The system as defined in claim 9, wherein the caller data comprises demographic data, and the demographic data selected from the group of gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, and credit score.
13. The system as defined in claim 9, wherein the pattern matching algorithm comprises an adaptive correlation algorithm.
14. The system as defined in claim 9, further comprising the one or more computers configured with program code to perform, when executed, the step of training the pattern matching algorithm by correlating the chance of an optimal interaction against the agent data and the caller data, wherein the agent data and the caller data is determined from at least one historical contact between a caller and an agent.
15. The system as defined in claim 9, wherein the set of callers comprises a queue of callers.
16. The system as defined in claim 9, wherein the matching using the multi-element pattern matching algorithm is performed in a pair-wise fashion to obtain scores for a plurality of caller-agent pairs and wherein the matching results comprise the scores for the plurality of caller-agent pairs.
17. A program product for routing callers to agents in a call-center routing environment, comprising:
a non-transitory computer-readable medium configured with program code to perform, when executed by one or more computers, the steps:
18. The program product as defined in claim 17, wherein the pattern matching algorithm comprises an adaptive correlation algorithm.
19. The program product as defined in claim 17, further comprising program code to perform, when executed, the step of training the pattern matching algorithm by correlating the chance of an optimal interaction against the agent data and the caller data, wherein the agent data and the caller data is determined from at least one historical contact between a caller and an agent.
20. The program product as defined in claim 17, wherein the matching using the multi-element pattern matching algorithm is performed in a pair-wise fashion to obtain scores for a plurality of caller-agent pairs and wherein the matching results comprise the scores for the plurality of caller-agent pairs.
The present application is a continuation of U.S. Ser. No. 12/331,201, filed Dec. 9, 2008, which is a continuation of Ser. No. 12/021,251, filed Jan. 28, 2008, the entire disclosures of which are incorporated herein by reference.
The weightings placed on the various optimal interactions can take place in real-time in a manner controlled by the contact center, its clients, or in line with pre-determined rules. Optionally, the contact center or its clients may control the weighting over the internet or some another data transfer system. As an example, a client of the contact center could access the weightings currently in use over an internet browser and modify these remotely. Such a modification may be not to take immediate effect and, immediately after such a modification, subsequent caller routings occur in line with the newly establishing weightings. An instance of such an example may arise in a case where a contact center client decides that the most important strategic priority in their business at present is the maximization of revenues. In such a case, the client would remotely set the weightings to favor the selection of agents that would generate the greatest probability of a sale in a given contact. Subsequently the client may take the view that maximization of customer satisfaction is more important for their business. In this event, they can remotely set the weightings of the present invention such that callers are routed to agents most likely to maximize their level of satisfaction. Alternatively the change in weighting may be set to take effect at a subsequent time, for instance, commencing the following morning.
Another aspect of the present invention is that it may develop affinity databases that comprise revenue generation, cost, and customer satisfaction performance data of individual agents as matched with specific caller demographic, psychographic, or other business relevant characteristics (referred to in this application as “agent affinity data”). An affinity database such as this may, for example, result in the present invention predicting that a specific agent performs best in interactions with callers of a similar age, and less well in interactions with a caller of a significantly older or younger age. Similarly this type of affinity database may result in the present invention predicting that an agent with certain agent affinity data handles callers originating from a particular geography much better than the agent handles callers from other geographies. As another example, the present invention may predict that a particular agent performs well in circumstances in which that agent is connected to an irate caller.
Accordingly, an embodiment of a method for operating an inbound contact center comprises determining at least one caller, data for a caller, determining at least one agent data for each of two agents, using the agent data and the caller data in a pattern matching algorithm, and matching the caller to one of the two agents to increase the chance of an optimal interaction. In step 501, at least one caller data (such as a caller demographic or psychographic data) is determined. One way of accomplishing this is by retrieving this from available databases by using the caller's contact information as an index. Available databases include, but are not limited to, those that are publicly available, those that are commercially available, or those created by a contact center or a contact center client. In an outbound contact center environment, the caller's contact information is known beforehand. In an inbound contact center environment, the caller's contact information can be retrieved by examining the caller's CallerID information or by requesting this information of the caller at the outset of the contact, such as through entry of a caller account number or other caller-identifying information. Other business-relevant data such as historic purchase behavior, current level of satisfaction as a customer, or volunteered level of interest in a product may also be retrieved from available databases.
Ideally, the connection rules should be configured to avoid situations where matches between a caller in a queue and all logged-in agents are likely to result in a small chance of a sale, but the cost of the contact is long and the chances of customer satisfaction slim because the caller is kept on hold for a long time while the present invention waits for the most optimal agent to become available. By identifying such a caller and jumping the caller up the queue, the contact center can avoid the situation where the overall chances of an optimal interaction (e.g., a sale) are small, hut the monetary and satisfaction cost of the contact is high.
EPO493292A2 Title not available
EPO949793A1 Title not available
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Cooperative Classification H04M3/5236, H04M3/5232, H04M3/5183, H04M3/5233, H04M3/5175, H04M3/5238, H04M3/5158, H04M2201/36, H04M2201/18, H04M3/5235
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