This invention relates generally to telephony, and more particularly, to automatic call distributors.
An automatic call distributor (ACD) is a system that facilitates the management of incoming and outgoing calls at telephone call centers. Traditionally, an ACD consisted of a specialized telephone switch for routing customer calls to available agents. The method for routing such calls is typically based on obtaining an equitable distribution of calls for each agent.
Today, like all business entities, call centers are experiencing incredible growth due to an increasingly interconnected, global and digital world. Incident to the operation of call centers is the accumulation of vast quantities of valuable information. In response to the availability of such information, owners of these call centers are demanding ACD systems with more sophisticated information management and channeling capabilities. Thus, modern ACD systems commonly incorporate various management tools including automated call identification, automated agent selection, automated call distribution, automated voice response, data integration, voice messaging, automated outbound call management, and real time displays that forecast load and analyze historical data.
Furthermore, modern ACD systems typically use improved methods for routing calls other than the traditional method based on equitable distribution. One such method is skill based routing. Skill based routing is typically based on the identity of the caller or on the number called and enables a call center to route calls to the agent most qualified to handle the customer request. When coupled, for example, with additional services such as a Dialed Number Identification Service (DNIS) that identifies a particular service number called by a customer, skill based routing can greatly increase the productivity of a call center.
While skill based methods for routing calls are commonly found in modern ACD systems, there is no known analogous development of a language based method of routing calls. However, in an increasingly global business environment, the need to identify the native language of customers is obvious. For example, when a customer answers the phone during an outdialing campaign, she may not necessarily speak the same language as the agent handling the call. Thus, regardless of any advantages that skill based routing might provide, such language barriers lead to confusion between the agent and customer which ultimately decreases productivity for the call center.
In March 1997, the U.S. Census Bureau estimated that the foreign born population of the United States was 25.8 million. This represents the largest foreign born population in United States history and an increase of 30 percent over the 1990 statistics (see Schmidley, A. Dianne and Campbell Gibson, U.S. Census Bureau, Current Population Reports, Series P23-195, Profile of the Foreign Born Population in the United States: 1997, U.S. Government Printing Office, Washington, D.C. 1999). Furthermore, marketing studies and commentaries have suggested that people feel more comfortable conducting business in their native languages (see “Spanish Language Advertising Most Effective, Even For Bilingual”, Minority Markets Alert, Vol. 6, No. 12, Dec. 1, 1994; Hamlyn, Helen, “Speaking the language of your customer”, Telemarketing, Vol. 11, No. 12, Jun. 1, 1993). Therefore, with the significant increase of non-English speaking households in the United States, call centers must be able to identify and service customers in their native languages in order to conduct effective business.
Accordingly, the present invention provides a method and system for determining a language of a call handled by an automatic call distributor. The method includes the steps of detecting the call, sampling an audio portion of the call, fitting a plurality of templates to the sampled portion of the call, and determining a language of the call based upon a best relative fit between one of the plurality of audio templates and the sampled portion of the call.
Similarly, the automatic call distributor system, comprises, in part, capabilities for detecting a call. Once the call is detected, the system may sample an audio portion of the call and attempt to match the audio sample to one of a plurality of audio templates that has the best relative fit. By doing so, the automatic call distributor is able to determine the language of the call and can route the call to an appropriate response service. Such response services may include, but are not limited to, human agents, voice response units (VRU), or interactive voice response (IVR) applications. If, however, the system is unable to obtain a best relative fit, it may ultimately route the call to a default agent.
One of the primary objectives of the invention is to identify the language of a customer who answers the phone during an outdialing campaign. When a customer answers the phone, they typically speak a greeting in their native language. For example, a Spanish speaking person might answer the phone by saying “BUENO”. The automatic call distributor would analyze the voice pattern of this initial greeting and compare it against sample greeting patterns in different languages to determine the customer's language.
Another main objective of the invention is to route the call to a response service capable of servicing the language of the customer. Such a service might be a human agent who can speak the language or an automated voice response unit with messages recorded in the language of the customer.
The advantages of identifying and routing a call based on the language of the customer are increased efficiency and productivity at the call center and higher customer satisfaction with call center service.
Further objectives, advantages and features of the invention will become apparent from the following detailed description of preferred embodiments of the present invention when taken in conjunction with the accompanying drawings in which like reference numerals designate like elements throughout the different views.
Referring the drawings,
With reference to
In an alternative embodiment of
For example, in one embodiment, the external PSTN provides the surname of the calling customer to the ACD. The ACD then uses a best guess method to determine the language of the caller corresponding to the surname and switches the call to an appropriate agent or VRU. In an alternative embodiment, the ACD uses the ANI number to identify a geographic location of the calling customer to determine whether there is a specific language associated with that location. For instance, in either of these embodiments, one could envision an IVR in the ACD greeting the calling customer with a salutation in the “best guessed” determined language. The ACD would then analyze the response of the calling customer to confirm that the best guessed language was indeed the proper language to use. If the CPU 200 should determine that the initial language was incorrect, the CPU 200 reconnects the call to the appropriate response service.
In another embodiment, the ACD is coupled to an internal database that stores the language used by a repeat customer. Upon obtaining the ANI number of a calling repeat customer, the ACD determines the language of the customer by accessing the database through the ANI number. In yet another embodiment of the invention, the ACD is coupled to a database, internal or external, that provides marketing information associated with the ANI number, including the language of the customer.
While a detailed description of the preferred embodiment of the invention has been given, it should be understood that implementation of other variations and modifications of the invention and its various aspects will be apparent to one skilled in the art, and that the invention is not limited by the specific embodiments described. Therefore, it is contemplated to cover the present invention, any and all modifications, variations, or equivalents that fall within the true spirit and scope of the basic underlying principles disclosed and claimed herein.
Number | Name | Date | Kind |
---|---|---|---|
4937870 | Bossemeyer, Jr. | Jun 1990 | A |
5367609 | Hopper et al. | Nov 1994 | A |
5475733 | Eisdorfer et al. | Dec 1995 | A |
5479488 | Lennig et al. | Dec 1995 | A |
5555179 | Koyama et al. | Sep 1996 | A |
5594791 | Szlam et al. | Jan 1997 | A |
5675707 | Gorin et al. | Oct 1997 | A |
5703935 | Raissyan et al. | Dec 1997 | A |
5758322 | Rongley | May 1998 | A |
5765033 | Miloslavsky | Jun 1998 | A |
5841852 | He | Nov 1998 | A |
5926539 | Shtivelman | Jul 1999 | A |
5946387 | Miloslavsky | Aug 1999 | A |
5953332 | Miloslavsky | Sep 1999 | A |
5953405 | Miloslavsky | Sep 1999 | A |
5999965 | Kelly | Dec 1999 | A |
6002760 | Gisby | Dec 1999 | A |
6021428 | Miloslavsky | Feb 2000 | A |
6029124 | Gillick et al. | Feb 2000 | A |
6035187 | Franza | Mar 2000 | A |
6044145 | Kelly et al. | Mar 2000 | A |
6044368 | Powers | Mar 2000 | A |
6067357 | Kishinsky et al. | May 2000 | A |
6069939 | Fung et al. | May 2000 | A |
6108711 | Beck et al. | Aug 2000 | A |
6138139 | Beck et al. | Oct 2000 | A |
6167395 | Beck et al. | Dec 2000 | A |
6170011 | Beck et al. | Jan 2001 | B1 |
6175563 | Miloslavsky | Jan 2001 | B1 |
6175564 | Miloslavsky et al. | Jan 2001 | B1 |
6185292 | Miloslavsky | Feb 2001 | B1 |
6229881 | Alonso et al. | May 2001 | B1 |
6243675 | Ito | Jun 2001 | B1 |
6345305 | Beck et al. | Feb 2002 | B1 |
6373836 | Deryugin et al. | Apr 2002 | B1 |
6389007 | Shenkman et al. | May 2002 | B1 |
6393015 | Shtivelman | May 2002 | B1 |
6442242 | McAllister et al. | Aug 2002 | B1 |
6535596 | Frey et al. | Mar 2003 | B1 |
6614885 | Polcyn | Sep 2003 | B2 |
6732156 | Miloslavsky | May 2004 | B2 |
6912499 | Sabourin et al. | Jun 2005 | B1 |
Number | Date | Country |
---|---|---|
4228997 | Mar 1994 | DE |
4430991 | Mar 1996 | DE |
001119169 | Jul 2001 | DE |