Voice Self-Service Applications for Transport Company Contact Center Clients
Every day hundreds of thousands of passengers use services of transportation companies, and in many cases they call contact centers for information or booking. To handle heavy call traffic, the companies have to resort the help of operators, which is often both expensive and insufficiently effective. ARMO-Vox applications providing automated call processing with speech recognition technology allow contact centers to reduce personnel and operating expenses and offer the most passenger-friendly service.
For transport companies, use of traditional call centers often implies such problems as insufficient number of incoming lines, long hold times, difficulty of centralized alteration of query processing scripts, substantial expenses, and inability to estimate quality of service. Implementation of ARMO-Vox solutions based on voice technologies and offering a wide range of automated applications for call centers help to solve these problems and improve level of service.
Full Range of Passenger Self-Service Functionality
After reaching automated call center, caller,
by asking questions, can obtain complete information about transport schedules,
ticket prices, baggage rules, flight locations and directions. In addition, ARMO-Vox applications allow
passengers to book tickets, or change/cancel bookings, as well as use bonus
system. After rendering a service to a caller, or in outgoing call mode, the system
can conduct surveys or inform about current promotions, offers, or important
changes.
High Implementation Efficiency
Contact center automation with implementation
of ARMO-Vox voice applications ensures faster processing of each call, reduces percentage
of wrong connections and shrinks the proportion of operator services. The system
allows callers to describe call reason in their own words, and determines the
best call routing path. From the moment of their implementation, ARMO-Vox solutions
provide correct recognition of over 80% of queries, while further adjustment
and self-learning capabilities allow to considerably increase this figure.
