טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
M.Sc Thesis
M.Sc StudentNitzan Carmeli
SubjectModeling and Analyzing IVR Systems, as a Special Case
of Self-Services
DepartmentDepartment of Industrial Engineering and Management
Supervisor Professor Emeritus Kaspi Haya
Full Thesis textFull thesis text - English Version


Abstract

Call centers play a prominent role in today's economy. They serve as the main customer contact channel in various enterprises, which makes them highly labor-intensive operations. Thus, call centers look for means to reduce the number of agents handling calls, and trying to do so without degrading service level. Interactive Voice Response (IVR) systems are presently one of the main self-service channels employed by call centers. They are used as means to reduce operating expenses derived from agent employment costs.

The goal of our research is to improve and enhance IVR systems, aiming to create a body of knowledge that will generalize to other self-service systems. We model customers flow within an IVR system as a stochastic search in a directed tree. The search goal is to find the optimal path on the IVR tree, which will result in maximal expected discounted utility for customers. We show that a calculable index can be assigned to each feasible option, and the optimal policy is to choose the option with the highest index at each stage.

Our model building blocks were created through an Exploratory Data Analysis (EDA) of real IVR transactions, in a call center of a large Israeli bank. The EDA revealed interesting phenomena regarding customer abandonments and learning within the IVR.

Our work enables the comparison between alternative IVR designs, both from the customer and the enterprise point of view. This complements related research in other fields, such as Human-Factor-Engineering and Telecommunication.

The model for IVR systems that we developed can easily be implemented to other self-service systems such as Internet websites which have become prevalent.