Archive for September, 2015

Call Center Trunks Optimization

September 9th, 2015 Comments off

Client: ORM Technologiesorm
Team: Lexi Farrar, Ashley Hall, Neimy Sarmiento
Faculty Advisor: Dr. Barr     Year: 2014
Documents: Presentation, Report, Video
ORM Technologies currently has a call center client looking to make their call center more efficient. Currently, ORM Technologies has a functioning system that uses web-based inputs that allowing the customer to input data and generate optimized resource requirements. The system runs an Erlang C profile for every agent level in the pre-model. From there, the optimization selects the minimum headcount required by 30 minute intervals by day, week, month and year. Some models are 12 months, but most are 5 years.

Our team had the task of using Erlang B probability distribution to dynamically estimate and model the most cost-effective number of trunks, or phone channels, needed to service a live call center by trunk type. The optimization model is written in GAMS, General Algebraic Modeling System, for integration into the client’s existing system. (Our team has no prior experience with GAMS.)

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Clustering and Segmentation of Ticketing Data

September 9th, 2015 Comments off

Client: Sabre Airline Solutionssabre
Team: Hunter Ross, Mary Liz Tuttle, Ramon Trespalacios
Faculty Advisor: Dr. Barr   Year: 2014
Documents: Presentation, Report, Video

Sabre Airline Solutions offers data solutions and software to aid airlines sell products, market themselves, and operate efficiently. The company would like to provide traveler segmentation services for their customer reservation system to support various marketing programs. (Segmentation involves classifying prospective buyers into groups, or segments, to create products specifically for each segment.) This project required creating segmentation rules that classify ticket purchase data in this manner.

The senior design team replicated the data to create pre-booking and post-booking results. Pre-booking segmentation will show clusters that do not include variables such as fare and travel time, because these can’t be known until after booking. On the other hand, post‐booking data will provide segments that include purchases made. Pre-booking clusters could be used to make promotions for customers while booking, and post-booking clusters could be used to make promotions after booking.

The team used k-means clustering method and the R software to find the optimal number of clusters in the data and assist Sabre with the design of good fare products. For example, if an airline has created a ticket fare product for a specific market like business‐travelers, the team’s segmentation rules can confirm whether the product is well‐defined and well-targeted.