Client: Sabre Airline Solutions
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.
Client: Frito Lay
Team: Rodrigo Cantu, Sergio Hueck, Rafael Virzi
Faculty Advisor: Dr. Barr
Year: 2012
Documents: Presentation, Report, Video
Gamesa, a subsidiary of Frito-Lay, is a Mexican Cookie company that sells its product in many different countries, including the United States. Their products, which includes different types of cookies and crackers, are targeted to the Mexican population. The company’s United States sales force consists of 38 representatives in 16 different regions, organized by their different routes to market, location of warehouses, and population density. Today, they dominate the U.s. Hispanic cookie market occupying 50 percent of the market.
The problem we address for Gamesa is: should they should deploy more sales representatives and, if so, where would their optimal locations be? With the current economic recession and the entering of Gamesa’s main competitor, Bimbo, sales have been dropping in the different regions. This resulted in Gamesa asking themselves if they needed a bigger sales force. They also wanted to know what regions could be good to add representatives in the future depending on the migration of the population or the strategy of the competition. Read more…
Categories: Final report, Forecasting, Manufacturing, Marketing, Operations, Optimization, Presentation, Real estate, Simulation, Statistical Methods, Uncategorized, United States, Video Tags:
Client: Capstone Asset Management Co.
Team: Mallory Harrison, Natalie Jaroski
Faculty advisor: Dr. Barr Year: 2010
Documents: Final report (PDF), Presentation (PDF)
Capstone is a privately owned investment advisory firm in Houston, TX that offers privately managed accounts to achieve client’s financial objectives. They currently manage over $3.8 billion in assets for about 3,000 different clients. They provide products and services through three distribution channels: brokers/advisors, institutions/corporations, and high-net-worth individuals. Revenues are generated by marginal product fees associated with assets under management allocated by product.
Capstone has in place a detailed budgeting process but desires to expand the process to include financial modeling to measure profitability by product. Our goal for this project is to develop a model that would calculate existing profitability on assets under management by product. Our second goal is to develop an optimization model that takes into account the revenues and expenses associated with sales of existing products versus the revenues and expenses associated with the development of new products. The final output of the optimization model is to identify the most profitable mix for new product sales. Read more…
Categories: Final report, Finance, Forecasting, Houston, Marketing, Optimization, Planning, Presentation, Scheduling, Simulation, Texas, United States, Video Tags:
Client: Lennox Industries
Team: Diana Batten, Maddie Kamp
Faculty Advisor: Dr. Siems
Year: 2011
Documents: Final Report, Presentation
Lennox Industries is a residential and commercial heating and air-conditioning company with a 13-15% market share in about 80 countries. Lennox is unique in that they operate as both the manufacturer and distributor of their products selling directly to their customers, typically contractors.
This project identified correlations between customers’ buying patterns and their attrition, or loss of their business. Based on transaction-level data of 4,500 customers over a three-year period, an early-warning model was developed to signal the potential loss of a customer and enable Lennox to act preemptively. The analytics were based on a ranking procedure based on key indicators and a Markov chain analysis with categorical transition probabilities derived from historical data. With this model encapsulated in spreadsheet form with the ability to customize the analysis geographically and seasonally, the results give Lennox management a new tool to maintain their current customers and evaluate new markets.
