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96 Part I • Decision Making and Analytics: An Overview

Application Case 2.2

Station Casinos Wins by Building Customer Relationships Using Its Data
Station Casinos is a major provider of gaming for • Slot promotion costs were reduced by $1 million
Las Vegas–area residents. It owns about 20 proper- (from $13 million per month) by better targeting
ties in Nevada and other states, employs over 12,000 the customer segments.
people, and has revenue of over $1 billion. • A 14 percent improvement in guest retention.
Station Casinos wanted to develop an in-depth • Increased new-member acquisition by 160
view of each customer/guest who visited Casino percent.
Station properties. This would permit them to bet- • Reduction in data error rates from as high as
ter understand customer trends as well as enhance 80 percent to less than 1 percent.
their one-to-one marketing for each guest. The com- • Reduced the time to analyze a campaign’s effec-
pany employed the Teradata warehouse to develop tiveness from almost 2 weeks to just a few hours.
the “Total Guest Worth” solution. The project used
used Aprimo Relationship Manager, Informatica, and Questions for Discussion
Cognos to capture, analyze, and segment customers. 1. Why is this decision support system classified as
Almost 500 different data sources were integrated to a data-focused DSS?
develop the full view of a customer. As a result, the 2. What were some of the benefits from implement-
company was able to realize the following benefits:
ing this solution?
• Customer segments were expanded from 14
(originally) to 160 segments so as to be able to Source: Teradata.com, “No Limits: Station Casinos Breaks the
target more specific promotions to each segment. Mold on Customer Relationships,” teradata.com/case-studies/
• A 4 percent to 6 percent increase in monthly station-casinos-no-Limits-station-casinos-breaks-the-mold-
on-customer-relationships-executive-summary-eb6410
slot profit. (accessed February 2013).

system (mbms). This component can be connected to corporate or external storage
of models. Model solution methods and management systems are implemented in Web
development systems (such as Java) to run on application servers. The model manage-
ment subsystem of a DSS is composed of the following elements:

• Model base
• MBMS
• Modeling language
• Model directory
• Model execution, integration, and command processor
These elements and their interfaces with other DSS components are shown in Figure 2.6.
At a higher level than building blocks, it is important to consider the different types of
models and solution methods needed in the DSS. Often at the start of development, there is
some sense of the model types to be incorporated, but this may change as more is learned
about the decision problem. Some DSS development systems include a wide variety of com-
ponents (e.g., Analytica from Lumina Decision Systems), whereas others have a single one
(e.g., Lindo). Often, the results of one type of model component (e.g., forecasting) are used
as input to another (e.g., production scheduling). In some cases, a modeling language is a
component that generates input to a solver, whereas in other cases, the two are combined.
Because DSS deal with semistructured or unstructured problems, it is often necessary
to customize models, using programming tools and languages. Some examples of these are
.NET Framework languages, C++, and Java. OLAP software may also be used to work with
models in data analysis. Even languages for simulation such as Arena and statistical pack-
ages such as those of SPSS offer modeling tools developed through the use of a proprietary

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