Data Mining And Business Growth: A Case Study Of Harrah Corporation

The Benefits of Data Mining in Business Growth

Data mining remains an integral part of modern-day business; customer databases are sources of invaluable information for strategic planning. Harrah Corporation launched a client reward program which aided in giving his customer incentives and provided information on customer movements. The info from analysis through dependency analysis, cluster analysis, classification, and data summarization among other aided in improving customer loyalty and teamwork among the staff members. Additionally, the data analysis helped maintain a competitive edge over others in the industry. The company predominantly used the opportunistic data-driven approach to extra information and achieved sustainable development.

Jackson (268) outlines two essential approaches when it comes to data mining. She asserts that the average analyst will use either “traditional, hypothesis-driven analysis approach or opportunistic, data-driven approach” (Jackson 268). These approaches occur as a result of the objective assessment. People choose the criterion by whether they want to construct models or detect patterns. The hypothesis- based method involves producing a summary from a data set. The summary becomes embodied as a graph by those involved in research.  Jackson postulates to the idea that some models such as “cluster analysis partition of a set of data, the regression model for prediction, and a tree-based classification rule” (Jackson 268). The research develops either as a result of an underlying belief or through operational assertions.

Additionally, Jackson mentions the pattern detection method based on data analysis (Jackson 268). The general idea in the examination of large databases is the detection of anomalies in specific processes. These anomalies act as points of references or affirmations needed for decision-making milestones. A good illustration of how the approach originates from the widespread fraud detection in credit card utilization. People use the approach small bits of information from large databases. The outlier theory that involves exposure of strange patterns or elements in the system; these elements would include “discontinuity, noise, ambiguity, and incompleteness” (Jackson 268). The author supports the assumption that predictive power increased as the number of faults/ irregularities increases in the system.

Businesses take up data mining techniques to lower costs, increase profits, and ensure sustainable growth. An interesting imperative in the corporate world involves developing a data gathering system; Most of these processes examine customer and employee characteristics for growth and development. Jackson (271) proposes the use of data mining to extra information and reduce unnecessary expenses for organizations. The knowledge gained is vital in establishing a competitive edge over rivals. She goes further to affirm that businesses use it to regulate all phases of the customer lifecycle. The customer-oriented initiative takes place through goals such as “acquiring new customers, increasing revenue from existing customers, and retaining good customers” (Jackson 272).  The companies develop models which provide an endless list of characteristics of customers and their impact on the business.

The launch of the Web creates a highly productive platform for gathering and analyzing information. Virtual databases which interact with customers and employees collect information. The data collected becomes grouped into “the kinds of data available, the segment of the population from which the data was collected, and its method of implementation” (Jackson 276).

Traditional vs. Opportunistic Data-Driven Approaches

 As a result, the management performs different processes closely related to pattern discovery and pattern analysis (Jackson 278). Jackson provides a definitive recommendation about the internet; he talks about how cyberspace gives a 24-hour interface where customers interact with the business without any geographical restrictions.  The ability to collect data without incurring an additional fee or having to wait a particular time is a technological jackpot for corporations in the modern age. An excellent means of exploring these sentiments arise from critical analyzing of a case study such as the Harrah Corporation.

Harrah case study gained numerous successes from data mining techniques. A close analysis of the company plan of action shows that they applied opportunistic data-driven approaches predominantly even though some hypothesis-based approaches exist in small-scale. Loveman (3) introduces the organization by ascertaining the heightened level of customer satisfaction enjoyed by the company. The first proof that they use data-based driven strategies is his remark that they use “database- marketing and decision-science-based analytical tools” (Loveman 4) to compete in the industry. The company opposes the idea of the traditional hypothesis-oriented system starting that they prefer evidence to intuition. They allow the statistical results to determine the best course of action rather than make an abstract decision from the managerial point of view. The primary goal of such a culture involves having customers who come back and more frequently over the years.

The Harrah entertainment company sought to bridge the market gap by detecting unusual behavior patterns among its customers. The management decided to focus on sustainable growth over long periods rather than investing in single visits to their premises (Loveman 4). An excellent statistical revelation which proves they use data-driven approaches is their findings in 2001 which showed that 87.2 % of the earnings come from casinos (Loveman 4). Strangely enough, they began to realize that 82% of the said incomes came not from the wealthy clientele; the majority of casino revenue from average people namely “former teachers, doctors, bankers, and machinists-middle-aged and senior adults” (Loveman 5).

These people enjoyed the pure pleasure of gambling and had time to do it. The unique and specific details of customer behavior and groups suggest that the management practiced cluster analysis, association rule learning, and regression analysis (Jackson 273-274). In effect, the company develops a system of collecting customer data while offering incentives and rewards for actions.

Loveman (4) talks about the card project which helped clients acquire points for use in their facilities. The management asserts that the system was not so big because other entertainment companies had the same thing. However, they used the reward program to track the movements of their clients and learn about what works for them. The information collected was approximately 300-gigabytes (Loveman 4). They made incredible discoveries about the majority of users.

The customers wanted incentives that were within casino rather than those targeting the restaurants and hotels. For example, the regular visitors preferred a $60 reward in the form of casino chips than free hotel rooms. In doing so, the management came up with a system of Gold, Platinum, and Diamond levels that checked on things like casino waiting time and other privileges. The differentiation in customer service proved effective in motivating existing clients and attracting new ones. The analysis from the reward program shows that classification, prediction, and segmentation originated from the database. The entrainment company relied on data findings from the statistical analysis rather than make unfound assumptions.

The Use of Data Mining in Customer Lifecycle Management

Another point of reference is the ability to attach customer satisfaction to employee behavior and other internal processes in the franchise. Dependency analysis showed that customers preferred certain kinds of slot machines irrespective of time and line-up. The management rearranged the casino floor so that preferred devices were accessible to the target group. The topological change proved lucrative for customers and the organization. The regression analysis and association rule applied in making changes.

Then again, customer satisfaction depended mostly on employee behavior, and the organization knew this by checking the databases. Branches were employees were quick in service, and friendly made the highest profits irrespective of general socioeconomic constraints in the industry. As a result, they came up with an employee incentive program attached to customer satisfaction. The happier the client was, the more money a worker would make in the end. Bonus plans proved useful in all branches and clients remained loyal. Noteworthy, the company disassociates financial performance from customer service/ satisfaction. The idea was to avoid associating customers with short-term financial remunerations; they wanted clients to be satisfied first then wait for the long-term rewards. All these dependency relationships prompted the business to a whole new level of success.

The hypothesis-based initiatives in the end when the management attempts to cultivate a culture of teamwork. Most of the strategies discussed are data-based; however, the idea of collaborative work among employees draws its assumption from information about customer satisfaction. The managers assume that each staff member is responsible for the performance of the overall department or branch. In effect, the receptionist can help those on the valet ( (Loveman 6) and the general manager has an open door policy even when off-duty (Loveman 7).

These strategies grew with hopes of improving the overall staff performance and boost customer satisfaction. The operational approach creates models and attests to the concept of data summarization. These regulations improve the robustness of staff relations and develop a sense of healthy competition among branches or departments. Incompetence is a rare occurrence in organizations where every person is alert and aware of any inaccuracies.

The critique of the case study shows that an integrated compilation of techniques was crucial to the overall success. Interestingly enough, the company utilized overwhelming techniques associated with the opportunistic data-driven method as compared to the hypothesis-oriented approach. The table (Table 1) explores and summarizes these tasks and procedures

Table 1: Data Analysis

DATA  Analysis Techniques	Data Summarization	Segmentation	Classification	Prediction	Dependency Analysis
Descriptive and Visualization	ü			ü
Cluster Analysis			ü
Discriminant Analysis		ü	ü
Regression Analysis					ü
Neural Networks
Case-Based Reasoning	ü				ü
Decision Trees		ü		ü
Association Rules		ü		ü

Harrah Entertainment is an excellent depiction of a modern company which values data extraction. They took advantage of the immense wealth of information stored in their database to make concrete business decisions. Moreover, they realized that customers and employees who received motivation were best for the continuity of the company. They came up with a player card system and customer levels to collect and differentiate customers depending on their worth to the corporation.

Some customers enjoyed privileges such as short or no waiting lines and added benefits depending on the level of worth. The employees received bonuses which focused on the satisfaction of clients. All these findings developed from the database associated with the incentive program for customers. The system tracked and correlated movements of clients in slot machines and different branches countrywide. Data techniques such as classification, summarization, dependency analysis, and cluster analysis helped shape the futuristic plans for Harrah Corporation.

Jackson, Joyce. “Data Mining; A Conceptual Overview.” Communication of the Association for Information Systems Vol.8, Article 19 March 2002: 267-296.

Loveman, Gary. “Diamonds in the Data Mine.” Harvard Business Review May 2003: 3-7.