Caesars Entertainment Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

Caesars Entertainment The Case Solution

Independent Variables

In order to find the check-ins in casinos, which are dependent variable, the independent variables are the type of the customers and average daily rate paid for the room by customers,because the type of the knowledge about the type of the customers is very important for the casinos. From that, the casinos would find out which type of customers mostly visit them. This knowledge will help in identifying the target market for the casinos. In addition to this, the average daily rate paid for the room by the customers will help in identifying the relationship of daily room prices with the check-ins, which can further help in identifying the suitable daily rate that would enhance the check-ins at the casinos, ultimately resulting in an increase in their revenues and profits.

The null hypotheses for these independent variables are:

There is no relationship between the type of the customers and the casinos check ins.
There is no relationship between the average daily rate paid for the room by customers and the casinos check-ins.

The alternate hypotheses for these independent variables are:

There is a relationship between the type of the customers and the casinos check-ins.
There is a relationship between the average daily rate paid for the room by customers and the casinos check-ins.

The coefficient of the casino (a total rewards card holder) type of customer is 0.37, which indicates that if 1 customer checks into the casino and has a type of customer named casino then it will increase the total check-ins at the casino by 0.37 check-ins.

The coefficient of the SE (visiting for a special event) type of customer is 0.03, which indicates that if 1 customer checks into the casino and has a type of customer named SE, then it will increase the total check-ins at the casino by 0.03 check-ins.

The coefficient of the Seg 0-3 (in customer segment of 0, 1, 2, or 3) type of customer is -0.07, which indicates that if 1 customer checks into the casino and has a type of customer named Seg 0-3 then it will decrease the total check-ins at the casino by 0.07 check ins.

The coefficient of the FIT (a free and independent traveler) type of customer is 0.22, which indicates that if 1 customer checks into the casino and has a type of customer named FIT then it will increase the total check-ins at the casino by 0.22 check-ins.

The coefficient of the Group (a member of s group) type of customer is 0.2, which indicates that if 1 customer checks into the casino and has a type of customer named Group, then it will increase the total check-ins at the casino by 0.2 check-ins.

The coefficient of the WS (a wholesale customer) type of customer is 0.02, which indicates that if 1 customer checks into the casino and has a type of customer named WS, then it will increase the total check-ins at the casino by 0.02 check-ins.

The coefficient of the FIT ADR (average daily rate paid for the room by a free and independent traveler) is -0.52, which indicates that if 1-dollar average daily rate paid for the room by a free and independent traveler in the casino increased, then it will decrease the total check-ins at the casino by 0.52 check-ins.

Furthermore, there aren’t any concerns regarding the variables, such as: the type of the customers and average daily rate paid for the room by customers or the way it is defined and used in the regression model.

Better Forecasting Model

After calculating the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), Maximum Error and Minimum Error; it has been found that the MAE and MAPE for moving average model is lower than the linear regression model. Liner regression model has 380 MAE, which is higher than the moving average model’s 190 MAE. In addition to this, the MAPE for linear regression model is 46 percent, which is also higher than the moving average model’s 21 percent MAPE.(Vandeput, 2019).

The maximum error and minimum error of the moving average model is also lower than the maximum error and minimum error of the linear regression model. The maximum error of the moving average model is 771, which is lower than the linear regression model’s 819 maximum error. Also, the minimum error of the moving average model is 0, which is lower than the linear regression model’s 5 minimum error.

These analysis helps in concluding that the moving average forecasting model is better than the linear regression forecasting model, for the casino.

More Advanced Time Series Forecasting Model

Although the moving average forecasting model is best time series forecasting model for the casino, in the given scenario, but according to its disadvantage as mentioned in above part of the document that the moving average model responds slowly to rapid price changes that often occur at market turning points. In order to avoid this, the casino should move to more advanced time series forecasting model, which is the exponential moving average forecasting model.

The advantage of an exponential moving average forecasting model is that it responds to the price changes faster than the moving average forecasting model when weighting recent price changes. This is especially useful for traders trying to trade with the highest and lowest levels, with in tar-day fluctuations, as the trend of the exponential moving average model changes faster than the moving average model..................

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