MGM330-0704A-04 Business Decision-Making - Phase 2 DB - Essay Example

The Piggybank project involves the selection of an incentive for the introduction of a new credit the bank is going to offer. The current status of the project is acceptance of the original proposal assessment for the product. There are currently three alternatives incentives in places to choose. In order to determine and analyze the three available options statistical analyzes and concepts must be applied to narrow down the options and find the optimal solution. In order to further analyze the available options this report will utilize a combination of qualitative and quantitative analyzes to access the current alternatives.

The three options of credit discount incentive offers are cash back on online purchases, cash back on purchases at clothing stores and entry into a sweepstakes every time the customer utilizes the credit card. The key quantitative tool that determines which option is more attractive for the customer is probability. Probability is the chance of an event occurring (McClave & Benson & Sincich, 2001). For example if a person flips a coin which has only two possible outcomes the probability of the coin landing on heads is 50%.

The Piggy case provides some research which can help a statistician determine the probability of a client winning a sweepstakes. The odds of winning a prize are 1 in 1000, thus the chances of winning are 0.1%. The company determines that the average American makes 52 credit card purchases a year. Each of these 52 occurrences represents a separate event with a probability of 0.1% of winning the prize. The value the customer receives can be compared to buying 52 one dollar lotto tickets. There are lotteries that offer a 1 in 1000 chance of winning $500 when a person purchases a $1 ticket.

The attractiveness of this option depends of what is the actual prize of the sweepstakes the company is offering. The other probability of the other two option can not be determine right now without additional data, but this does not mean that an initial analysis can not be performed. As far as the probability the two options follow a different probability called subjective probability. Subjective probability utilizes statistical models based on past information in which two different players may have different probabilities for the occurrence of a same event (Levine & Stephan & Krehbiel & Berenson, 2006).

To understand this concept lets analyze each option. The online purchases option implies that a person will be attractive by the incentive because the customer can get cash on online purchases. Online purchasing is a hot trend that accounted for $104 billion in the US in 2006, but in comparison with total US retail sale this number is only 2.66% of the total 2006 US retail sales (Plunckett research, 2007). The attractiveness for the customer of this option depends if the customer is an active online purchasing.

People that make no online purchases with is still the majority of the population this option is not attractive at all. The third option which is the cash back on clothing stores is an option which the company can assume may be attractive to a large population. This option is more attractive for large households that have large clothing needs for its family members since their total purchases are higher. A single person makes less than 40% of clothing purchases than a family with kids. People that buy used clothes or new clothes at flee markets which do not accept credit cards would not be attracted to this option at all.

The size of house is household in this option is a perfect example of the factors that affect relative frequency. Based on the available data the only option that provides an equal incentive for all potential customers is the entry into a sweepstakes alternative which provides an equal incentive for all potential customers. ReferencesLevine, D., Stephan, D., Krehbiel, T. Berenson, M. (2006). Statistics for Managers: Using Microsoft Excel (4th ed.). India: Prentice Hall. McClave, J., Benson, G.

, Sincich, T. (2001). Statistics for Business and Economics (8th ed.) New Jersey: Prentice Hall.Plunckett Research (2007). Ecommerce Industry Statistics. Retrieved October 20, 2007 from Plunckett Research database.