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英国留学生英语本科课程作业范文模板—Pricing and Revenue Management Individual Coursework
您需要定制的论文具体信息如下：
论文题目：Pricing and Revenue Management Individual Coursework
论文语言：英语论文 English
指导价格：1500RMB
论文专业：Pricing and Revenue Management
字数：2000-2500
学校国家：英国
是否有数据处理要求：否
您的学校：Lancaster Univer
论文用于：BA Coursework 本科课程作业
截止日期：2012-01-25
Pricing and Revenue Management
This individual coursework report is due Jan 25th, 2012, at noon. Please submit a paper copy of your report that explains clearly how you address the following exercise http://www.ukassignment.org/ygkczy/ questions. You can copy part of your spreadsheet or SPSS output into the report and use the annotation feature to comment on the content in cell. In addition, please submit two pieces of your course work in electronic form on LUVLE. First, please submit your printed report as a word file. Please name your file MSCI381-LastName-LibraryCardNumber.docx, e.g., in my case the file name will be MSCI381-Pang-00112233.docx. Second, please also submit your excel-workbooks with your numerical answers for the two exercises. This will be used only if your paper answer is not self-explaining and we need to check how you derived the solutions. If you use SPSS, please copy the output to the excel sheet. Please put the answer for each question on a separate sheet within the Excel-workbooks (e.g., your submission will have 5-6 sheets named Pricing 1, …, Pricing 4, Conjoint 1, …). Please name your file MSCI381-LastName-LibraryCardNumber.xlsx.
Part A: Petrol Pricing Exercise (60%)
This exercise is based on the guest lecture provided by KSS Fuel. You need to use the sample data provided by KSS Fuel to fit the demand model and analyze the pricing strategies. You can find the data in LUVLE.
The data in the file shows Petrol sales in gallons and prices in dollars from 5 petrol stations in the USA. The columns are as follows:
• Site – this is a number indicating which site the data is from
• Date
• Weekday
• Vol – the volume in gallons of regular unleaded petrol sold at the site on that day
• Price – the price per gallon in dollars changed by that site
• AvgCompPrice – The average price charged by competitors to the site
• MinCompPrice – The lowest priced competitor to the site on the given day
• MaxCompPrice – The highest priced competitor to the site on the given day
Exercises
1. Fit the price-response demand model using aggregate data (whole sample) , analyze the price elasticity and provide insight into petrol pricing. (20%)
Hints: Fit the demand model using the following approaches:
a. Linear regression with volume (sales) as the response (dependent variable) and own price and average competitor price as the predictors (independent variables).
b. Linear regression with natural logarithm (LN) of volume as the response and LN(price) and LN(average comp price) as the independent variables.
c. Include the weekday as a predictor by creating dummy variables (Note that you need to remove linear dependency).
d. You can also include the month or season as the predictor to address the seasonality effect.
e. You can include the site as a predictor by creating dummy variables.
f. You can further improve the model performance by examining the reference price effect and competition effect.
Write down the model coefficients and R squared output (and adjusted R squared output), then compare the goodness-of-fit using the R squared output. Next, choose the best performance model and write down the demand function. Finally, comment on the price elasticity of the aggregate sales and the impact of different characteristics (e.g.,weekday, seasonality, location or cite) on the price elasticity.
2. Fit the price-response demand model for each site separately and compare the price elasticity for the sites and to the aggregate model in Question 1. (20%)
Hints: Same as that of Question 1.
3. Suppose that you use a static pricing strategy. If fuel costs $2.7 per litre and the average competitor price is $3 what would be my optimum price for each site? Fixing the fuel cost at $2.7, apply the same algorithm to all the records in the sample and compare the performance of your model (total optimal profit based your model) to the actual performance (total actual profit). (10%)
Hints: Write down your profit function as total revenue minus total cost, substitute in your demand model (the best model you found) and find the turning (or maximum) point of the profit curve using calculus, as done in the lecture. You can then compute the demand and profit using the optimal price you found. To apply the algorithm to all the record, you need to find a simple formula for the optimal price using the approach introduced in the lecture; otherwise, you will have to take long time to compute the optimal price one by one for 1738 records.
4. Suppose that you use a dynamic pricing strategy. Fixing the fuel costs at $2.7 per litre, please compare the performances of the dynamic pricing strategy and performance of the static pricing strategy and the actual performance using the sample data. (10%)
Hints: First, choose a demand model. Second, compute the optimal pricing decision for each record in the sample. For example, the average competitor price was 3.07 at site 2 on 8/2/2010 and volume was 2545 litres and the price was $3.1. You can substitute in your demand model the information (AvgComPrice=3.07, Site=2) to find the theoretical optimal price and the corresponding profit for this record. You can applying the same algorithm to all the records and then summarize the total theoretically optimal profit of your model. Then, you can compare the performance of the dynamic pricing strategy to the performances you obtained in Question 3.
Part B: Full-factorial Experimental Design for Smart Phones (40%)
Suppose you are helping the smart phone manufacturer Samsung to design a new smart phone, targeting UK’s smart phone market. The product will be formally launched http://www.ukassignment.org/ygkczy/ in the fall of 2012. The main competitors are Apple (iPhone) and HTC. To understand the consumer preference, you need to conduct a full-factorial conjoint experiment. Please propose a design and perform the experiments and the conjoint analysis for at least 10 respondents to provide some insight.
Hints: You can restrict your attention to a few attributes (e.g., brand, system (iOS, Android or Windows) screen size, price, weight, etc.) I suggest you to focus on 3 to 4 most important attributes. For each attribute, you can only consider two or three levels. Note that a three-attribute and three-level-each design has already 3×3×3=27 product concepts. Next, you can request respondents to rate all the product concepts using the scale (0-10). Please also record some demographic information of the respondents (age, gender, whether they have own a smart phone and what brand/model). Then, you can use linear regression to find out the path worth utilities for each level and each attribute and the relative importance. Finally, you analyze the distribution of the path worth utilities and relative importance.
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