Based on the data below, forecast US hotel revenues for 2017, 2018, and 2019. Provide the model developed for your calculations.

YEAR REVENUE ($USD BILLION)

2001 105.00

2002 111.90

2003 118.80

2004 119.30

2005 135.50

2006 146.20

2007 153.80

2008 154.70

2009 133.30

2010 142.00

2011 153.30

2012 155.50

2013 163.00

2014 176.70

2015 189.50

2016 199.30

Please give a movie review of any movie. The movie can be any ENGLISH speaking movie of your choice. Examples would be Star Wars (newer series), Best Exotic Marigold Hotel, The 100 Foot Journey etc.. Any film that you feel will suit the assignment. Please make sure the movie is NOT a foreign film, and that it is no older than 10-15 years old.

at least 3-4 pages

Suppose a particular test of cognitive ability has been found to have great practicality in selecting members of a high school debate team. How much practicality would this same test have for the following situations?

Law school application

Art school application

A police hostage negotiation unit

Executive level positions in a labour union

Actors in a theme park who spend their day dressed in a character costume

You are a hotel manager and you are considering four projects that yield different payoffs, depending upon whether there is an economic boom or a recession. The potential payoffs and corresponding payoffs are summarized in the following table. Project Boom (50%) Recession (50%) A $20 -$10 B -$10 $20 C $30 -$30 D $50 $50 If a manager adopted both project A and B simultaneously, the variance in returns associated with this joint project would be:

On a dark desert highway, a car leaves a gas station and heads straight for 10 km, heading exactly northeast.

(i.e., directed 45 degrees northward of due east). The car then turns onto a dirt road and drives completely

straight until it reaches a hotel located exactly 12 km north and 9.0 km east of the gas station. How far did the

car drive along the straight dirt road?

Suppose you have assumed the role as the construction manager (CM) in one of the City of Kelowna’s water park projects. The previous CM resigned the City. The project is 2 months behind the schedule. Over 75% of the allocated budget has been spent and only 55% (based on number of installed/constructed items) of the construction has been completed.

1. What will be your immediate actions in the first few days? - (300 words maximum)

Assume that you are hired in a marketing manager position at a hotel. You are supposed to develop a marketing positioning statement and communicate it with all employees so that they can understand organizational marketing directions. There is no precedented work about a marketing strategy, and sooner or later, you need to start with the work. How can you lead to develop the positioning statement? Explain the process of developing the statement from the beginning (NOT about just making the statement).

Case: Capital One’s Online Profiles

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In 2010, Capital One Financial Corporation began using special software to createinstantaneous profiles of visitors to its website. Constructed from information such as recent purchases, web browsing history, and geographic location, these profiles were used mainly to determine which credit card offers to display on a visitor’s computer screen.136

Customer Profiles

In the case of one customer, Carrie Isaac, Capital One’s website used “cookies” left by other websites, her Internet Protocol (IP) address, and other technical information transmitted by her computer to conclude that she was a member of the “White Picket Fences” group, a profile for customers who are thought to be middle-class parents who live in a metropolitan suburb and have reliable creditworthiness. Capital One used sophisticated algorithms to determine correctly that she was female and a young parent and that she earned approximately $50,000 annually, had attended, and shopped at discount department stores. On the basis of this information, Capital One’s software displayed a credit card designed for people of average creditworthiness with no annual fee and an initial monthly interest rate of zero percent, increasing to 19.8 percent thereafter. Overall, Capital One’s inferences about Ms. Isaac’s identity were accurate.

The same appeared to be true of another potential customer, Paul Boulifard. Capital One’s website focused on Mr. Boulifard’s residence in Nashville, Tennessee, and his interest in travel. It displayed the “VentureOne Rewards” credit card to him, which allows the accumulation of points that can be used to purchase airline tickets. The images surrounding this card included a beach scene and the slogan “Still Searching? Get double miles with Venture.”

In the case of Karyn Morton, however, Capital One’s software was less accurate. Ms. Morton was profiled as a member of the “City Roots” segment. Capital One accurately determined that she was a homeowner living in Detroit, a member of the National Association for the Advancement of Colored People (NAACP), and a regular reader of major newspapers. It inaccurately inferred that Ms. Morton was retired without children, had little education, and was living on a modest income of $28,000. She actually earned three times that amount, was 33 years old, and held a law degree. Capital One offered Ms. Morton two credit card options, one for individuals with average credit scores and an interest rate of 24.9 percent and one for customers with excellent credit scores and an interest rate of 13.9 percent.

Use of Profiles

Capital One emphasized at the time that it did not use the information gathered in a visitor’s online profile to determine who actually received certain lines of credit. It used only the concrete information voluntarily offered by a customer on a credit application for such purposes. Capital One, therefore, did not violate the Equal Opportunity Credit Act, a federal law that prohibits banks and other lenders from targeting or restricting financial services based on race, ethnicity, national origin, or residency.137 Capital One claimed that it simply made an “educated guess” about what it thought customers would want and featured products based on those inferred preferences.138

Capital One’s efforts at product placement were not unique. Other online retailers have used similar methods in setting online prices.

In 2012, Orbitz, the online travel site that provides low-priced deals on car rentals, hotel rooms, and airfares, offered the same products to different customers at different prices.

Customers who used desktop computers with an Apple operating system paid 30 percent more for hotel rooms compared with customers who booked the same rooms using computers with a Microsoft operating system.139

The office supply giant Staples has sold products at different prices depending upon a customer’s proximity to competitors’ stores. A recent investigation found that theStaples.com website displayed different prices to different people by “estimating” their location based on their computer’s IP address. Staples considered the distance from a competitor’s store, such as OfficeMax or Office Depot, and if a store was located within 20 miles, then a discounted price was shown.

Profiling Technology

Capital One arguably refined a common practice. Marketing decisions involving product placement and pricing have long been guided by the concept of “segmentation.” The marketplace is composed of groups of customers—or segments—with different experiences, demographic traits, and preferences. The rise of information technology and e-commerce has enabled marketers to modify the manner in which they sell products based on their knowledge of the segment to which a potential customer belongs. Segments provide a useful, if imperfect, guide to quickly predict a customer’s likely purchases.

Capital One’s software was engineered by a little-known supplier, [x+1], Inc. Neither this fact nor the exact methods employed by the profiling software were disclosed to visitors on the website. Capital One did disclose that it collected and used visitors’ IP addresses, browser and operating system information, “cookies” placed by other websites, navigation preferences, social media activity, and geographic data. These disclosures, however, were placed within the “privacy” section of Capital One’s website, located at the bottom of the user’s screen in small font. This is typical in the online commercial environment. Internet users are rarely cognizant of how they are being profiled, and privacy disclosures are not easy to find without some effort.141 Users also expect their online activity to take place in a market that provides impersonal, even anonymous, interaction. This expectation is apparently important to Internet users. Marketing studies142 indicate that consumers typically find product and price customization problematic when there is a lack of transparency regarding the customization efforts. When consumers expect standardized sales experiences, customized experiences are considered unfair, but if there is an expectation that product offers or prices will differ between consumers, then variations are perceived as less problematic.143

Capital One’s algorithms were focused exclusively on the information that could be gleaned from visitors’ computers at the moment that they started using Capital One’s website. More advanced technology exists, however, which can combine the up-front data provided by a visitor’s computer, web browser and IP address with larger sources of data that contain historical records of Internet transactions, in-person retail purchases, and e-mail addresses.144 This technology could conceivably enable customer profiling that combines online with offline behavior. It also holds the prospect of eliminating anonymity in Internet transactions. As more data, such as ZIP codes, telephone numbers, birth dates, e-mail addresses, and online social activities, are accessed and used by online advertisers, the accuracy with which companies can place a customer within a segment, or even construct a concrete identity profile, is increased. This capability would expand and refine the ability of companies like Capital One to customize experiences for each consumer.

Question: What is the problem of this case. Does Capital One's have any issued that use customer online profile to clarify their requirement? Can you point out of each problems of this case?

The following table reports the Consumer Pirce Index for the Los Angeles area on a monthly basis from January 1998 to December 2000 (base year=1982-1984). Eliminating the data for 2000, use Excel to forecast the index for all of 2000 using a three-and -six month average. Which provides a better forecast for 2000 using the data provided?