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Forte Hotel Conjoint Analysis MGM671 Assignment 5 Ramkumar Iyer Chen-Chung(Phil) Lu Litan Tai Effectiveness evaluation of new hotel chain Forte Hotel, the United Kingdom’s largest hotel chain, is developing a new hotel chain in the United States. Forte decides to invest in this hotel chain partly to take advantage of the increasing numbers of business people traveling from Europe to the United States. In this report, we evaluated several possibilities and tried to find a optimal bounding as a guideline for managements. Data Base on the company’s marketing strategy, the Forte Executive Innes would be mid-priced, around $100 per night. After discussed with our managers, we decided that the survey would ask respondents’ preference on five key attributes: room type, business amenities, leisure facilities, conveniences and extras, and restaurant and dining. Each attribute consists of two to four levels (see Exhibit 1). During the data-collection procedure, we experience the challenge to decide which combination of the attribute options would most appeal to our target audience. Coming to the step of evaluating the products bundles, the products bundles represented to the respondents would it is unreasonable to expect a respondent to evaluate every possible combination. We used factorial designs to reduce the number of products we ask respondents to evaluate. We tried to select orthogonal combinations of attribute levels to reduce the number of product bundles respondents must evaluate and to permit one to measure the independent contribution of each attribute to the utility function. Next, we asked the respondent to evaluate each product on a rating scale (e.g. on a scale of 0 of 100), with larger numbers indicating greater preference. There were 300 business travelers participated in our study. However, our data was obtained from 40 of the respondents, which we believed were our target customers. (The mean ratings of the attribute weights were showed in Table 1) Analysis After we got the suitable data, we draw on ideas in hybrid conjoint and adaptive conjoint analysis to obtain the part-worth functions. Our computer software adopted dummy variable regression to compute the part-worth functions (As showed in Appendix 1). With the part-worth functions, we could easily calculate the utility function to each product bundle and get the part-worth utility function. We believe that our target customer would make their purchase decision by their utility scores to each brand name hotel. Base on this assumption, we could not only evaluate the original 16 hotel profiles but four pre-targeted hotel profiles: Deluxe, Profession 1, Professional 2, and Tourist.
Approximate Word count = 1587
Approximate Pages = 6.3
(250 words per page double spaced)
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