CASE ANALYSIS REPORT on LaQuinta Inn
Sullivan University
MGT 620
By Anusha Lingareddy
Which of the three success measures is appropriate? (Use both intuitive and data-driven arguments.)
The three measures which the site selection committee agreed to be appropriate measures for a good hotel are – occupancy, profit and operating margin. A measure of occupancy may not provide accurate results – as the number of rooms sold does not consider the prices at which the rooms were sold. Profit measure, as a number, holds no value as it does not measure the revenue of a company as a whole and the part of each location in earning profits. Intuitively, operating margin seems like an appropriate measure of the success of an Inn location. Operating margin considers factors like depreciation, operating costs and company’s revenue.
My findings can be backed up by the data provided for Inn number 17, occupancy rates for year 83 was lower than the occupancy rate for year 86 – while profits earned in 83 was higher than 86. Though the difference in profits earned in 83($634) vs. profits earned in year 86($490) was not high – it is observed that the Inn earns an operating margin of 51% in 83 vs. an operating margin of 15% in the year 86. Hence suggesting the operating margin to be the most appropriate of the success measure.
Are the variables considered (Table 16.16) appropriate for the decision at hand? What other variables might you want to consider?
The decision at hand is to determine an appropriate location for expansion in the Dallas market, which is described as a growing business town. Some of the variables considered for the decision at hand seem implausible – MALLS which takes into account the square footage of malls, RETAIL which measures the scale ranking of retail activity and TOURISTS which counts annual tourists. The above variables would be effective in targeting leisure travelers than a business traveler. Since the target location is a business town, variables like distance to convention centers, distance to restaurants or number of restaurants in the area will be some useful variables to consider.
Determine appropriate predictors of operating margin through correlation and regression analysis. Comment on the variables both in and not in your predictive model.
My model consists of the following independent variables –
- RATE – This variable is important as customers always look for the cheapest available option and check for competitors prices for a better deal.
- RMS1 –
- RMSTOTAL – this variable provides a measure of other hotels and inns nearby and indicate demand for hotels in the area.
- COLLEGE – Colleges attract customers – parents visiting children, events at a college attract people from other cities, etc.,.
- OFC1 – Business travelers prefer to stay at a hotel closer to their office to avoid longer commutes.
- PASSENGR – More number of people traveling to the airport suggests that airport travelers prefer this inn location.
- DISTCBD – Distance to downtown is always a favorable measure as most businesses are based in the downtown.
- ACCESS – Accessibility and ease of transportation is an important measure in measuring the success of a hotel.
- ARTERY – Access to a major road provides easy accessibility to major freeways.
- TRAFFIC – Heavy traffic may be an unfavorable factor for some while it suggests that the location is highly populated and offers many options for dining and proximity to major attractions.
- EMPLYPCT – A low unemployment area suggests a safe neighborhood and an abundance of businesses.
- NEAREST – Closeness to other LaQuinta Inns can either be seen as an advantage for marketing and management or a disadvantage if the demand for hotel rooms in that area is low. This factor helps one decide if a LaQuinta Inn should be located in the vicinity or away from another LaQuinta inn.
- LGTIND – This variable is used as a measure of business conducted in the area
- HOSPTOTL – Proximity to a hospital provides an assurance to customers and also attract doctors who are visiting the hospital.
- INCOME – Areas of high average income are usually located in a better neighborhood and acts as a positive factor for customers.
- MILITARY – Staying close to military area acts as an assurance for some people.
Variables not in my model:
- PRICE – Since we are considering the competitive rate in the area, price will be a redundant variable.
- ROOMSINN – considering the number of rooms in the inn does not make a significant impact on attracting customers.
- SIGNVIS – Sign visibility also plays a minimal role as most people make reservations online and are not aware of this factor.
- MALLS – Square footage of the mall does not matter as most tourists travel for business.
- RETAIL – Scale ranking of retail activity does not effect a travel plan.
How should your model be used in site selection?
My model can be used to calculate operating margins for the years ’83 and ’86. Based on the results of operating margins – the most and least probable areas for the site should be identified. The regression model should not be the sole factor in making a decision for site selection. Results from regression analysis may be used for eliminating locations and gaining further knowledge on future trends and markets.
Make recommendations concerning the Dallas expansion.
Using my regression model, Fair Park has proved to be the best location for the year 1983 and Coppell has performed better in the year 1986. It is imperative to consider the data from both years as it is difficult to predict economical situations for all years to come. In order to decide on one of these locations, I would recommend that they research on more data from past years.
References:
Kimes, S., Fitzsimmons, J., (1990). Selecting profitable hotel suites at La Quinta motor inns. Interfaces, 20, 2, 12 – 20.
Palrmer, A ( December, 2010). Case Study: La Quinta narrows target to boost guest loyalty. Direct marketing. Retrieved from http://www.dmnews.com/email-marketing/case-study-la-quinta-narrows-target-to-boost-guest-loyalty/article/191187/