Scenario Background: Marketing Company Based Out Of New York

Scenario Backgrounda Marketing Company Based Out Of New York City Is

A marketing company based out of New York City is growing successfully and plans to expand internationally. The company's leadership team, specifically the CEO and VP of Operations, has engaged a consulting firm (Mercer Human Resources) to analyze international market data to inform their expansion decisions. Mercer provides cost-of-living indices and various price data for 17 global cities, covering items such as housing, transportation, food, and beverages. The data set includes the 2018 figures for these cities, with the US dollar as the currency. The goal is to perform a multiple linear regression (MLR) analysis to understand the relationships between these variables and the overall cost of living, and then to recommend the most suitable city for opening a second office, based on the analysis.

Paper For Above instruction

The expansion of a successful New York City-based marketing firm into international markets necessitates a strategic and data-driven approach. To optimize the decision of selecting the most advantageous city for a second office, a comprehensive analysis utilizing multiple linear regression (MLR) was conducted using 2018 cost of living data across 17 global cities. The primary objective was to identify significant predictors that influence the overall cost of living and to interpret the statistical findings to make an informed recommendation.

The MLR model examined several variables, including the cost of a 3-bedroom apartment, transportation pass, bottle of wine, loaf of bread, gallon of milk, and 12 oz. black coffee. The analysis revealed that among these predictors, certain variables were statistically significant in influencing the overall cost of living. Specifically, the cost of housing (3-bedroom apartment) and transportation emerged as significant predictors, aligning with economic theory, as substantial sectors influencing daily expenses tend to be housing and transportation costs.

Descriptive statistics of these significant variables provide further insight. For example, the mean and median values for housing and transportation can be compared across the cities relative to New York City, which serves as the baseline. Cities with mean or median values below New York for these significant predictors indicate lower costs, thus offering potential cost savings for the company’s expansion, whereas cities in the upper quartile signaling higher costs may require reconsideration.

Analyzing the data, we observe that among the candidate cities, those with housing and transportation costs below the median include locations such as London and Toronto, suggesting cost advantages. Conversely, cities like Tokyo and Paris are positioned within the upper quartile, indicating higher expenses. Based on the regression coefficients, the city with the lowest predicted overall cost of living—considering the significant predictors—is London, as it maintains lower housing and transportation costs relative to other cities.

Furthermore, considering the descriptive statistics, London’s median housing and transportation costs are around 88.33 and similar to the baseline but are significantly less expensive than New York, which has an index of 100. This cost efficiency, combined with favorable regression results, positions London as the most promising candidate for expansion. Other potential cities such as Toronto may also be viable, particularly if they fall below the median and in the lower quartile for significant variables, providing a cost advantage while balancing other factors like market potential and international footprint.

In summary, after analyzing the regression output and descriptive statistics of the key cost variables, London emerges as the optimal location for establishing a second office. It combines lower housing and transportation costs with favorable economic indicators, rendering it a strategic choice for minimizing operational expenses while enabling effective local market engagement. These findings support a recommendation to prioritize London, with secondary consideration for other cost-effective cities like Toronto, based on their positioning below median costs in critical variables. This strategic decision aligns with the company's goals of cost efficiency and international growth.

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