Statistical Report – Pastas R US Inc

Statistical Report – Pastas R US Inc

Section 1: Scope and Descriptive Statistics

The Report’s Objective

The company’s marketing department aims at using the existing data in its 74 restaurants across its chain outlets. The information derived from the analysis of data on aspects such as sales per square feet, year to year sales, and average sales per customer will be used to provide an insight into whether opening new restaurants within a 3-mile radius is a profitable venture. For profitability, the company needs to consider its strategic factors, perhaps through an analysis of its strengths and weaknesses. There are several approaches the company can adopt to establish the strategies to focus on in its operations. For instance, Porter’s Value Chain model is a useful tool. According to Porter’s (1995) model, a company can employ a value chain to establish key strategies to adopt for operational success. The model explains that primary activities and support activities operate hand in hand to improve the effectiveness of the company’s operations. Although the company targets expansion through new restaurants, the report’s findings may imply that the company may incur high overhead operational costs, which may lead to losses. This will make the marketing department embark on other strategies, such as improving the existing chain restaurants and adopting advanced marketing measures to increase net revenue. As such, Pastas R US Inc. will come up with a framework of workable and realistic opportunities for improvement, depending on the prevailing circumstances.

The Nature of the Current Database and Variables Analyzed

The database used contains data for 74 chain restaurants in different regions. The database contained age brackets between 25 and 45 years, and the household median income considered was to be above the national average. Regarding education levels, the adult population comprised of at least 15% of college-educated individuals. The variables considered for the analysis were critical for the company’s operations, which included average sales per customer, sales per square feet, year to year percentage sales growth, loyalty card use as a percentage of sales, and customer demographics such as the median age and income. These variables were analyzed descriptively to come up with the simple descriptive statistics.

Summary of Descriptive Statistics

LoyaltyCard(%) versus SalesGrowth(%)

MedAge versus BachDeg%

Section 2: Analysis

Figure 1: BachDeg% versus Sales/SqFt Scatter Plot

Regression Equation= y= 6.669x + 244.03. This implies that the variables are positively related, where an increase in education levels for people leads to an increase in sales per square feet.

Figure 2: MedIncome versus Sales/SqFt Scatterplot

Regression Equation= y= -0.0002x + 431.11. The variables are negatively related. This implies that an increase in median income does not lead to an increase in sales per square feet. The negative value indicates a negative gradient, which is the basis for deriving the relationship (Field, 2018).

Figure 3: MedAge versus Sales/SqFt Scatterplot

Regression Equation= y= -2.2452x + 499.34. Negative Relationship. The negative relationship implies that an increase in the median age does not cause an increase in sales per square feet.

Figure 4: LoyaltyCard(%) versus SalesGrowth(%) Scatterplot

Regression Equation= y= -0.001x + 2.0704. Negative Relationship. The negative relationship indicates that the newly introduced loyalty cards do not lead to increased growth in total sales. This is among the crucial factors the company needs to consider when making the strategic decisions.

Section 3: Recommendations and Implementation

• Based on the above findings, only education levels seem to be positively related to sales per square feet. This implies that the higher the education level, the higher the sales per square feet. It means that an expansion criteria based on education levels could be effective for the company. For instance, targeting areas with intellectuals may increase sales.
• From the analysis, loyalty cards are negatively correlated with sales growth. Since the company target was sales growth through the loyalty card, it is recommendable to change the strategy and adopt an alternative approach to boost sales.
• Market positioning is a crucial strategy in product marketing. The advantage with market targeting is that the company will study a specific consumer group, which enables the company to establish the most appropriate pricing, product promotion, and distribution strategies (Ward, 2020). For instance, the above analysis implies that the company can target regions with young educated millennials to increase its sales. The negative correlation between median age and sales per square implies that an increase in customers’ age will lead to low sales. Therefore, the company needs to target younger people.
• Market research and information gathering are vital for the formulation of the most appropriate market strategies. The consumption habits for the target customer segments is one of the information that can be obtained to evaluate the effectiveness of the above recommendations. For instance, the company can adopt a customer survey to understand consumer habits vis-à-vis the company’s products. The survey can be conducted through an online platform and in its various chain store outlets. Customer feedback will guide the company on the best strategies to adopt.

References

Field A. (2018). Discovering Statistics Using IBM SPSS Statistics, Sage Publishers, California.

Porter, M. E. (1985). The Competitive Advantage: Creating and Sustaining Superior Performance. New York: Free Press

Ward, S. (2020). “Target Marketing and Market Segmentation.” https://www.thebalancesmb.com/target-marketing-2948355

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Statistical Report – Pastas R US Inc

Scenario:

Pastas R Us, Inc. is a fast-casual restaurant chain specializing in noodle-based dishes, soups, and salads. Since its inception, the business development team has favored opening new restaurants in areas (within a 3-mile radius) that satisfy the following demographic conditions:

Median age between 25 – 45 years old
Household median income above the national average
At least 15% of college-educated adult population
Write a 750-word statistical report that includes the following sections:

Section 1: Scope and descriptive statistics
Section 2: Analysis
Section 3: Recommendations and Implementation

Section 1 – Scope and descriptive statistics

State the report’s objective.
Discuss the nature of the current database. What variables were analyzed?
Summarize your descriptive statistics findings from Excel. Use a table and insert appropriate graphs.

Section 2 – Analysis

Using Excel, create scatter plots and display the regression equations for the following pairs of variables:
“BachDeg%” versus “Sales/SqFt”
“MedIncome” versus “Sales/SqFt”
“MedAge” versus “Sales/SqFt”
“LoyaltyCard(%)” versus “SalesGrowth(%)”
In your report, include the scatter plots. For each scatter plot, designate the type of relationship observed (increasing/positive, decreasing/negative, or no relationship) and determine what you can conclude from these relationships.

Section 3: Recommendations and Implementation

Based on your findings above, assess which expansion criteria seem to be more effective. Could any expansion criterion be changed or eliminated? If so, which one and why?
Based on your findings above, does it appear that the Loyalty Card is positively correlated with sales growth? Would you recommend changing this marketing strategy?
Based on your previous findings, recommend marketing positioning that targets a specific demographic. (Hint: Are younger people patronizing the restaurants more than older people?)
Indicate what information should be collected to track and evaluate the effectiveness of your recommendations. How can this data be collected? (Hint: Would you use survey/samples or census?)