Descriptive Statistics for Pastas Inc

Descriptive Statistics for Pastas Inc

Section One: Scope and Descriptive Statistics

The Report’s Objective

The main aim of the current report is to analyze the operational variables for Pastas Inc. restaurant to suggest a viable and effective framework that the restaurant can adopt in its expansion strategy based on the available data. Some data variables used in the analysis include sales per customer, sales per square feet, yearly percentage sales growth, loyalty card use as a percentage of sales, and customer demographic information such as the median age and income. Particularly, Pastas Inc. wants to determine whether operations of the restaurant within a 3-mile radius will be a profitable venture for the business. The findings from the analysis will guide the company’s management in making a decision about expansion criteria and the opportunities that would be associated with the expansion strategy.

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The Nature of the Current Database and the Variables that were Analyzed

The database contains data for Pastas Inc. from its 74 restaurant branches situated in different locations. The median data used was higher than the national average. The database also contained client demographics between 25 years and 45 years of age, with 15% of adult individuals who attained a college level of education. The analyzed variables were Percentage Sales Growth, Loyalty Card use in percentage, Sales per Square Foot, Median Income, Median Age, and education level in Bachelor’s Degree. The data was mainly analyzed quantitatively and visually presented using scatter plots.

Summary of the Descriptive Statistics Findings from Excel Using a Table

	SalesGrowth%	LoyaltyCard%	Sales/SqFt	MedIncome	Sales/SqFt	MedAge	BachDeg%
Mean	7.414054054	2.026486486	420.305405	62807.7027	420.305405	35.2014	26.3108108
Standard Error	0.770109257	0.064211803	15.9537705	2081.32945	15.9537705	0.42483	0.8142851
Median	7.03	2.075	396.01	62757	396.01	35	26.5
Mode	4.05	2.04	#N/A	#N/A	#N/A	34.8	29
Standard Deviation	6.624730322	0.552370812	137.239523	17904.273	137.239523	3.65455	7.00474531
Sample Variance	43.88705183	0.305113514	18834.6868	320562990	18834.6868	13.3558	49.0664569
Minimum	-8.31	0.29	178.56	32929	178.56	24.7	14
Maximum	28.81	3.38	987.12	114353	987.12	43.5	40
Sum	548.64	149.96	31102.6	4647770	31102.6	2604.9	1947
Count	74	74	74	74	74	74	74
Confidence Level(95.0%)	1.534825536	0.127973938	31.7958188	4148.08362	31.7958188	0.84669	1.62286787

Section Two: Analysis

Scatter Plots and Regression Equations for the Various Pairs of Variables:

“BachDeg%” versus “Sales/SqFt” Scatter Plot

The regression equation of the scatter plot shows that there is a positive relationship between the level of education and sales per square foot (Glen, n.d). This shows that the higher the education level, the higher the sales.

“MedIncome” versus “Sales/SqFt” Scatter Plot

The regression equation indicates that there is a negative relationship between the variables. This shows that an increase in the median income does not lead to increased sales per square foot.

“MedAge” versus “Sales/SqFt” Scatter Plot

The equation shows that the variables have a negative relationship. The negative relationship indicates that an increase in median age leads to a decrease in sales growth rates.

“LoyaltyCard(%)” versus “SalesGrowth(%)” Scatter Plot

For loyalty cards and sales growth, the relationship between the variables is negative. The negative relationship between the variables indicates that loyalty cards do not increase the rate of sales growth. In fact, sales growth is reducing with an increasing number of loyalty cards.

Section Three: Recommendations and Implementation

The Effective Expansion Criteria

Based on the findings, the restaurant management can expand its restaurant services to areas with more educated populations since the higher the level of education, the higher the sales per square foot. This is evidenced by the positive relationship between education and sales per square foot. Loyalty cards can also be eliminated.

Analyzing whether the Loyalty Card is Positively Correlated with Sales Growth

Due to the negative relationship between the sales per square foot and the loyalty cards, the management should change the marketing strategy. This is because loyalty cards did not yield the expected results, thus, necessitating a change of strategy.

The Recommended Marketing Positioning Targeting a Specific Demographic

As Ward (2020) states, segmentation based on demographic factors is a very crucial factor when determining the location of a business. The demographic segments that seem promising in terms of purchases should be prioritized. In this case, the management should target younger and educated populations since they seem to patronize the restaurants more than the older people.

Collecting Information for Tracking and Evaluating the Effectiveness of These Recommendations

Market research requires the management to employ the most appropriate and suitable method to yield the most reliable information for decision-making. Therefore, surveying is the best and most convenient method for conducting market research. Data analytics could also be a useful data collection strategy for obtaining crucial information about the target audience.

References

Glen, S. (n.d). Linear Regression: Simple Steps, Video. Find Equation, Coefficient, Slope. From StatisticsHowTo.com: https://www.statisticshowto.com/probability-and-statistics/regression-analysis/find-a-linear-regression-equation/

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

Resources: Pastas R Us, Inc. Database & Microsoft Excel®, Wk 1: Descriptive Statistics Analysis Assignment

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Purpose

This assignment is intended to help you learn how to apply statistical methods when analyzing operational data, evaluating the performance of current marketing strategies, and recommending actionable business decisions. This is an opportunity to build critical thinking and problem-solving skills within the context of data analysis and interpretation. You’ll gain a first-hand understanding of how data analytics supports decision-making and adds value to an organization.

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 favoured 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

Last year, the marketing department rolled out a Loyalty Card strategy to increase sales. Under this program, customers present their Loyalty Card when paying for their orders and receive some free food after making 10 purchases.

The company has collected data from its 74 restaurants to track important variables such as average sales per customer, year-on-year sales growth, sales per sq. ft., Loyalty Card usage as a percentage of sales, and others. A key metric of financial performance in the restaurant industry is annual sales per sq. ft. For example, if a 1200 sq. ft. restaurant recorded $2 million in sales last year, then it sold $1,667 per sq. ft.

Executive management wants to know whether the current expansion criteria can be improved. They want to evaluate the effectiveness of the Loyalty Card marketing strategy and identify feasible, actionable opportunities for improvement. As a member of the analytics department, you’ve been assigned the responsibility of conducting a thorough statistical analysis of the company’s available database to answer executive management’s questions.

Report:

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 as if 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?)

Cite references to support your assignment.

Format your citations according to APA guidelines.

Submit your assignment.

Resources

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