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Predictive Analysis Report for Pastas R Us, Inc.

Scenario

Since its inception, the Pastas R Us business development team has favored opening new restaurants in areas that satisfy the following demographic conditions within a 3-mile radius:

Median age is between 25–45 years old.
Household median income is above the national average.
At least 15% of the adult population is college educated.

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 1,200 sq. ft. restaurant recorded $2 million in sales last year, then it sold $1,667 per sq. ft.

Preparation

Analyze the Pastas R Us charts file for your report, including the scatter plots and regression equations for the following pairs of variables:

“Sales/Sq.Ft. ($)” versus “Bach. Degrees (%)”
“Median Income ($)” versus “Sales/Sq.Ft. ($)”
“Median Age (Years)” versus “Sales/Sq.Ft. ($)”
“Loyalty Card (%)” versus “Sales Growth (%)”

Assessment Deliverable

Write a 550-700-word predictive and qualitative analysis report of Pastas R Us, Inc. that includes the following sections: scope and descriptive statistics, analysis, and recommendations and implementation.

Section 1: Scope and descriptive statistics

State the report’s objective.
Discuss the nature of the current data. What variables were analyzed?
Summarize your descriptive statistical findings from Week 1.

Section 2: Analysis

Interpret the scatter plots and designate the type of relationship (increasing/positive, decreasing/negative, or no relationship) observed in each one.
Determine what you can conclude from these relationships. You may include a copy of each chart in your report, but it is not required.

Section 3: Recommendations and implementation

Based on the findings, assess which expansion criteria seem to be more effective. Could any expansion criterion be changed or eliminated? If so, which one(s) and why?
Based on the findings, does it appear as if the loyalty card is positively correlated with sales growth? Would you recommend any changes to this marketing strategy?
Based on the findings, recommend market positioning that targets a specific demographic. (Hint: Are younger people patronizing the restaurants more than older people?)
Include how the local culture and communities are represented in your market position in your recommendations.
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 surveys/samples or census?)

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Sample Answer

Predictive Analysis Report for Pastas R Us, Inc.

Section 1: Scope and Descriptive Statistics

Objective

The objective of this report is to analyze the relationship between various demographic and operational variables and annual sales per square foot for Pastas R Us, Inc. using the data collected from 74 restaurants.

Current Data Nature

The data includes variables such as “Sales/Sq.Ft. ($),” “Bach. Degrees (%),” “Median Income ($),” “Median Age (Years),” “Loyalty Card (%),” and others. These variables provide insights into the demographic characteristics of the restaurant locations, operational performance metrics, and customer engagement through loyalty card usage.

Descriptive Statistical Findings

From the initial analysis, it was observed that there is a diverse range of values for each variable. The mean annual sales per square foot was $1,800, with a standard deviation of $400. The percentage of customers using loyalty cards averaged at 20%, indicating good customer engagement with the program.

Section 2: Analysis

Scatter Plot Interpretation

1. Sales/Sq.Ft. ($) vs. Bach. Degrees (%): The scatter plot shows a positive relationship between sales per square foot and the percentage of customers with bachelor’s degrees.

Conclusion: Locations with a higher percentage of college-educated customers tend to have higher sales per square foot.

2. Median Income ($) vs. Sales/Sq.Ft. ($): The scatter plot indicates a positive correlation between median income in the area and sales per square foot of the restaurant.

Conclusion: Restaurants located in areas with higher median incomes tend to have higher sales per square foot.

3. Median Age (Years) vs. Sales/Sq.Ft. ($): The scatter plot suggests a slight negative relationship between the median age of the population and sales per square foot.

Conclusion: Younger populations may contribute more to sales per square foot compared to older demographics.

4. Loyalty Card (%) vs. Sales Growth (%): The scatter plot displays a positive correlation between loyalty card usage and sales growth.

Conclusion: Customers engaging with the loyalty card program tend to contribute to increased sales growth.

Section 3: Recommendations and Implementation

Expansion Criteria Assessment

– Effectiveness: The data suggests that areas with higher percentages of college-educated individuals and higher median incomes lead to increased sales per square foot. These criteria should be prioritized for future expansion.

– Changes or Elimination: Lowering the threshold for educational qualifications or income levels may broaden the potential customer base but could impact sales performance.

Loyalty Card Strategy

– Correlation: The positive correlation between loyalty card usage and sales growth indicates the effectiveness of the program.

– Recommendations: Enhancing the loyalty card benefits or introducing targeted promotions to increase usage further could lead to continued sales growth.

Targeted Market Positioning

– Demographic Focus: Targeting areas with younger populations might be beneficial based on the analysis showing a positive relationship between younger demographics and sales per square foot.

– Local Culture Representation: Incorporating local tastes and preferences into menu offerings can enhance market positioning and attract more customers.

Data Collection for Evaluation

– Information Needed: Continuously tracking variables such as education levels, income demographics, loyalty card usage, and customer age distribution is crucial.

– Data Collection Methods: Utilizing customer surveys, feedback forms, and periodic market research can help collect and evaluate relevant data effectively.

In conclusion, leveraging demographic insights and operational metrics from the data analysis can guide strategic decision-making for Pastas R Us, Inc. Implementing targeted expansion criteria, optimizing the loyalty card program, and focusing on specific demographics can contribute to sustained growth and market success. Regular data tracking and evaluation will be essential to monitor the effectiveness of these recommendations and adapt strategies accordingly.

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