Understanding Franchise 

Performance

 Your boss wants your help to understand what drives the performance of stores of the Subway franchise. Your boss helps you acquire data from last year’s sales and the demographic factors of each store:

SALES

SQFT

INVENTORY

ADVERTISING

FAMILIES

STORES 

SCHOOL

The EXCEL data file (Franchises.xlxs) has a data dictionary describing the meaning of the variables.

Using Simple Linear Regression 

Models to Understand Franchise Performance

u Use the dataset in Franchises.xlxs.

u Build a model to establish which individual factors affect sales in our franchises and how? 

u Can we create a model to help design a store and achieve a certain level of sales? 

u Which factors are critical to the design of the store: area (sq ft), inventory, advertising spend, size of the sales district, number of competing stores, schools near by? 

Using SLR Analysis to Understand 

Franchise Performance

u Examine the Bivariate Relationships. Use linear regression to build models to answer these questions.

u Use the data to determine the relationship between SALES and FAMILIES (the size of the customer base around each store).

u How is the size of the customer base around each store related to our expected sales?

u Use the data to determine the relationship between SALES and SQFT (the area each store occupies).

u Use the data to determine the relationship between SALES and INVENTORY (the number of items in each store).

u Use the data to determine the relationship between SALES and ADVERTISING (the amount of money each store spends on promotion).

u Use the data to determine the relationship between SALES and STORES (the number of competing stores near by each store in our franchise).

u Use the data to determine the relationship between SALES and SCHOOL (whether there is school near by).

Using SLR Analysis to Understand Franchise Performance

u Perform the analysis and then give your answer to the questions posted above, with you reason for the answers. 

u The EXCEL data file has a data dictionary describing the meaning of the variables. 

Using Multivariate Regression 

Models to Understand Franchise Performance

u Use the same dataset in Franchises.xlxs.

u Build a model to answer the questions:

u Which collection of factors affect sales in our franchises and how?

u Can we create a model to design a store given certain demographics so we can achieve a certain level of sales?

u Examine each of the variables and see if any variables need to be  transformed to improve the multi-variate model.

u Build a multivariate regression model that can be used to predict store sales

Prediction Analysis 

u Use the model to do some analysis.

u We just opened a store in a neighborhood with 5,000 families, the store is 5,000 sq ft, we are planning to spend $5,000 a month in advertising, carry $250,000 in inventory, there are 5 competing stores in the neighborhood and there are schools near by.

u What are the projected sales?

Project Report Guidelines

u Include tables and visuals to support your analysis.

Your project report should.

u You can use the following outline for your project report:

1. Introduction: introduce the problem, provide some background

2. Problem Statement: describe the problem you are proposing to solve

u State the assumptions (if any)

u Data Sources: list the sources of data you expect to extract data for developing the analysis.

3. Data Description: describe the data (explain any terms that are specific to your dataset Project Report Guidelines (cont.)

u Outline (cont.)

4. Methods: discuss your modelling approach

u What are the independent variables (co-variates)?

u What is the dependent variable?

u Provide as much detail about the various models you have in your study.

u Did you need to transform any variables?

u How well do the variables fit the model?

u How well do the variables describe the model?

5. Results: what is the outcome of your analysis across the different models?

6. Conclusion: Do you have a recommendation?

franchises-