1. Spring 2018
Buxton Challenge

2. WHO WHERE VALUE Buxton’s Approach
We define WHO your best potential customers are
WHO
We identify WHERE your best potential customers are found
WHERE
We tell you the VALUE of your best customers
VALUE

3. Example Solutions Retail Restaurant Healthcare Public Sector
Private Equity
Location Sales Models
New Site Prioritization
Franchise Territory Alignment
Existing Store Performance Management
Relocations
Market Optimization
US Potentials
Marketing Services
Customer Activity Dashboarding
Revenue Forecasting
New Concept Benchmarking
Competitive Impact Studies
Prospect Marketing
Omni-Channel Modeling
Physician Network Analysis
Service Line Model
Medical Office Building Analysis
Patient Retention
Distribution Center Analysis
Merchandise Optimization
Store Closure Analysis
Due Diligence Analysis
Canadian Analytics
Retail Tenant Matching
Tourism Analytics
Staffing Optimization
Market Share Analysis and Benchmarking
Customer Churn Analysis
Real Estate
Operations
Merchandising
Marketing

4. Buxton’s Relationships
This has retail, restaurant, healthcare and public sector client logos all represented on one slide

5. THE BUXTON
CHALLENGE

6. Overview
Retail site selection is one solution provided by Buxton to its clients. Using a retailer’s existing locations, Buxton builds statistical models to forecast revenue of potential new locations.
You will build one or more statistical models using historical performance and data describing the characteristics of the area around the client’s locations from which they draw their customers (the trade area).
You are expected to summarize your results in a presentation for the client’s board of directors. None of them have taken a Stats class, so you will need to communicate your results appropriately. 6

7. Client Background Client: Subs by Tubbs
Category: Quick-service restaurant serving sandwiches
Number of Locations: 330
Mission: Provide the best-tasting sandwiches in the world
Notes: Subs by Tubbs was founded by notable UNT alum Cameron Tubbs. His mission was to provide a great tasting sandwich at a reasonable price.

8. Objective
Subs by Tubbs has experienced rapid growth in their key markets. Although they’ve been successful with their expansion efforts, executive leadership is running out of “Easy Win” locations and needs help identifying areas with strong revenue potential. While their initial strategy was aggressive expansion, they are now focusing on conservative expansion.
Your primary objective is to help Subs by Tubbs grow more efficiently by only opening up high revenue potential locations.
The real estate team has identified 20 locations of interest that must be analyzed to determine if a Subs by Tubbs should be opened there.
You are to build a sales forecasting model based on sales in 2017 to predict the future performance of these locations. Your forecasts as well as any other factors or data points should drive your recommendation for which sites to pursue.

9. Variable Explanation
You are supplied with an exhaustive set of variables for all open stores. These include…
Sales for 2017
Restaurant characteristics
Competitive presence
Demographic information
Estimated customer value
Retail and business density (cotenants)
A data dictionary is included that will give additional details on each variable.
Cotenant counts are some of the business proximity variables provided. Cotenants are large retail stores or businesses that act as an area draw and bring in customers to nearby stores. The cotenants are assigned a type: convenience stores, grocery stores, mall-type stores, movie theaters, pharmacies, or power center. A power center is a large “big box”-type store (like Target or Best Buy) that is often the anchor for retail centers.
Because of the spatial nature of much of the data, it is necessary to specify some geography associated with these variables. An example of spatially-generated data is given on the following page.

10. Spatially-Generated Variables
Spatially-generated variables use the following naming convention:
VARIABLE_NAME_XTO
Where X is the number of drive-time minutes from the store.
VARIABLE_NAME_XRO
Where X is the number of radial miles from the store.
According to this map, there is a discount department store, Competitor “A”, within 10 minutes, but not within 1 mile.
So we will observe the following values for the Competitor Count variable:
CM_COMP_A_1RO = 0
CM_COMP_A_10TO = 1
Competitor A
Location
10 Min. Drive-Time
1 Mile Radius

11. Variable Explanation (cont.)
Distance Score – Measures the count and proximity of a given cotenant or competitor within 1 mile. The farther the business is from the location, the smaller the value.
Customer Value – Measures the estimated value of households near a given location. This is evaluated at the residential level as well as at the workplace. Takes distance and household segmentation into consideration.
Qualitative Variables – Some variables provided are a measure of the percent rather than count. These use the same naming convention as the other variables but will have “X” as a prefix to the variable name.

12. Model Tips
There are many variables from which to choose, but many of these are simply the same variable at different geographies. It may be easier to start by focusing on a few theory-derived functional forms.
Perform pre-model preparation – In spirit of the “Garbage In, Garbage Out” adage, it is important that you perform hygiene and prepare your data for modeling. There are a multitude of ways to go about this. Focus on ensuring that what goes into your model is an appropriate representation of what you’re looking to predict.
Focus first on the theory – if the variables in a model do not make sense it does not matter if the R2 is 0.98.
Consider the ultimate goal of your model – forecasting revenue potential at new locations that have not yet been built. While a model may be able to explain most of the variation in sales with restaurant characteristics, that model will not be useful when scoring new potential locations.

13. Presentation & Summary Paper
Your final presentation should contain the following information:
Explanation of methodology used in the analysis, including pre-model build, model build, and site scoring.
Explanation of variables used in the model, and any relevant statistics you feel are important.
For your final model choice, you must also provide the forecasts (fitted values) for the potential locations.
You must also submit an Excel file containing the model statistics and forecasts/fitted values for all locations (a template will be provided for you to use).
You must submit a summary of the model variables and model results (approximately 2 pages). Use this summary to explain anything you deem important, yet too technical for the presentation audience.

14. Judging Criteria
You will have the opportunity to make your presentation for the judges. While there is no penalty for not presenting, the judges will provide you with feedback that you can use to improve your model and presentation before submitting it to the entire judging panel.
Judging Criteria:
Statistical soundness and theoretical viability of the model
Predictive power
Real world application
Creativity
Completeness
Clarity of communication