2015 WHARTON BUXTON CHALLENGE

Transcription

1 2015 WHARTON BUXTON CHALLENGE

2 BUXTON S APPROACH WHO WE DEFINE WHO YOUR BEST POTENTIAL CONSUMERS ARE WHERE WE IDENTIFY WHERE YOUR BEST POTENTIAL CONSUMERS ARE FOUND VALUE WE TELL YOU THE VALUE OF YOUR BEST CONSUMERS

3 BUXTON S RELATIONSHIPS

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

5 Count Potential Core Customers Drive-time Trade Area Site Potential View Actual Customers Competition

6 THE BUXTON CHALLENGE

7 OVERVIEW Retail site selection forecasting is one solution provided by Buxton to its clients. Using a retailer s existing locations, Buxton builds econometric models to forecast revenue of potential new locations. You will build one or more econometric 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 statistics class, so you will need to communicate your results appropriately.

8 CLIENT BACKGROUND Client: JoJo s Fro-Yo Dojo (JFD) Category: Frozen Yogurt Shoppe Founded: 2011 Ownership: Corporate Number of Locations: 132 Mission: To Deliver Happiness with a Kick Other Notes: JFD offers frozen yogurt on a Pay-by-the-Pound basis with over 25 different flavors and over 40 different toppings.

9 OBJECTIVE JFD 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 high revenue potential. While their initial strategy was aggressive expansion, they are now focusing on conservative expansion. Your primary objective is to help JFD grow more efficiently by only opening up high revenue potential locations. The real estate team has identified 5 locations of interest: You are to build a sales forecasting model based on the 2014 sales 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.

10 VARIABLE EXPLANATION You are supplied with an exhaustive set of variables for all open stores. These include: Sales for 2014 Site characteristics Competitive pressure Demographic information Customer value Retail and business density (cotenants) A data dictionary is included that will give additional detail 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: department store, grocery store, mall store, or power center. A power center cotenant is a large big box-type store, such as Target, that is often located in strip 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 slide.

11 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. Competitor A Location 10 Min. Drive-Time According to this map, there is one competitor, a clothing store, Competitor A within 10 minutes, but not within 1 mile. So we will observe the following values for the Competitor Count variable: CM_CLOTH_A_1RO = 0 CM_CLOTH_A_10TO = 1

12 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 Demand 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 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 PCT appended to the variable name.

13 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 prep 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 R 2 is 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 store characteristics, that model will not be useful when scoring new potential locations.

14 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 stores. 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 core audience.

15 JUDGING CRITERIA You will have the opportunity to make your presentation for the judges at Wharton. 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: 1. Statistical soundness and theoretical viability of the model 2. Predictive power 3. Real world application 4. Creativity 5. Completeness 6. Clarity of communication

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