Unbiased Hospitality Forecasting

Using Cheque level historical data from 20 locations for a 3 year period, the objective is to build a weekly forecast that can be viewed at 15 minute increments across a variety of on premise and off premise channels. Influencing factors such as (but not limited to) weather, local demographics, and marketing initiatives need to be considered. The project is divided into 2 key areas: 1) Understanding what the influential factors are and what degree each impacts the overall results 2) Building a model that can project sales based on the variable factors and test using historical results.

What is the right level of service for Lone Star

The objective is to complete a service audit of at least 2 locations, with one being our Milton location. Milton has recently launched an augmented service model using technology to entertain our guests and enhance their experience by allowing them to order, reorder and request service. The intent would be to get an unbiased comparison of service with and without this technology. This comparison would include (but not be limited to) speed of service, atmosphere, level of interaction from staff and managers, premises review and overall impression. The project would also include using the locations guest WiFi and engaging in "The Sizzle" Loyalty Program.

Mapping Future Business (Channels)

Business Challenge: Take a casual concept and make it attractive to a convenience biased consumer using a cost effective delivery model. While there are numerous sources of data for demographics, population density, drive times and traffic patterns, little exists to pull this together, especially in real time. The objective is threefold: 1) Determine the factors that define the best location for off-premise sales. 2) Create a model that can test a location at a specific time for fit with respect to the determined model. 3) Use the model to determine best locations to build new sites for a brand.

Unbiased Hospitality Forecasting

Using Cheque level historical data from 20 locations for a 3 year period, the objective is to build a weekly forecast that can be viewed at 15 minute increments across a variety of on premise and off premise channels. Influencing factors such as (but not limited to) weather, local demographics, and marketing initiatives need to be considered. The project is divided into 2 key areas: 1) Understanding what the influential factors are and what degree each impacts the overall results 2) Building a model that can project sales based on the variable factors and test using historical results.