In 2014, the owner of a food truck based in Hamilton, Ontario, was looking over the first year of her operations. In addition to working in Hamilton, she had tried to maximize her revenues by driving to several other cities and charging various prices for each burger, depending partly on the fresh ingredients available in each city. Besides location, the owner had collected data on a few other factors—the weather, the day of the week, the city’s population, and whether a festival was going on—that had had an impact on the demand for her product. She wondered whether analytics could help her decide where to sell and how much to charge on a daily basis. The owner also wondered whether this decision-making and data-collection process could be automated since she would be using it every day.
In 2014, the owner of a food truck based in Hamilton, Ontario, was looking over the first year of her operations. In addition to working in Hamilton, she had tried to maximize her revenues by driving to several other cities and charging various prices for each burger, depending partly on the fresh ingredients available in each city. Besides location, the owner had collected data on a few other factors-the weather, the day of the week, the city's population, and whether a festival was going on-that had had an impact on the demand for her product. She wondered whether analytics could help her decide where to sell and how much to charge on a daily basis. The owner also wondered whether this decision-making and data-collection process could be automated since she would be using it every day.