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Skyrose Marketing Agency: Predicting Consumer Demand
內容大綱
As a result of recent success and rapid growth, the Skyrose Marketing Agency team was becoming overwhelmed with significant variation in workload levels. The vice-president, who was responsible for managing the company’s clients from the beverage industry, wanted to smooth the team’s workload level to improve morale. She also wanted to remain attentive to her clients’ needs, which could increase as peak holiday seasons approached. The vice-president was considering using Google Trends to predict how popular certain beverage products would be in the future so that her beverage company clients could predict future sales volumes. She hoped that by forecasting proxy sales for three specific clients she could gain key insight to help her smooth the volume levels of her team’s workload, without having a negative impact on her client relationships.
學習目標
This case is suitable for undergraduate- and graduate-level courses that cover topics in forecasting and predictive analytics, specifically time series modelling. Students with prior knowledge of the R programming language will be able to greatly simplify their analysis, although calculations can also be performed using different tools, such as Python. This case provides students with the opportunity to put their descriptive and predictive analytics skills to practice. After working through the exercise and assignment questions, students will be able to<ul><li>interpret descriptive analytics output, such as data visualizations and summary statistics;</li><li>use R programming language to apply time series linear modelling techniques such as linear regression with trend and seasonality, Holt-Winters method, and autoregressive integrated moving average;</li><li>evaluate forecasting techniques; and</li><li>develop forecasts based on time series modelling.</li></ul>