Jоhn Tetzel, infuriаted Luther by selling whаt, tо the pоint thаt Luther drafted his "Ninety-five Theses?"
An eаrly descriptiоn оf the Gоspels comes from the writings of Justin Mаrtyr in AD 155.
Blue Sky Airlines, а mаjоr Nоrth Americаn airline, wants tо develop an analytics-driven approach to this year’s seasonal marketing campaign. With a diverse customer base, the airline’s strategy involves developing a more customized approach to its marketing efforts during the busy holiday season. Blue Sky’s analytics team was asked to recommend targeted holiday promotions, tailored specifically to different customer segments identified within their customer base. The analytics plan developed for this initiative involved two key phases: 1. Customer Segmentation: In the first phase, Blue Sky Airlines undertook a comprehensive data collection exercise. The analyst team gathered detailed data on customer travel patterns, preferences, and demographics. The analysts then performed a k-means cluster analysis on the data set. The data description and k-means output are shown on the following pages. 2. Development of Targeted Promotional Strategies: During last year’s holiday season, four different promotions were run periodically. These promotions were not targeted to any particular customers or customer group; they were cycled through on a weekly basis and advertised on Blue Sky’s website. 1. Off-Peak Discount: Promotion offers reduced fares for flights scheduled during less busy travel times. 2. Double Loyalty Points: Travelers can earn double the usual points on their loyalty program for each flight booked during the promotional period. 3. VIP Lounge Access: Passengers receive complimentary access to the VIP lounge when they purchase qualifying tickets. 4. Early Booking Discount: A discount is provided to customers who book their flights more than 21 days in advance of their travel date. For this year’s targeting strategy, the analysts used the k-means model from phase one to classify last year’s holiday travelers, and then performed a regression analysis to estimate the effect of customer segment and promotion type, as well as the interaction of those variables, on the success of the promotion campaign. As the newest member of the Blue Sky analyst team, you have been asked to summarize the results of the analysis for an upcoming executive meeting. The management team consists of strategic thinkers who are non-technical but highly business focused. The managers are interested in understanding the customer segments identified by the analysis, as well as seeing recommendations for which promotions will be most effective for each segment. The data for the cluster analysis contained 6 variables relating to the passenger who made the flight reservation. The columns in the data file are described in the table below. Essay_F_1.jpg Essay_F_2.jpg The data for the regression analysis contained 2 independent variables: Cluster (from the k-means model above) and Promotion (described in the case scenario on previous page). The dependent variable was Promotion Response Rate (PRR), which measures the percentage of customers who respond to a promotional offer. It is calculated by dividing the number of customers who take action on a promotion (e.g., booking a flight, upgrading a service) by the total number of customers who received or were exposed to the promotion. PRR is a key metric that Blue Sky uses to evaluate the effectiveness of their marketing campaigns. The average PRR for all promotions run in the past year was 15.79%. Essay_F_3.jpg Essay_F_4.jpg