Laptop Sales at a London Computer Chain: Interactive Visualization. The next exercises are designed for using an

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Laptop Sales at a London Computer Chain: Interactive Visualization. The next exercises are designed for using an interactive visualization tool. The file LaptopSales.csv is a comma-separated file with nearly 300,000 rows. ENBIS (the European Network for Business and Industrial Statistics) provided these data as part of a contest organized in the fall of 2009. Scenario: Imagine that you are a new analyst for a company called Acell (a company selling laptops). You have been provided with data about products and sales. You need to help the company with their business goal of planning a product strategy and pricing policies that will maximize Acell’s projected revenues in 2009. Using an interactive visualization tool, answer the following questions.

a. Price Questions:

i. At what price are the laptops actually selling?

ii. Does price change with time?

iii. Are prices consistent across retail outlets?

iv. How does price change with configuration?

b. Location Questions:

i. Where are the stores and customers located?

ii. Which stores are selling the most?

iii. How far would customers travel to buy a laptop?

◦ Hint 1: You should be able to aggregate the data, for example, plot the sum or average of the prices.

◦ Hint 2: Use the coordinated highlighting between multiple visualizations in the same page, for example, selecta store in one view to see the matching customers in another visualization.

◦ Hint 3: Explore the use of filters to see differences. Make sure to filter in the zoomed out view. For example, try to use a “store location” slider as an alternative way to dynamically compare store locations. This might be more useful to spot outlier patterns if there were 50 store locations to compare.

iv. Try an alternative way of looking at how far customers traveled. Do this by creating a new data column that computes the distance between customer and store.

c. Revenue Questions:

i. How do the sales volume in each store relate to Acell’s revenues?

ii. How does this relationship depend on the configuration?

d. Configuration Questions:

i. What are the details of each configuration? How does this relate to price?

ii. Do all stores sell all configurations?

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Machine Learning For Business Analytics

ISBN: 9781119828792

1st Edition

Authors: Galit Shmueli, Peter C. Bruce, Amit V. Deokar, Nitin R. Patel

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