Catalogue Cross-Selling Background ProRetail is a catalog firm that sells products in a number of different catalogs that it owns. The catalogs number in the dozens, but fall into nine basic categories:
1. Clothing
2. Housewares
3. Health
4. Automotive
5. Personal electronics
6. Computers
7. Garden
8. Novelty gift
9. Jewelry.
The costs of printing and distributing catalogs are high. By far the biggest cost of operation is the cost of promoting products to people who buy nothing. Having invested so much in the production of artwork and printing of catalogs, ProRetail wants to take every opportunity to use them effectively. One such opportunity is in cross-selling—once a customer has “taken the bait” and purchases one product, try to sell them another while you have their attention.
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Such cross-promotion might take the form of enclosing a catalog in the shipment of a purchased product, together with a discount coupon to induce a purchase from that catalog. Or, it might take the form of a similar coupon sent by e-mail, with a link to the web version of that catalog. But which catalog should be enclosed in the box or included as a link in the e-mail with the discount coupon? ProRetail would like it to be an informed choice—a catalog that has a higher probability of inducing a purchase than simply choosing a catalog at random. Use the A-priori algorithm for an Association rules analysis, then prepare a report on your methods and results and results for internal use within ProRetail. This should include:
a) Description of all the steps you took to prepare association rules, including the R code that you run.
b) Explanation of the meanings of the various output statistics (lift ratio, confidence, support) and the frequent itemsets.
c) Present the candidate rules that can be formed from the selected confidence with some form of visualization.
d) Discussion of how the association rules analysis results could be useful to the catalog company to make an informed choice about which catalog or category to cross-promote to a purchaser. Your final report should be a maximum of 3,000 words, not including the R code. It should include an introduction, data and methods, results, recommendation/conclusion, and, if necessary, a reference list. Your R code should be distinguished in the text using Courier New font. This is a quantitative assignment that requires data analysis with R.
The dataset to be used for this assignment is a .csv file called ‘CatalogCrossSell’. It contains 10 columns and 4999 rows of data. Marking will be equally weighted across methods, results, and interpretation of results. Your report will be marked according to the following criteria: • METHODS: Clear explanation of steps taken, and methods used, correct application of analytical
