ISYE 6501 Homework 15 (Essay).

ISYE 6501 Homework 15 (Essay). Question 20.1 Describe analytics models that could be used to help the company monetize their data: How could the company use these data sets to generate value, and what analytics models might they need to do it? There are lots of good answers, and I want you to think about two types – at least one of your answers should be based on just one data set (the one they’ve collected internally on customer browsing patterns on the website), and at least one of your other answers should be based on combining more than one of the data sets. Using just Data Set #3 (collected by the company using website tracking code) – Classify and profile customers to identify their spending potential (how much they would be willing to spend for an item, as well as how much they could spend over time) Model 1: given the list of products purchased in the past, the price of the product (assuming this information is also available), the date of purchase, and the ship-to address, use K-means clustering to cluster customers into nine categories depending on the frequency of spending and the expensiveness of the product (low, medium, or high for both). By identifying the type of spender each customer is (and therefore their spending potential), the company would be able to cater the price points of the recommended products for each customer. If a customer is a ‘high spender’, then the company would be able to show/recommend more expensive products to them and ideally generate more revenue. The date of purchase can be used to help determine whether the customer frequently goes shopping or not. One way would be to take the time difference (eg, in days) between each subsequent date of purchase and average those differences. If the customer is a frequent shopper, then the company can update their recommendations more frequently. Since the customer is exposed to more products, this can encourage spending if a product catches their attention. The ship-to address should be taken into consideration because even though the purchases are on one account, there many be multiple users and the spending habits of these users should not be confused with each other. One way to evaluate how expensive a product is would be to compare the product’s price compared to similar products. The company can then use the product price clusters to determine the spending potential for each customer – if a customer has a lot of expensive products (compared to similar products), they are probably a high spender. The clustering gets more complicated the more mixed the products are for a specific customer. • Model 1b: given the list of products purchased in the past, the price of the product, and the interquartile range of prices for similar products, use K-means clustering to cluster the product price and determine whether the product is a less expensive, medium expensive, or expensive item.