ISYE 6501 HW 15 Latest Updated 2022 Rated A

ISYE 6501 HW 15 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 ne ...

ISYE 6501 HW 15 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 web site; and at least one of your other answers should be based on combining more than one of the data sets. Think about the problem and your approach. Then talk about it with other learners, and share and combine your ideas. And then, put your approaches up on the discussion forum, and give feedback and suggestions to each other. You can use the {given, use, to} format to guide the discussions: Given {data}, use {model} to {result}. Ideas for Monetization: Using the Major from the university along with credit info to advertise related training based on expected career level. Might be able to get this just from the university data set. Using credit information along with the websites the person visited on the company’s web site may provide data for an “upsell”. If a user has excellent credit and is looking for auto parts, maybe they could be included into a list of potential car buyers to sell to dealerships in the local area. Or similarly if they are looking for items related to travel (luggage, language training, travel guides…etc) that data could be sold to websites like Travelocity and Priceline. For data set three eye tracking is mentioned, if that is in done, perhaps pupil dilation could be monitored, our pupils can dilate if something is exciting. We could use that as a potential identifier. Data from the credit bureau along with the data for offspring could be used to predict large purchasesweddings, college supplies, births of grandchildren. All of which could be used on the company website to sell related products, that are listed in this person’s feed. Matching the data: How do we match the datasets to match individuals in order to use all the data and get the highest ROI for the money we just spent? Normally you would look for a unique id for each user and match that up like in a database, unfortunately I don’t think that would be available. This study source was downloaded by 100000834091502 from CourseHero.com on 05-16-2022 07:10:07 GMT -05:00 https://www.coursehero.com/file/40843634/ISYE-6501-HW-15pdf/ I think the best way is to use formula based on the number of fields that match like first name, last name which are available in all three data sets. Then look for instances where two data sets match like middle name & middle initial, Marital status & marital status, email domain & email domain. Step 1. Matching- Before we can use the data sets together, we must find all the likely matches. GIVEN similar fields in the 3 data sets USE a Bayesian model to match the data from each TO one person with a reasonable degree of certainty. Step 2. Single data set- Use a model on a single data set to quickly get some return on investment. GIVEN previous search data and eye tracking data USE a linear Regression Model TO take the amount of time spent on each item and amount of time spent on each category and create a score for all the items in inventory from the model to suggest the most interesting items the user is most likely to buy, increasing profit. Step 3. Multiple Data Sets- Combine multiple data sets and multiple models to maximize ROI on our data investment. GIVEN the credit data and the Major and Marital Status from the College data USE a Tree model TO categorize users into buckets that can be given a value and used in conjunction with the single data set model in order to know what large purchases to advertise. Create a targeted advertising product to sell that will allow higher end advertisers to target those most likely able to afford their products. Step 4. Repeat step 3 with new ideas testing each for profitability and adding as many profit streams as possible while the data is still valid.