Fall 2024 section of EMSE 6035: Marketing Analytics for Design Decisions @ GWU
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Updated
Sep 3, 2024 - HTML
Fall 2024 section of EMSE 6035: Marketing Analytics for Design Decisions @ GWU
This repository contains configuration files for performing conjoint analysis on Netflix survey data.
Used python machine learning to preform conjoint analysis on generated smartphone data
Explore consumer preferences & valuations for TV products with Conjoint & Bootstrapped Willingness-To-Pay analysis. Compare and analyze own design against competition for an edge! Enhance your product strategy!
A package for a more general, more straightforward, and more creative conjoint analysis
I help an espresso machine manufacturer assess the product attribute preferences coffee drinkers have while purchasing espresso machines using conjoint analysis in Microsoft Excel
This project is to predict credit card customer default risk using data from April to September 2005 for customers in Taiwan. It involves preprocessing the data, including cleaning and transforming, and employing algorithms like Apriori for association analysis and Random Forest for modeling to predict next month's defaulters.
Conjoint Analysis on consumer preferences on Sunnyvale, California Housing Market
An app to help in the calibration of Vulnerability Scores for humanitarian assistance targeting
A companion plugin for Excel for charting Conjointly outputs (easy formatting for preference share and revenue charts from conjoint analysis simulations as well as colouring TURF analysis tables)
This project aims to conduct a random survey design for collecting responses regarding wine preferences of Italian consumers. Furthermore, it attempts to understand how preference share gets affected as we vary different attributes associated with wine with the use of a research method called Conjoint Analysis..
Shiny BPTO Conjoint Siumlator for HB models
Conjoint analysis is a data informed approach to understanding what consumers prefer about a product
portfolio of business & marketing analytics coursework
This is a full conjoint analysis study ran on 250 survey participants, analysed through Bayesian Hierarchical Model, using RSTAN package
Market and Insights Analyst at the consulting services department of a multinational professional services firm. As part of this role, you are asked to work across the following three (3) different client engagement projects.
Used R to build a Conjoint Analysis from scratch, assessing the importance of different features of a Flat-Panel TV.
This repository is a coursework I have taken at McCombs School of Business, UT Austin during my master's degree
Simple Conjoint in Python
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