TV Product Analysis Suite

This suite contains two projects: Conjoint Analysis and Bootstrapped WTP Analysis. Both projects are centered around TV product strategy and provide insights into consumer preferences and valuation of product features.

Conjoint Analysis Project

Overview

The Conjoint Analysis project applies statistical techniques to determine how consumers value different features of TV products. It includes configurations for Sony and Sharp, alongside the "Own Design" scenario, offering insights into consumer preferences and competitive dynamics within the TV product landscape. Project outputs include partworths, attribute importance, and market simulations to inform optimal product design and pricing.

Components

• Input data of customer preferences.
• R Markdown script for conjoint analysis.
• Knitted HTML results of the analysis.
• Detailed report of methodology and findings.

Bootstrapped WTP Analysis Project

Overview

The Bootstrapped WTP Analysis extends the conjoint analysis by applying bootstrap techniques to estimate confidence intervals for willingness to pay. This advanced analysis offers a deeper dive into the financial valuation of TV features. It also includes configurations for Sony and Sharp, alongside the "Own Design" scenario, allowing for a thorough examination of consumer preferences and competitive dynamics across different product scenarios.

Components

• R Markdown script for bootstrap regression analysis.
• Knitted HTML results of the analysis
• Output tables for each team member's WTP for TV product attributes.
• Detailed report of methodology and findings.

General Usage

For both projects, users will require R and relevant packages installed on their machine. Data should be updated according to the current market scenario before running the scripts.

These projects are intended to provide a toolkit for analyzing and enhancing TV product strategies through consumer data analysis.

Acknowledgements

Thanks to:

• Professor Prasad Naik, co-founder of the MSBA program at UC Davis and professor for Advanced Statistics, whose teachings have been instrumental in the execution of this project.

• My classmates for their collaboration and contributions:

  • Sujai Aditya Muralidaran
  • Kritikka FNU
  • Sushma Niveni Pindiga
  • Ruben Cases