https://github.com/camdavidsonpilon/lifestyles

Work-In-Progress: conjoint analysis in Python
https://github.com/camdavidsonpilon/lifestyles

conjoint-analysis pymc3 statistics survey

Last synced: 2 months ago
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Work-In-Progress: conjoint analysis in Python

• Host: GitHub
• URL: https://github.com/camdavidsonpilon/lifestyles
• Owner: CamDavidsonPilon
• License: mit
• Created: 2017-08-28T02:15:05.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-04-29T21:31:55.000Z (over 6 years ago)
• Last Synced: 2024-10-18T19:54:02.270Z (3 months ago)
• Topics: conjoint-analysis, pymc3, statistics, survey
• Language: Python
• Homepage:
• Size: 16.6 KB
• Stars: 52
• Watchers: 10
• Forks: 10
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
![lifestyles_logo](https://imgur.com/SjZBq1V.png) 

_lifestyles_ is a Python package for performing conjoint analysis. What is conjoint analysis? I'm glad you asked! Conjoint analysis is an alternative survey analysis technique. Instead of asking survey partcipants about how they feel about specific characteristics, instead the particpants are asked to evaluate holistically. For example, suppose you are interested in creating a new lemonade beverage, and you want to better understand what your potential customers' preferences are. We _could_ design a survey like:

> Q1. How much sugar do you prefer in your lemonade?

>  - [ ] No sugar

>  - [ ] 1 sugar

>  - [ ] 2 sugar

> 

> Q2. How much lemon do you prefer in your lemonade?

>  - [ ] None

>  - [ ] Some

> 

> Q3. How much ....

 

There are some drawbacks to this survey design. We have isolated the attributes of the lemonade, so participants must also compare in isolation. This isn't how consumers make choices. Instead they compare products holistically. Compare the above survey to this instead:

> Q1. Which lemonade would you prefer to purchase?

> - [ ] Some sugar, ice cold and strong lemon flavour

> - [ ] No sugar, ice cold and mild lemon and mild mint flavour

Or, something like:

> Q2. On a scale of 1 to 10, how likely are you to purchase the following lemonade? 

> 

> Warm, honey-sweetened, with strong lemon flavour. 

> 

> 1 ♢ ♢ ♢ ♢ ♢ ♢ ♢ ♢ ♢ ♢ 10

The latter surveys asks us to look at beverages, and not attributes. This is the much more common consumer task. Indeed, walking into a convience store for a lemonade implies the consumer will have to make these decisions. 

How can we analyze surveys like this? That's where conjoint analysis comes in. The statistical methods will decompose the consumers' choices into what attributes strongly correlate with purchase or selection.

### Work in Progress

This library is a work-in-progress, and alpha-stage development. 

### References 

 - [Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs. Peter J. Lenk; Wayne S](http://webuser.bus.umich.edu/plenk/HB%20Conjoint%20Lenk%20DeSarbo%20Green%20Young%20MS%201996.pdf).

 - [Software for Hierarchical Bayes

Estimation for CBC Data](https://www.sawtoothsoftware.com/download/ssiweb/CBCHB_Manual.pdf).

 - [Case study into university pricing](https://conjoint.online/2017/04/20/pricing-case-study/)