Work-In-Progress: conjoint analysis in python
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data adding new cbc hb model Sep 5, 2017
lifestyles adding some documentation to rank-based Apr 29, 2018
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MANIFEST.in adding intial untested setup. Version 0.0.1 Sep 9, 2017
README.md adding some documentation to rank-based Apr 29, 2018
requirements.txt adding a model dir to keep things clean, update readme Sep 5, 2017

README.md

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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