Month: March 2018
Combining concepts from Choice theory (Economics) and applications of Parameter estimation (Statistics), Conjoint analysis is a quantitative market research technique which helps identify which attributes of a given product or service users most value.
Employed in the areas of Product development, Marketing (Product positioning), and Operations, knowing user choices allow to improve product offering (by modeling and testing focused set of product options), and best allocate resources.
A conjoint study typically starts with identifying an approach exercise starts with the design of a Conjoint research following by estimating model parameters. This involves breaking the product/service into constituent parts (attributes) to build profiles and gathering preference data through surveys.
Design of conjoint study typically involves the below steps:
- Recognize approach (type of analysis required in the situation)
- Identify attributes and assign levels
- Define utilities and design experiment (based on choice situations)
- Parameter estimation and synthesize results
- Develop implications
For the purposes of the post, I present a hypothetical situation where I want to develop the next billion-dollar cola product based on what attributes cola drinkers most like.
In this survey, I had 10 respondents rate 8 profiles of cola products. The profiles have been created based on varying levels of a cola drink’s taste profile. These distinguishing attributes used are “kola (the kola nut bitter to ending in vanilla-sweet flavor)”, “fizz (effervescence)”, “sweetness” and “price”.
Based on the profiles presented, the respondents were asked to provide their liking for each of the product profile.
install.packages("conjoint") library("conjoint") setwd("C:/R") #Read in the files profiles <- read.csv("profiles.csv", header=T, na.strings=c("")) preferences <- read.csv("preference.csv", header=T, na.strings=c("")) #Add the levels levels <- c("low", "high", "low", "high","low", "high","1.5", "2") levels.df <- data.frame(levels)
For simplicity, the four attributes identified have been configured with levels across the flavor profiles and price.
Once the data is loaded, we will call the conjoint function by passing three inputs:
- (dataset: preference) Survey responses from participants with ratings across each of the flavor+price profile created
- (dataset: profiles) Profiles created based on varying levels of flavors and price points
- (dataframe: levels.df) Levels across the four attributes
Plot: Utilities of individual attributes
Plot: Average importance of factors
Looks like based on survey results, the most appreciated attributes are fizz followed by kola. So we might want to create a cola with higher fizz and kola flavor (like Thums up). Sweetness is relatively less important but based on the positive utility of the attribute “sweetness”, there are respondents who have liked a sweeter cola. Of all the attributes, the least important is price. This in fact could be observed by eyeballing the two paired profiles which have the same taste profile but a different price (for example: profile 1 & profile 2 are such a pair). This means people are ok to pay $0.5 premium for the cola which most appeal to them.
The challenges in setting up a conjoint study are often complex and multi-faceted. In a more realistic setting, typically products or services have both more attributes and levels which leads to a huge number of possible profiles to evaluate for candidacy. And during evaluation of these large set of profiles by respondents, the results are subject to various type of response bias.