Skip to main content

Conjoint Analysis

One of the most powerful and necessary tools available to a product manager or market researcher is conjoint analysis. Conjoint analysis helps you assess the relative appeal of various product features that would add to the cost of a product.

A recent project presented me with an ideal case for applying conjoint analysis. My company was doing market research for a client building condominiums at the northwest tip of downtown Austin. Floor plan mix (square footage and number of bedrooms of the various units), pool, concierge, finishes, storage space, etc. all contribute to the cost of building condominiums or to recurring maintenance fees.

To determine the relative appeal of each feature, I couldn't just ask prospective buyers to rate or rank them, since the appeal of each feature traded off against its cost. A buyer might have ranked a feature highly until he discovered how much it would cost. Furthermore, a buyer might have been willing to pay for each individual feature but might not have had the budget to buy all of them. Enter conjoint analysis.

I included in a survey two conjoint analysis questions. Each of them asked the respondent to choose among several pricing packages. Here is one of the questions:

"If you were to buy a condominium and had to select from the following configurations, which would you choose?"

a. 850 sq. ft., price $233,000, 1 bed/1.5 bath, stainless appliances, garage storage
b. 850 sq. ft., price $231,000, 1 bed/1.5 bath, standard appliances, garage storage
c. 1100 sq. ft., price $294,000, 2 bed/2 bath, stainless appliances, no garage storage
d. 1100 sq. ft., price $292,000, 2 bed/2 bath, standard appliances, no garage storage
e. 1400 sq. ft., price $384,000, 2 bed/2 bath, stainless appliances, garage storage
f. 1400 sq. ft., price $374,000, 2 bed/2 bath/study/2 story, standard appliances, no garage storage

After importing the responses to the conjoint analysis questions into Excel, I used the "Regression" statistical analysis tool to compute the relative appeal of each feature.

Comments

RD said…
In a conjoint analysis survey, you have to have at minimum as many questions in the survey as there are variables to choose from. Regression analysis from 2 inputs for the variables you are trying to solve for cannot predict consumer preference with any statistical significance. In general, you would want between 1.5 to 3 times as many questions as variables to achieve full saturation as well consistency in results.
rcauvin said…
As I understand it, the statistical significance of conjoint analysis lies not in the number of different questions, but in the number of different profiles. (Each multiple choice answer represents a profile.) You need to have more profiles than you have attributes.

One caveat is that a large number of attributes entails a large number of profiles. Presenting a large number of profiles and attributes to the user in a single question is confusing and burdensome to respondents.

Also, it does certainly help to cross-check the conclusions of conjoint analysis with other survey questions, and with the conclusions from qualitative research.

Popular posts from this blog

Why Spreadsheets Suck for Prioritizing

The Goal As a company executive, you want confidence that your product team (which includes all the people, from all departments, responsible for product success) has a sound basis for deciding which items are on the product roadmap. You also want confidence the team is prioritizing the items in a smart way. What Should We Prioritize? The items the team prioritizes could be features, user stories, epics, market problems, themes, or experiments. Melissa Perri  makes an excellent case for a " problem roadmap ", and, in general, I recommend focusing on the latter types of items. However, the topic of what types of items you should prioritize - and in what situations - is interesting and important but beyond the scope of this blog entry. A Sad but Familiar Story If there is significant controversy about priorities, then almost inevitably, a product manager or other member of the team decides to put together The Spreadsheet. I've done it. Some of the mos

What Product Managers Can Learn from the Apple iPod

The Story When Apple unveiled its iPod digital music player back in October 2001, I dismissed it as a  parity product . I already owned the Cowon iAUDIO CW100 MP3 player, loaded with my favorite tunes. There was Apple, generating great hype over the iPod as if it were a breakthrough product. The idea of a portable digital music player was nothing new. The first mass-produced MP3 players came out in 1998. In late 2001, the concept may have been new to a lot of Apple customers, but it wasn't new to me. I proudly showed my MP3 player to friends when they gushed about the iPod. Thus Apple's iPod was not an innovative product in and of itself. Years later, however, I realized the significance of ecosystem of which the iPod was a part. Apple had released iTunes (with technology purchased from  SoundJam MP ) and created the iTunes Store for finding and downloading music. Unlike Napster , it was a safe and legal way of distributing and acquiring music. The prior way of playing

Stop Validating and Start Falsifying

The product management and startup worlds are buzzing about the importance of "validation". In this entry, I'll explain how this idea originated and why it's leading organizations astray. Why Validate? In lean startup circles, you constantly hear about "validated learning" and "validating" product ideas: The assumption is that you have a great product idea and seek validation from customers before expending vast resources to build and bring it to market. Indeed, it makes sense to transcend conventional approaches to making product decisions . Intuition, sales anecdotes, feature requests from customers, backward industry thinking, and spreadsheets don't form the basis for sound product decisions. Incorporating lean startup concepts , and a more scientific approach to learning markets, is undoubtedly a sounder approach. Moreover, in larger organizations, sometimes further in the product life-cycle, everyone seems to have an opinio