![\"conjoint](\"https:\/\/www.questionpro.com\/blog\/wp-content\/uploads\/2016\/06\/maxresdefault.jpg\")
Conjoint Analysis is used when a brand wants to know how important different elements of a decision are. We know from neuroscience that people (people like you and me) cannot put numeric values to how important is, say, flying direct versus flying on your preferred airline. But brands need numeric values when they seek to maximize revenue, profit, customer choice, and satisfaction.<\/p>\n
Consider an organization producing tablets, perhaps a competitor to the Apple iPad and Samsung Galaxy. The organization needs to understand how different customers value Attributes such as Size, Brand, Price, and Battery Length. Armed with this information they can create their product range and offering.<\/p>\n
Conjoint Analysis seeks to assign values to these product Attributes and Levels by creating realistic choices and asking people to evaluate them. Math is then used to calculate what the underlying values are.<\/p>\n
LEARN ABOUT:<\/strong> Level of Analysis<\/a><\/em><\/p>\n In the case of Choice-Based Conjoint Analysis (currently the most popular form of Conjoint Analysis) participants are shown a series of options, like the one below, and asked to select the one they would be most likely to buy.<\/p>\n Nexus \u2610<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Choice-Based Conjoint asks people to pick the option they would be most likely to buy, other forms of Conjoint Analysis ask people to rank or rate options. Choices are widely considered to be more realistic than asking people to rank options or to rate them.<\/p>\n Each participant is shown several of these choices and the answers they give allow us to work out the underlying values. For example, we can work out what their preferred size is, and how much they would pay for their preferred brand. Once we have the values for each of the Attributes (e.g. brand, size, battery length etc) and for each of the Levels of each Attribute (e.g. Apple iPad, Google Galaxy, Sony Xperia & Nexus) there is a range of analytic options. The key tools for analysis include: What-if modelling, forecasting, segmentation, and applying cost-benefit analyses.<\/p>\n Not all research problems can be tackled with Conjoint Analysis. Conjoint works best when the value of a product can be approximated by the sum of its parts, such as mobile phones, cars, computers, ski holidays, and financial product. Conjoint works less well with emotional variables or interacting combinations, such as advertising messages or food combinations.<\/p>\n To conduct a Conjoint Analysis project you need: access to suitable software (to design and analyse the questions), and a design that meets the rules for conjoint analysis (e.g. covering the important Attributes, proper rotation of the question elements, and avoiding interactions).<\/p>\n Conjoint Analysis requires more preparation than most forms of quantitative market research and the analysis takes longer \u2013 which tends to make the whole project slower and more expensive than a simple quantitative test. Conjoint Analysis is used when simpler\/cheaper options are unlikely to provide a useful outcome.<\/p>\nExample<\/h1>\n
\n\n
\n \n
\n11 inch
\nBattery 13 hours
\n$800<\/p>\n<\/td>\nSamsung Galaxy
\n8 inch
\nBattery 9 hours
\n$600<\/td>\nApple iPad
\n9 inch
\nBattery 7 hours
\n$700<\/td>\nSony Xperia
\n10 inch
\nBattery 11 hours
\n$500<\/td>\n<\/tr>\n\n \u2610<\/td>\n \u2610<\/td>\n \u2610<\/td>\n \n When and How to use Conjoint Analysis<\/h1>\n
Want to find out more?<\/h1>\n