![\"Unveiling](\"\/wp-content\/uploads\/2016\/07\/unveiling-user-needs-conjoint-analysis-720px.jpg\")
<\/a><\/p>\nTraditional regression analysis and hierarchical Bayesian analysis are the most common methods used to perform the data analysis. The outcome will answer questions such as:<\/p>\n
- \n
- How do you know what the market wants?<\/li>\n
- Are your market segments correctly divided?<\/li>\n
- What are your customers willing to pay for your product?<\/li>\n
- Is quality more important than price?<\/li>\n
- How much value is really perceived in your product attributes?<\/li>\n<\/ul>\n
The trade-off list is sometimes long and complex. Understanding how to design and implement a conjoint analysis can help to discern customer preferences and to ensure that your product design concept is based on sound market research.<\/p>\n
Using Conjoint Analysis to Develop New Products<\/h2>\n
In the case of developing a new product, the conjoint analysis will typically be administered after the Voice of the Customer (VOC) and market segmentation studies have been performed. Many successful organizations will use a conjoint analysis when deciding what product attributes to include in the customer and product requirements. Because it is not reasonable to design a product with attributes that meet all customer criteria, it is necessary to gain a deeper understanding for what the customer preferences are. For example, if 90 percent of the customers said that a low price was important, it is critical to understand what that means. Key questions include:<\/p>\n
- \n
- What trade-offs exist?<\/li>\n
- How low is low?<\/li>\n
- Is low price more important than a high quality result?<\/li>\n
- What standard of quality would they expect and at what price?<\/li>\n
- Would they be willing to pay more for a product that offered a faster turn-around time, or had automated processing, or improved performance?<\/li>\n<\/ul>\n
The following is an example of how a conjoint analysis can uncover real user needs and identify the customer requirements necessary for a successful product launch. Several years ago, there was a flu pandemic that caused a shift in respiratory testing that resulted in a flood of new products and manufacturers entering the market. One start-up company launched an innovative product that addressed the most critical, unmet customer needs.\u00a0This company launched the right product, at the right time to the right customer, and grew rapidly to become a market leader. Within one year of launching, the product grabbed significant market share. A few years later, a market segmentation study and conjoint analysis for a competing company was performed to collect customer information on the respiratory market and to create customer requirements for a product under consideration for development.<\/p>\n
The analysis uncovered that most customers were proudly paying list price for the product. What was most interesting was that the product was selling for a much higher price per unit than any other product in that segment. In some cases, the product commanded more than twice the incumbent market leader\u2019s average selling price. The conjoint analysis revealed that customers valued the key product attributes so much that they were willing to switch from their current testing process and to pay more. This is a good example of how a conjoint analysis can uncover true customer needs. All customers want a good price, but finding out what attributes they value most and delivering that value to the customer at a fair market price is critical for success.<\/p>\n
Transforming User Needs into Successful Products<\/h2>\n
There are many ways a conjoint analysis can be applied, but the basic objective for performing the analysis is to help define user needs and transform those needs into product requirements based on customer prioritization. The trade-off list can be\u00a0long and complex. Key questions cover everything from what the market wants and what customers are willing to pay, to whether quality is more important than price.<\/p>\n
Understanding how to design and implement a conjoint analysis can help to discern customer preferences and ensure that your product design concept is based on sound market research.<\/p><\/blockquote>\n
It is important to have a statistically sound sampling of the target market in order to properly model the business opportunity, penetration and revenue expectations. Another key factor to a successful conjoint analysis is not using too many variables. More than 30 can lead to a statistically unmanageable data set and often lead to more questions than answers.<\/p>\n
The most important key factor to ensure that a conjoint analysis is effective is to make sure that the market research it is being applied to is sound. This starts with establishing a market driven organization<\/a> that is performing effective VOC and market segmentation studies (see IOI Partners article on market segmentation<\/a>), in addition to ongoing re-evaluation of the market, your capabilities and the dynamics. These key elements along with a strong go to market strategy will provide for a strong product launch and a sustainable long-term business.<\/p>\n","protected":false},"excerpt":{"rendered":"
Conjoint analysis is a powerful, multivariate, statistical market research strategy designed to gain a deeper understanding and prioritization of customer trade-offs. There are multiple ways a conjoint analysis can be administered, but the most traditional way is to use a customer survey in the form of a focus group or a live, web or phone […]<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[1,54,12],"tags":[41,46,47,29],"_links":{"self":[{"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/posts\/1888"}],"collection":[{"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/comments?post=1888"}],"version-history":[{"count":0,"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/posts\/1888\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/media?parent=1888"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/categories?post=1888"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ioipartners.com\/wp-json\/wp\/v2\/tags?post=1888"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}