News, updates, articles from Conjoint.ly

Frequently asked questions

Posted on 18 March 2017. Topics:

Here is a summary of questions our users often ask us. If there is anything else you’d like to know, please do not hesitate to contact us.

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Comparison of types of conjoint analysis

Posted on 2 March 2017. Topics:

Conjoint.ly offers two types of conjoint analysis: generic conjoint and brand-specific.

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Announcing collaboration with Call For Participants

Posted on 17 February 2017. Topics:

Conjoint.ly announces collaboration with Call For Participants, an online service for recruiting study participants. CFP helps academic and industry researchers to easily advertise experiments, surveys, interviews, and other studies to thousands of potential participants around the world for free.

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How to get participants for your study

Posted on 12 February 2017. Topics:

This guide shows you the range of options we offer to get responses for your experiments.

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How to integrate Conjoint.ly with another survey tool

Posted on 9 February 2017. Topics:

If you’d like to use Conjoint.ly in conjunction with another survey tool, we’d like to make it easy for you. There are currently two ways to do it:

  • through redirects, and
  • with iframes.
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How to interpret marginal willingness to pay (MWTP)

Posted on 13 January 2017. Topics:

Generally, in economics, marginal willingness to pay (MWTP) forms the basis of price, which is defined as the maximum price someone would pay for an extra good or service. It is also known as marginal rate of substitution of something for money.

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How to interpret partworth utilities

Posted on 30 December 2016, updated on 19 March 2017. Topics:

Partworth utilities (also know as attribute importance scores and level values) are numerical scores that measure how much each attribute and level influenced the customer’s decision to make that choice. Because attribute and level partworths are interrelated, in this post we will look at them using the same example of tissue paper. Suppose the company wants to find out customers’ preferences for tissue paper to re-assess its product range, as a pathway to growth. The charts below show some common attributes of the company’s (and competitors’) tissue paper:

  • Texture: weave-like or simple
  • Ply: 3 ply or 2 ply
  • Scent and colour: “recycled unscented”, “white unscented”, or “white scented”
  • Price per 100 sheets: 30¢, 55¢, or 70¢
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How to specify attributes and levels in conjoint analysis

Posted on 29 December 2016. Topics:

Attributes are ‘dimensions’ of your product (such as price, colour, shape, size, brand, location). Include the attributes that you believe are most important to your customers when they make buying decisions, as well as any attribute whose importance you would like to check. For example, if you know that customers are driven by price and size, and want to investigate whether colour is important, include all three attributes (price, size, and colour). Try not to include more than five attributes because it might confuse your respondents.

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Top ten tips for great conjoint analysis

Posted on 28 December 2016. Topics:

Conjoint analysis is a very powerful tool for market research. Here are Conjoint.ly’s best practice tips for setting up your analysis to yield useful results.

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Announcing 2017 Conjoint.ly Best Discrete Choice Experiment Award

Posted on 27 November 2016, updated on 2 March 2017. Topics:

Conjoint.ly invites you to practice conjoint analysis (discrete choice experimentation) on our platform. This competition is for marketing students and those interested in learning marketing research.

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CBC Excel simulator with Conjoint.ly

Posted on 17 November 2016. Topics: ,

For each conjoint study on Conjoint.ly, one of the outputs we provide is an Excel profitability model (also known as CBC simulator). By letting you simulate the market environment, it lets you estimate the profitability of your new product development (NPD). These calculations are based on a simple model:

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Technical points on DCE with Conjoint.ly

Posted on 2 November 2016, updated on 27 March 2017. Topics:

This note is prepared for those familiar with the specifics of discrete choice experimentation to answer key questions in detail.

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Beta version is coming out next week

Posted on 29 September 2016. Topics:

Conjoint analysis has been around for a fairly long time, it is widely used in marketing research, and is taught at almost every marketing course. Yet, when it comes to implementing it in practice, there is a surprising lack of available tools that can help you do that. We found that the big websites like SurveyMonkey and Qualtrics do not quite let you do it easily.

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Why use conjoint analysis

Posted on 29 August 2016, updated on 30 December 2016. Topics:

Do you know what your customers value the most (and least) about your product? With conjoint analysis, you can. Uncovering customers’ preferences provides valuable information to guide decisions about new products, marketing strategy, advertising and promotion to increase sales.

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