News, updates, articles on conjoint analysis

Pricing a new course for Malaysian university through conjoint analysis

Posted on 20 April 2017. Topics:

Kitzpatrick University is operating in the competitive educational market in Malaysia, where dozens of providers offer an abundance of courses and programs at various levels. Kitzpatrick, building upon its strong position in the field of chemical engineering and connections with key players in the petroleum industry, is considering opening a new Bachelor of Science in Chemical Engineering program, which will feature industry placements.

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What is conjoint analysis?

Posted on 19 April 2017. Topics:

Conjoint analysis is one of the most widely used and powerful quantitative methods in market research. It helps uncover how people make choices and what they really value in products and services.

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What is MaxDiff?

Posted on 18 April 2017. Topics: ,

MaxDiff (Maximum Difference Scaling) and BWS (Best–Worst Scaling) are related statistical techniques that help prioritise product features. Unlike conjoint, MaxDiff does not look at products as combinations of levels grouped by attribute. In MaxDiff, researchers are free to examine features in a more haphazard manner, which comes at the cost of limited analytical capabilities and limited depth of insight.

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What is two attribute trade-off analysis?

Posted on 18 April 2017. Topics: ,

Two-attribute trade-off analysis was an early conjoint-like research technique where respondents were shown a series of trade-off tables. Each table would contain all the possible combinations of levels for two attributes. Respondents would then need to rank each column in the table according to their preference.

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What is self-explicated conjoint analysis and why you should not use it

Posted on 18 April 2017. Topics: ,

First of all, “self-explicated conjoint analysis” is not conjoint analysis. It is an inferior technique that attempts to present similar outputs as conjoint, but does that badly and should not be used. Conjoint.ly does not offer self-explicated conjoint and we recommend you steer clear from suppliers advocating this technique.

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Classification of conjoint analysis

Posted on 14 April 2017. Topics: ,

We are often asked what types of conjoint analysis exist and which ones we offer on Conjoint.ly. This is an opinionated classification of conjoint analysis that helps you understand what some experts are talking about.

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Frequently asked questions

Posted on 18 March 2017, updated on 19 May 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, updated on 7 May 2017. Topics:

Generally, marginal willingness to pay (MWTP) is the amount of money your customers are willing to pay for a particular feature of your product (i.e., how much your customers are ready to pay for an upgrade from feature A to feature B). Conjoint studies are well-suited to the calculation of MWTP.

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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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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 7 May 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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