Conjoint analysis – a brief guide

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Do you ever wonder how your product fares against market competitors? Or imagine how a small tweak to current product features could affect customer preference?

If so, these questions could be answered via conjoint analysis, a powerful research technique that allows you to establish the importance of product features such as pricing or brand on consumer preference. Answering how do changes to product features affect market share? And how do these features combine to therefore affect consumer decisions?

What is conjoint? – an example-based approach

By definition conjoint is a research methodology that assesses product preference and helps to determine which combination of product features people find most attractive.

The basics of this technique can be surmised when looking at how consumers shop for a product, let’s take a desktop computer as an example. This product has several attributes which we consider when making a purchase, these can be brand, price, RAM and screen size to name a few and each of these attributes can have different levels for example a computer could have 8GB, 10GB or 12GB of RAM.

Conjoint analysis aims to understand the interaction of these levels on product preference. This is by calculating preference scores for each of the levels (called part worth utilities), these can then be combined to produce overall product preference scores. The result is that researchers and marketers can create “what if” scenarios (using our in house simulator tool) whereby different products can be trialled and tested against one another. Allowing the trade-off of existing products against market competitors, how new products will fair in the market place and how modifying existing products can gain a competitive advantage in terms of market share (share of preference).

Example of simulator tool

Other key outputs include the attribute importance scores and part-worth utility scores. The attribute importance scores show the relative importance of the attributes, each attribute is assigned a value ranging from 0 to 100 which collectively sum to 100. If price has an importance score of 40 and RAM has an importance score of 20 we can infer that price is twice as important in the decision making process than RAM.

Example of attribute importance scores

AttributeImportance score
Screen size10

The part-worth utility scores give insight into the relative importance of each of the levels within an attribute, these values are often centred at 0 and are displayed in the bar chart below. Here we can see that 16GB of RAM is preferred to 8GB and 12GB of RAM when considering buying a desktop computer.

Example of utility scores for RAM

Choice based conjoint

One variant of conjoint is called choice based conjoint which involves presenting respondents with an array of different package permutations (i.e. varying product levels) and asking them to select their preferred concept from a selection on screen. The idea is to trade-off products in a manner which simulates a typical purchase decision whereby the different levels are designed to alternate in a balanced manner, avoiding bias and allowing us to understand which features are most important to buyers and also determine the relative importance of each level within each attribute.

Example of choice based conjoint survey

Why Dipsticks Research?

We have an in-depth knowledge of conjoint analysis, allowing us to advise on the do’s and don’ts to deliver the best insight as well as understanding the variant of conjoint which is best for your brand. We are experienced programming conjoint into our surveys having worked with many clients over the years implementing this technique, as well as this experience our in-house team of statisticians will work alongside the research team to walk you through the project set-up and utilise the outputs of this research method.

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If this innovative methodology could help answer your business questions or if you would like to know any more about this technique, please contact or call us on 01434 611160.

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