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Conjoint analysis and choice models

Conjoint analysis modelling Conjoint analysis is an advanced market research technique that gets under the skin of how people make decisions and what they really value in products and services (it also known as Discrete Choice Estimation, or stated preference research). Conjoint analysis involves presenting people with choices and then analysing what were the drivers for those choices. Our interactive conjoint analysis demonstration shows a simplified example of this process at work or our simple conjoint in Excel example.

The output from conjoint analysis is a measurement of utility or value and is perfect for answering questions such as "Which should we do, build in more features, or bring our prices down?" or "Which of these changes will hurt our competitors most?" In addition these utilities are used to build market models that enables forecasts to be made of what the market would choose given different product or service designs.


Conjoint analysis is about finding the optimum point between cost and quality

Conjoint analysis aims to find the optimum positioning between low-price-low-quality and high-price-high-quality in terms of price and features by quantifying the trade-offs and compromises customers take in decision making.

Every customer making choices between products and services is faced with trade-offs (see our conjoint demonstration). Is high quality more important than a low price and quick delivery for instance? Or is good service more important than design and looks? Or, are improvements in efficacy outweighed by adverse effects for health care products for instance.

For businesses, understanding precisely how customers, and by extension markets, value different elements of the product and service mix means product development can be optimised to give the best balance of features or quality for prices the customer is willing to pay, or result in different products produced for different segments or market needs aiming to maximise the value the customer gets from the products or services the business offers.

Conjoint Analysis is a technique developed since the 1970s that allows businesses to work out and quantify the hidden rules people use to make trade-offs between different products and services and to quantify the values they place on different features or component parts of the offer. By understanding precisely how people make decisions and what they value in your products and services, you can work out the sweetspot or optimum level of features and services that balance value to the customer against cost to the company and forecast potential demand or market share in a competitive market situation.



The principles behind conjoint analysis start with breaking a product or service down into it's constituent parts (called attributes and levels - see the section on how to design a conjoint analysis study) then to test combinations of these parts in order to find out what customers prefer. By designing the study appropriately using carefully chosen statistical designs (also known as experimental designs) it is then possible to use statistical analysis to work out the value, or utility score, of each part of the product or service in terms of its contribution to the customer's decision.

For example a computer may be described in terms of attributes such as processor type, hard disk size and amount of memory. Each of these attributes is broken down into levels - for instance levels of the attribute for memory size might be 1GB, 2GB, 3GB and 4GB.

Terms and language used to describe a typical choice task for conjoint analysis These attributes and levels can be used to define different products by choosing different levels for different products so the first stage in conjoint analysis is to create a set of product profiles (possible combinations of attributes and levels) to produce a set of options from which customers or respondents are then asked to choose - know as choice sets. Obviously, the number of potential profiles increases rapidly for every new attribute added as the number of possible combinations increases, so there are techniques to simplify both the number of profiles to be tested and the way in which preferences are tested so that the maximum amount of choice information can be collected from the smallest set of choice tasks. Different type or flavours of conjoint analysis such as choice-based conjoint (CBC), full-profile, adaptive conjoint analysis (ACA), menu-based conjoint, adaptive choice based conjoint, and other approaches have different ways to manage the balance between the number of attributes that can be included and the relative complexity of the choices that need to be shown in order to get good quality data.

After the choice tasks have been completed, a range of statistical tools can be used to analyse which items customers choose or prefer from the product profiles offered in order to quantify both what is driving the preference from the attributes and levels shown, but more importantly, give an implicit numerical valuation for each attribute and level - known as utilities or part-worths and importance scores. These utilities give an measurement of value for each level in terms of its contribution to the choices that were made and so shows the relative value of one level against another.


Market models

The result is a detailed quantified picture of how customers make decisions, and a set of data that can be used to build market models which can predict preferences or estimate market share in new market conditions in order to forecast the impact of product or service changes on the market. For businesses this allows them to see where and how they can gain the greatest improvements over their competitors, where they can add value for the customer, how price impacts on decisions and so forecast demand and revenue. Not surprisingly conjoint analysis has become a key tool in building and developing market strategies.

By combining these market models with internal project costings, companies can evaluate decisions in terms of Return on Investment (ROI) before going to market. For example determining what resources to put into New Product Development and in what areas. Choice-based conjoint or discrete choice modelling also form the basis of much pricing research and powerful needs-based segmentation.

"We were looking for an agency that could understand our solutions and complex customer base in order to transfer this understanding into a comprehensive customer survey. quickly gained deep insight into the specificities of our business and designed an excellent, state-of-the-art conjoint survey. They delivered professional and individual service of a quality we had never experienced before. It was great working with and the findings derived from the survey are invaluable for us."

Marketing Manager, Leica Microsystems


Alternatives to conjoint

Conjoint analysis is relatively complex as it requires an understanding of how to use and create attributes and levels, what flavour to use, how to make the product profiles, what choice task to offer and then how to analyse the data and build the market model. It is possible to use off-the-shelf software which will provide guidance and help, but it can be also make it easy to make mistakes or generate poor designs. And conjoint analysis doesn't always fit, particularly if there are many levels, or a deeper more emotional drive to decision making. So, depending on the product or service, it is possible that off-the-shelf approaches aren't always suitable and other methods are needed. Fortunately there are a number of related approaches used as alternatives to conjoint analysis, such as MaxDiff, configurators or Simalto (also known as trade-off grids). MaxDiff is more about measuring the value from a list of items, than generating complete products, but it uses many of the same features and analytics as conjoint. Simalto, like conjoint analysis, breaks products down into attributes and levels, but then presents them as a grid of options to respondents.

A range of other research techniques including menu building (building a configured product from a range of selected options), and search and filter studies in the form of e-commerce style mock-ups where respondents hunt for their most preferred products can also be used in conjunction with or as alternatives to conjoint analysis.

Demonstrations and further reading

To see the workings we have a fully worked up simple conjoint analysis worked example in Excel to show how conjoint analysis works from design to analysis.

For help and advice on using conjoint analysis for market modelling, or to carry out conjoint analysis research email

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