Conjoint analysis (also known as Discrete Choice Modelling, or stated preference research) 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. 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 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 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 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.
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 the values they place on different features. 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.
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 it is then possible to use statistical analysis to work out the value, or utility score, of each part 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.
These attributes and levels can be used to define different products or product profiles. The first stage in conjoint analysis is to create a set of product profiles which customers or respondents are then asked to compare and choose from. Obviously, the number of potential profiles increases rapidly for every new attribute added, so there are techniques to simplify both the number of profiles to be tested and the way in which preferences are discovered. Different type or flavours of conjoint analysis such as choice-based conjoint (CBC), full-profile, or adaptive conjoint analysis (ACA) and other approaches have different ways to managing 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.
By analysing which items customers choose or prefer from the product profiles offered, it is possible to work out statistically 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.
The result is a detailed 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. 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.
dobney.com 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 dobney.com and the findings derived from the survey are invaluable for us."
Marketing Manager, Leica Microsystems
Depending on the product or service, it is possible that off-the-shelf approaches may be limited in some ways. Fortunately there are a number of related methods 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 brief overview of Simalto and trade-off grid approaches as a (.pdf) or (.rtf paper)
A range of other research techniques including menu building (building a configured product from a range of selected options), and search and filter studies 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
Our paper on conjoint analysis (.pdf) (.rtf) gives a print out summary of what to expect, or see our case study examples. To see the workings we have a fully worked up simple conjoint analysis worked example in Excel to show how how it works from design to analysis.
To understand how it works see our interactive instant conjoint analysis demonstration of how what you value can be calculated from the choices you make.
See how market modelling works so you can make better ROI decisions (new window)
For help and advice on carrying out conjoint analysis projects contact email@example.com