Conjoint analysis and market models >> Discovering conjoint analysis - what's it all about

Conjoint analysis applications

Applications of conjoint analysis including shelf display for category management Conjoint analysis is used in a wide range of different market research and insight applications from copy testing, to pricing research to product and service design, to defining membership schemes. The list of applications is relatively long as conjoint gets adapted to different purposes. For instance it's possible to use some of the design principles to develop and test areas like website or promotional message design using live in-market testing. Below is a list of common uses.

Category management and SKU pricing

Shelf-display versions of conjoint analysis are a specialist form of pricing research aimed specifically at FMCG (fast moving consumer goods) or consumer packaged goods (CPG). A collection of products at different prices is displayed in the form of a shop or shelf-display as it might be in a supermarket or grocery store. The aim is to see how varying prices change or affect demand or take up across the set of SKUs (stock keeping units) on display. There are only really two attributes - the SKU and the price, though technically each SKU may be considered an attribute and have an individual set of prices (an alternate design structure). A shelf display conjoint analysis can then be used to assess how changes on prices will affect demand for specific SKUs and also be used to optimise the set of SKUs and prices offered within a category (category management) to maximise coverage, share or revenue. This type of conjoint analysis is often tuned with data about real purchase behaviour derived from shop scanner data to ensure the models are tuned to match with real observed data.

Pricing research for other categories

Away from the specialist use for FMCG pricing, conjoint is also used for pricing research for other products in order to understand the value (or willingness to pay - WTP) for different features that make up different products. For instance what is the optimum price for a larger versus a smaller screen size? In these types of conjoint design, price is one attribute among several. A specific factor that might be included is brand and it becomes possible to separate out the direct value of the brand (brand equity) from specific features of the product. A key measure of feature value is willingness to pay (WTP), or alternatively par value - the amount extra that could be charged for an additional feature so overall so the new product is valued the same as a reference lower spec product

Conjoint used for pricing research is normally choice-based conjoint or discrete choice modelling, but options such as menu-based (MBC) are also available. This type of data can also be used to provide base information for yield-management strategies in industries such as hotels or transportation.

Healthcare and pharmaceutical conjoint analysis

Pharmaceutical research is one area where conjoint analysis and trade-off approaches can have many different types of uses. When creating or designing drugs and health interventions there is a fundamental balance between effectiveness, safety and convenience (eg dosing). For pharmaceutical manufacturers developing new drugs the level and method of dosing directly affects all three areas. Higher doses may be more effective, but in counterbalance may bring more side-effects. The pharma company needs to consider what formulation or approach to take into clinical trials (ie to optimise the TPP), and with the cost of clinical trials running into several millions of dollars, taking the wrong or sub-optimum test product profile to take into the clinical trial can lead to expensive mistakes. For this reason, pharmaceutical companies like to understand the treatment preferences of physicians prior to making the final decisions about which formulations to take to trial.

In other situations, patients may have the choice between a range of different intervention possibilities. For instance to counter obesity options may range from diets to drugs or medications, or to surgical intervention such as gastric bands. Each of these options brings a different set of benefits (efficacy likelihood) and challenges and costs. For the health provider, conjoint analysis can be used to understand the relative value of the different health interventions. It can also be used to help patients make more informed decisions about which type of treatment or intervention is more applicable and so leads to more informed patient decision making (we have done work in this area with the University of Michigan).

Product and service design

The main and most familiar use of conjoint analysis is in product and service design. Product and service design can mean many different things. It can be simple involving the optimisation of a few elements, or it can be detailed and complex involving the optimisation of a large number of features potentially through multiple exercises that are then linked into a large model of preference and that can then be used for product and service optimisation. The most famous academic case study in the early days was the use conjoint analysis in the design of Marriott Courtyard hotels (1989 Paul Green et al).

Core to effectiveness in this area is the design of the attributes and levels. For new products creating and describing the features to fit within a conjoint exercise can be an extremely effect way of focusing strategic thinking and setting key priorities and targets.

Membership scheme/club features

Many companies run membership schemes and loyalty programs. In these type of schemes there is often a long list of potential benefits that can be used in such schemes (the same can apply to software design). Techniques such as MaxDiff or conjoint analysis can be used to prioritise which elements to include or not include within the package to ensure the top priority items are included, but also how many items to include and how these affect aspects such as the amount the customer would be willing to pay. Additional analysis like TURF (total unduplicated reach and frequency) which looks at maximising the reach of the scheme while minimising the number of items offered can also be used within these methods to ensure the most effective coverage of the target market.

Employee benefits research

In the US, and for multinational corporations, employee recruitment and retention is an extremely important part of the success of the business. And whereas smaller businesses will tend to rely on salary-based packages, larger corporations usually offer a wide range of employee benefits including factors such a stock options, healthcare plans, sports facilities etc. As a result a growing use of conjoint analysis comes from human resources departments looking to understand how employees value and trade-off different forms of benefits and remuneration packages with the aim of setting optimium policies across the business group - for instance would employees prefer it if we spent more on cafe and food areas, or more on social clubs and entertainment?

Needs based segmentation

Conjoint analysis can also be used with cluster analysis to develop needs based segmentations enabling different customer groups to be developed, profiled and targeted based on underlying product preferences. Many markets for instance customer groups that are budget focused, those that are quality focused and those that are feature focused. Understanding and uncovering these types of groups and others, of a more complex variety can be extremely powerful in terms of positioning products against customers. In international markets, where respondents treat questionnaires differently in different countries (for instance the Chinese and Japanese use rating scales differently to the UK or USA), conjoint can be used successfully for international segmentation projects stepping above these cultural differences.