Conjoint analysis: can a product establish itself on the market?

The de­vel­op­ment of a new product always involves con­sid­er­able financial risk. In addition to the costs of actually de­vel­op­ing the product, there are also the costs of raw materials and pro­duc­tion as well as the costs related to market po­s­i­tion­ing and marketing. Not to mention that even if the market launch is suc­cess­ful, it doesn’t ne­ces­sar­ily mean that it will be able to establish itself on the market over the long term and generate the desired revenue.

Therefore, companies want to minimise the risk of their product not selling. There are a number of business analysis methods available for this purpose. One of these methods is called conjoint analysis. It requests feedback on customers’ pref­er­ences and needs before launching a product on the market. The results help companies to design a product which meets as many demands as possible.

Conjoint analysis is also known as conjoint meas­ure­ment or the conjoint method. It is a market research method which has been used since the 1970s to determine how important the different at­trib­utes of a product are for potential customers.

Conjoint analysis is a type of mul­tivari­ate analysis. This means that more than one stat­ist­ic­al outcome variable (i.e. the product’s at­trib­utes) is included in the overall analysis. When only one attribute is used, it is called uni­vari­ate analysis. Using multiple variables allows a company to determine which com­bin­a­tion of at­trib­utes (e.g. quality, packaging, price) is likely to meet the highest demands on the market and to develop its product ac­cord­ingly.

This analysis method is used to determine which product a customer would choose in a real-life situation i.e., when directly comparing several re­l­at­ively similar products which only differ in a couple of ways. The following questions are a good starting point: Which of these similar products would a customer buy? Which product attribute has the most influence on their pur­chas­ing decision?

Conjoint analysis is based on a customer survey which is conducted under specific terms. This is not a free-form survey where par­ti­cipants can just give their opinion of a product. In this survey, various al­tern­at­ives to a product are provided, and the par­ti­cipant is asked to say which one they would choose when shopping.

This method provides more in­form­a­tion­al value than a direct customer survey on a specific product. Even if a product receives a positive eval­u­ation from a customer ex­press­ing en­thu­si­asm for its in­di­vidu­al at­trib­utes, this does not mean that they would choose it over competing products. In a real-life situation, the customer will always evaluate a product by con­sid­er­ing all of its at­trib­utes and comparing them with those of competing products. The cost-benefit ratio also plays an important role.

Conjoint analysis recreates these con­di­tions in the customer survey and provides the company with important in­form­a­tion for product de­vel­op­ment and pricing.

Conjoint analysis requires a bit of effort. You need to be thorough when setting it up and think carefully about which product at­trib­utes to select, as well as their spe­cific­a­tions before you start so that the customer survey can provide valuable in­form­a­tion.

You will need to carefully do the following steps:

1. Product selection: Conjoint analysis is only useful for products or at­trib­utes that customers are already familiar with. When it comes to in­nov­at­ive products, the survey par­ti­cipants will lack the practical ex­per­i­ence needed to real­ist­ic­ally assess and compare the re­spect­ive at­trib­utes. This analysis method works best for everyday products.
2. Product at­trib­utes: In this step, you specify the product’s at­trib­utes, allowing the survey par­ti­cipant to make as informed a decision as possible. It is important to avoid spe­cify­ing too many at­trib­utes or ones which are too different for the in­di­vidu­al product types. Otherwise, the par­ti­cipant might just make an arbitrary decision, rendering the findings provided by the survey useless. In any case, you should include pricing since it is a deciding factor for consumers’ pur­chas­ing decisions.
3. At­trib­utes’ spe­cific­a­tions: You should also avoid providing too many at­trib­utes’ spe­cific­a­tions; otherwise, the survey par­ti­cipants may find them­selves over­whelmed when trying to compare the in­di­vidu­al products. It is better to limit yourself to about three spe­cific­a­tions and not to choose ones which are too different. You also need to take into account the target group’s living situation and (expected) pref­er­ences.
4. Survey: Once the product’s at­trib­utes and spe­cific­a­tions have been chosen, a survey is created which presents the possible choices as different product versions (i.e. stimuli). The question of whether to use images or text de­scrip­tions depends on the product and how much effort you wish to expend. In this step, you also need to decide whether to conduct the customer survey using the tra­di­tion­al hardcopy survey, on a computer or online.
5. Target group selection: Defining the target group is already one of the first things you do at the start of any new product de­vel­op­ment. A rep­res­ent­at­ive number of subjects is randomly selected from the target group and invited to par­ti­cip­ate in the survey. The par­ti­cipants then rank the choices (e.g. by using points) to indicate which of the products they are most likely to buy and which they are not likely to buy.
6. Cal­cu­lat­ing values: When eval­u­at­ing the survey, the par­ti­cipants’ choices are assessed using stat­ist­ic­al methods for mul­tivari­ate analysis. This can be done by either using the ap­plic­able stat­ist­ic­al formulas or special stat­ist­ic­al software.
7. Eval­u­ation: The cal­cu­lated values can then be used to determine which of the product’s at­trib­utes and spe­cific­a­tions are par­tic­u­larly important to the par­ti­cipants, what pricing is ap­pro­pri­ate and how changes in pricing affect demand.
8. Marketing strategies: The eval­u­ation’s findings can now be used to plan your next steps. First, you decide which at­trib­utes will be included in the product and then how to best market it to reach the target group.

This procedure describes tra­di­tion­al conjoint analysis. From this found­a­tion, other types have been developed which allow more mean­ing­ful results to be obtained from specific questions, and which also address the dis­ad­vant­ages as­so­ci­ated with the tra­di­tion­al method. These dis­ad­vant­ages consist mainly of limiting the choices to just a few product versions and the un­real­ist­ic ranking of what they are willing to buy which does not happen in real-life situ­ations.

Among the many types developed over the years, two have become standard methods of mul­tivari­ate analysis:

Adaptive conjoint analysis (ACA) is a computer-aided method in which par­ti­cipants are asked ad­di­tion­al spe­cial­ised questions based on their choices. The next question or choice shown is based on their answer to the previous question. The questions are therefore tailored in­di­vidu­ally to each par­ti­cipant through­out the customer survey. This means the products provided as choices never show all the possible at­trib­utes. Instead, the pre­vi­ously selected at­trib­utes are compared with new at­trib­utes in the next question. This allows the computer to learn the par­ti­cipants’ pref­er­ences and to obtain mean­ing­ful in­form­a­tion which is useful for marketing by providing relevant ad­di­tion­al questions.

Choice-based conjoint analysis (CBC) takes into account economic behaviour and decision-making theory by present­ing products with all their at­trib­utes. The par­ti­cipant can only choose one product with all its at­trib­utes in each step of the survey. Unlike in tra­di­tion­al conjoint analysis, the par­ti­cipant does not rank the choices. This makes CBC the best choice for sim­u­lat­ing a real-life shopping situation which is why it is currently the most fre­quently used analysis method.

The following are ad­di­tion­al types of conjoint analysis:

• Limit conjoint analysis (LCA)
• Hier­arch­ic­al in­di­vidu­al­ised limit conjoint analysis (HILCA)
• Multi-rule conjoint analysis (MRC)
• Choice-based conjoint analysis with hier­arch­ic­al Bayes es­tim­a­tion (CBCHB)

To recap the procedure, we would like to end this article with an example of conjoint analysis:

A company wants to launch a new fruit juice beverage on the market and they want to know which product design is likely to be suc­cess­ful be­fore­hand. To do so, it conducts a conjoint analysis of the target group of people living in urban areas between the ages of 25 and 40 with a steady average income. Using this analysis, the company hopes to determine which at­trib­utes are important to potential customers and what price they are willing to pay for them.

The product manager defines three at­trib­utes she wants to examine using the analysis method: fruit content, packaging and pricing. For each of these, she selects three different spe­cific­a­tions resulting in the following table:

From all the possible com­bin­a­tions, she again selects three, which she presents to the par­ti­cipants in the customer survey.

• Profile 1: glass bottle (deposit), 55% fruit content, £3.49
• Profile 2: plastic bottle (deposit), 100% fruit content, £3.99
• Profile 3: carton (re­cyc­lable), 35% fruit content, £2.99

The par­ti­cipants must now rank the three product profiles based on their own pref­er­ences. This is the best way to simulate a real-life situation since customers will be eval­u­at­ing each product based on all their at­trib­utes when shopping.

Once the customer survey has been completed, the data is analysed and converted into utility values using stat­ist­ic­al methods. These values reflect the im­port­ance of each in­di­vidu­al specified attribute (i.e. the partial utility value) when it comes to the customer’s pur­chas­ing decision.

Con­duct­ing conjoint analysis on the different versions of the fruit juice beverage results in the following partial utility values:

Now, the product manager can calculate the total utility value for each of the product profiles presented by adding together the partial utility values as­so­ci­ated with the cor­res­pond­ing product attribute spe­cific­a­tions.

Profile 1: 1.8 + 1.8 + 2.3 = 5.9

Profile 2: 1 + 2.6 + 1 = 4.6

Profile 3: 1.5 + 1 + 2.8 = 5.3

This results in figures which can be compared to determine which product profile is most likely to be suc­cess­ful on the market. She can also easily calculate how a change in an attribute spe­cific­a­tion may affect the utility value for consumers. Using the findings obtained through this analysis, the product can be designed to optimise its chance at success with the target group.

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