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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.
What is conjoint analysis?
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?
The customer survey will light the way
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.
The procedure for traditional conjoint analysis
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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
In principle, conjoint analysis can also be used to evaluate services. However, these services need to be standardised and not individually adapted for the customer.
Popular types of conjoint analysis
Among the many types developed over the years, two have become standard methods of multivariate analysis:
Adaptiveconjoint 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)
Example of conjoint analysis
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:
|Fruit content||Packaging||Price per 500 ml|
|35%||Glass bottle (deposit)||£2.99|
|55%||Plastic bottle (deposit)||£3.49|
|100%||Carton made from recyclable materials||£3.99|
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:
|Packaging||Glass bottle||Plastic bottle||Carton|
|Partial utility value||1.8||1||1.5|
|Partial utility value||1||1.8||2.6|
|Partial utility value||2.8||2.3||1|
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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