Conjoint analysis is a sophisticated market research analysis approach based on surveys that aim to understand how individuals make difficult decisions.
Every day, we make decisions that entail trade-offs, and we may not even recognize them. Even basic actions like picking a laundry detergent or booking a trip are mental conjoint studies that involve a number of factors that influence our choices.
One of the most successful approaches for capturing consumer preferences throughout the purchase process is conjoint analysis. Statistical analysis is then used to convert the data into a quantifiable measurement. It assesses goods and services in a way that no other approach can.
How does conjoint analysis work?
Conjoint analysis asks consumers to compare and contrast distinct aspects to see how they value them. When a business learns how its consumers value the qualities of its products or services, it can utilize that knowledge to build a pricing plan.
For example, a software firm aiming to develop its business by leveraging network effects can adopt a “freemium” strategy in which customers have free access to its product. If a corporation concludes through conjoint analysis that one function is more valuable to its consumers than the others, it may decide to put that item behind a paywall.
As a result, conjoint analysis is a great way to figure out which product features influence a customer’s willingness to spend. It’s a way to figure out what features a consumer is willing to pay for and if they’d pay extra for them.
Steps involved in Conjoint Analysis Design
There are a lot of qualities for a product or service area. E.g., a TV may have screen size, screen format, brand, pricing attributes, etc. You may divide each attribute into several tiers. For example, levels may be LED, LCD or plasma for screen format.
A collection of goods, Prototypes, mock-ups, or images made from a mix of levels in all or part of the component characteristics are displayed to respondents and requested to pick the items exhibited, classify, or rated. Each example has a sufficiently near replacement but not a sufficiently identical one so that respondents may readily discern their choice.
There are four steps in creating a Conjoint Design
- Determine the sort of research that will be conducted
- Determine the appropriate characteristics
- Levels of the properties must be specified
- Create a questionnaire
What is the purpose of a conjoint analysis?
Conjoint analysis is a prominent product and pricing research approach that reveals customer preferences and uses that knowledge to assist pick product features, estimating price sensitivity, anticipating market shares, and anticipating acceptance of new products or services.
Conjoint analysis is widely utilized in various sectors for a wide range of items, including consumer goods, electrical goods, life insurance plans, retirement homes, high-end items, and air travel. It may be used in various situations, including determining what sort of goods consumers are most likely to buy and what they value most (and least) about a product. It is widely used in marketing, advertising, and product management as a result.
Businesses of all sizes, including local grocery stores and restaurants, can benefit from conjoint analysis, and its application is not restricted to commercial reasons; for example, charities can utilize conjoint analysis techniques to determine donor preferences.
What is Part Worth in Conjoint Analysis?
Level utilities for conjoint qualities are referred to as Part-Worths. The utility values for the individual pieces of the product (given to the numerous attributes) are part-worths when multiple characteristics are combined to reflect the entire worth of the product concept.
What are the Types of Conjoint Analysis?
Conjoint analysis can be done in a variety of ways. The following are a few of the most common:
Choice-Based Conjoint (CBC) Analysis
This is one of the most prevalent types of conjoint analysis, and it’s used to figure out how a responder rates different aspects in different combinations.
Adaptive Conjoint Analysis (ACA)
This type of analysis personalizes each respondent’s survey experience based on their responses to the first few questions. It’s frequently used in studies when several traits or traits are being evaluated to speed up the process and get the most useful information out of each responder.
Full-Profile Conjoint Analysis
This type of study gives the responder a list of complete product descriptions and asks them to choose the one they’d be most likely to buy.
MaxDiff Conjoint Analysis
This type of study gives the responder a variety of alternatives, which they must rank on a scale of “best” to “worst” (or “most likely to purchase” to “least likely to purchase”).
The aims driving the study (i.e., what does it hope to learn?) and, presumably, the sort of product or service being reviewed define the sort of conjoint analysis a corporation does. To make use of the benefits of each, different conjoint analysis methods can be combined into “hybrid models.”
Menu-Based Conjoint Analysis
Menu-based conjoint analysis is a new type of analysis that is quickly gaining popularity in the marketing industry. Because menu-based conjoint analysis allows each respondent to package their own product or service.
Conjoint can assist you with pricing, product features, product combinations, package bundling, and more. Conjoint is useful because it resembles real-world purchasing situations in which respondents must choose between two options
Two-Attribute Tradeoff Analysis
The oldest way of co-collecting data may be the result of a number of tradeoff tables (two attributes at a time) with respondents classifying their preferences for the different levels of the attributable combinations. If two qualities have three levels each, the table will have nine cells, and respondents will score their tradeoff preferences from 1 to 9.
The two-factor-at-a-time method requires little cognitive effort from the respondent and is straightforward to use, but it is time consuming and boring. In addition, responders typically lose their position at the table or build a stylized design to complete the task. However, above all, the job is unrealistic since genuine options are not two qualities at a time to be evaluated.
Hierarchical Bayes Analysis (HB)
Hierarchical Bayes Analysis (HB) is also used to estimate the level of attributes of selected data. In circumstances where the data collecting work is so extensive, HB is particularly beneficial because a responder cannot appropriately evaluate preferences at all the attributes.
For each person, hierarchical Bayes concentrates the individual responder measurement on very variable attributes as part of the technique to estimate level utility attribute levels and employs sample attribute levels when the attribute level variability is low. Again, with fewer data obtained from each individual responder, this methodology permits other qualities and levels to be inferred.
- Consumers make psychological tradeoffs when assessing many features simultaneously, according to estimates.
- May assess personal preferences on a one-to-one basis.
- Identifies true or hidden drivers that may not be obvious to responders.
- Simulates a real-life decision or purchasing task.
- Able to interact with tangible items.
- Can represent interactions between characteristics if properly constructed.
- When using models that detect respondent variability in tastes, it may be utilized to build needs-based segmentation.
Disadvantages of using Conjoint Analysis
- Conjoint study design can be difficult.
- Respondents frequently use simplification tactics when faced with too many product features and characteristics.
- Because there isn’t one, it’s impossible to employ for product positioning study.
- Converting real features to perceptions of a smaller set of underlying characteristics is a method.
- Respondents are unable or may be able, to define their sentiments regarding new categories.
- feel compelled to consider matters they would not have given much attention to otherwise.
- Studies with a poor design may overvalue emotionally charged product aspects while undervaluing actual characteristics.