Definition of Conjoint analysis

What is Conjoint Analysis?

Conjoint analysis is a well-liked approach to product and pricing research that identifies customer preferences and makes use of that knowledge to choose product features, evaluate price sensitivity, anticipate market shares, and foretell consumer acceptance of new goods or services. Conjoint analysis is often utilised for all sorts of items, including consumer items, electronic goods, life insurance policies, retirement communities, luxury goods, and air travel, across several sectors. It may be used in many situations that revolve around learning what kind of product customers are most likely to purchase and what features consumers value the most (and least) in a product. As a result, it is widely used in product marketing, promotion, as well as advertising. Even small local businesses like grocery stores and restaurants may advantage from the conjoint analysis. Its applications are not simply restricted to for-profit organisations though; for instance, charities can utilise conjoint analysis techniques to determine contributor preferences.

How to work conjoint analysis?

A conjoint study of a company's product characteristics can yield valuable information that can be used in a variety of ways. Conjoint analysis frequently has an influence on plans for pricing strategy, sales and marketing initiatives, and research and development.

Pricing Conjoint Analysis

Conjoint analysis asks consumers to evaluate various aspects side-by-side in order to gauge their opinion of each one. A business may utilise the knowledge of how its consumers value the attributes of its goods or services to create a pricing plan.

For instance, a software company that wants to grow its business by using network effects can adopt a "freemium" business plan that allows consumers to use the product for free. The corporation can decide to put a feature behind a paywall if conjoint research reveals that users value one feature more than the others. Therefore, conjoint analysis is a great tool for figuring out which product characteristics affect a customer's readiness to spend. It's a way to find out what features customers are prepared to pay extra for and what features they don't mind paying for.

Conjoint Analysis in Sales & Marketing

Conjoint analysis can influence a company's marketing and sales efforts in addition to its price approach. When a business is aware of the aspects that consumers find most valuable, it may emphasise those features in its advertising material, promotions, as well as commercials. On the other hand, a business can discover that its consumers' assessments of the worth of certain characteristics vary widely. Conjoint analysis may be a potent tool in this situation to segment clients according to their preferences and value of traits, enabling more precise communication. For instance, a chocolate-selling online retailer may discover through conjoint analysis that its consumers value quality and the knowledge that a percentage of every purchase goes toward supporting environmental sustainability initiatives.

Conjoint Analysis in Research & Development

The research and development pipeline of a corporation can benefit from conjoint analysis. The information gathered may be used to decide whether new features should be included in the company's goods or services as well as whether there is enough desire for a completely new product. Take a smartphone maker as an example, which performs a conjoint analysis and learns that its consumers choose bigger displays over all other characteristics. With this knowledge, the business can reasonably deduce that creating bigger displays would be the greatest use of its resources and product development budget. However, if further assessments show that consumer value has switched to a new characteristic, such as audio quality, the business may utilise that knowledge to change its original intentions for product development.

Why is conjoint analysis useful?

Conjoint analysis is regarded by researchers as the top survey technique for identifying consumer values. In order to predict clients' purchase decisions based on the response analysis, surveys are created, distributed, and analysed among customers. In order to understand customer behaviour, a conjoint analysis may automatically produce and evaluate numerical quantities. Our programme examines replies to determine how much importance respondents give to price, features, location, and other criteria. This data is then connected by the programme to customer profiles. Instead of producing a hypothesis, a software-driven regression analysis of data collected from actual consumers produces an accurate report. Your company has the best opportunity of producing a good or service that satisfies all the demands and desires of your clients if you have reliable, accurate data. The most prevalent type of conjoint analysis at the moment is choice-based conjoint analysis.

Examples of Conjoint Analysis?

We will suppose that the item in this conjoint research example is a cell phone. Apple, Samsung, and Google are the rivals. The company must comprehend how various clients value characteristics like brand, pricing, screen size, and screen resolution. With this knowledge, businesses may tailor their product offering to the needs of customers. The conjoint analysis creates realistic alternatives and solicits user evaluations to give value to various product features and levels.

It lets companies to use client data to make decisions based on accurate insights about consumer or client behaviour. As a result, they may create superior business plans that provide them with a competitive advantage. Profitably demands businesses to completely grasp which features of their product and service are most valued in order to satisfy client needs.

How do you calculate conjoint analysis?

Conjoint study sample sizes typically vary from 150 to 1,200 participants. If the goal of your study is to compare groups of respondents and find significant variation, one should pick a sample size that can hold at least 20 participants per group. Consequently, it would be good to include, at a least, 4 200 = 800 respondents if you are doing segmentation research and intend to split respondents into as many as four groups (i.e., using cluster analysis). This naturally expects that the final group sizes will be roughly identical, therefore further information is typically desired. The more extensive segmentation studies have at least 800 respondents.For robust quantitative research method where one does not intend to compare subgroups, I would recommend at least 300 respondents. For investigational work and developing hypotheses about a market, between thirty and sixty respondents may do.