44. Gail was participating in a market research study, and she was given 20 pairs of brands ofshampoo and asked to indicate which pair is most similar, which is second most similar, andso forth until all pairs were ranked. Which type of indirect measurement technique used toassess Gail's evaluative criteria does this represent?A.perceptual mappingB. conjoint analysisC. evaluative mappingD. regression analysisE. factor analysis Conjoint analysis is a popular method of product and pricing research that uncovers consumers’ preferences and uses that information to help select product features, assess sensitivity to price, forecast market shares, and predict adoption of new products or services. Show Conjoint analysis is frequently used across different industries for all types of products, such as consumer goods, electrical goods, life insurance plans, retirement housing, luxury goods, and air travel. It is applicable in various instances that centre around discovering what type of product consumers are likely to buy and what consumers value the most (and least) about a product. As such, it is commonplace in marketing, advertising, and product management. Businesses of all sizes can benefit from conjoint analysis, including even local grocery stores and restaurants — and its scope is not just limited to profit motives, for example, charities can use conjoint analysis’ techniques to find out donor preferences. How does conjoint analysis work?Conjoint analysis works by breaking a product or service down into its components (referred to as attributes and levels) and then testing different combinations of these components to identify consumer preferences. For example, consider a conjoint study on smartphones. The smartphone is sorted into four attributes which are further broken down into different variations to create levels: Here’s how the combination of these attributes and levels may appear as options to a respondent in a conjoint choice task: Going further than simply asking respondents what they like in a product, or what features they find most important, conjoint analysis employs a more realistic approach: asking each respondent to choose between potential product concepts (or alternatives) formed through the combination of attributes and levels. These combinations are carefully assembled into choice sets (or questions). Each respondent is usually presented with 8 to 12 questions. The process of assembling attributes and levels into product concepts and then into choice sets is called experimental design and requires extensive statistical and mathematical analysis (done automatically by Conjointly or manually by researchers). Using survey results, it is possible to calculate a numerical value that measures how much each attribute and level influenced the respondent’s choices. Each of these values is called a “preference score” (AKA “partworth utility” or “utility score”). The below example shows preference scores for attributes and levels of a mobile phone plan. Preference scores are used to build simulators that forecast market shares for a set of different products offered to the market. By using the simulator to model (i.e. simulate) respondents’ decisions, we can identify the specific features and pricing that balance value to the customer with cost to the company and forecast potential demand in a competitive market situation. The below example shows how different data amounts in a mobile plan will affect a company’s market share. Why do conjoint analysis with Conjointly?Conjointly automates the often complicated experimental design process using state-of-the-art methodology. This gives you control over specific settings, such as the number of concepts per choice set and the number of choice sets per respondent when you set up a conjoint analysis experiment. Respondents then complete the choice tasks within the conjoint survey – this typically requires a few hundred responses but may vary depending on the complexity of the study. Once we’ve gathered the recommended sample size of respondents, Conjointly produces a survey report which contains several in-depth outputs. The outputs of Brand Specific Conjoint, Generic Conjoint, and Brand-Price Trade-Off include estimates of respondents’ preferences, overall sample profile, segmentation and interactive simulations. Conjointly estimates and charts preference shares, revenue projections, and price elasticity using simulators. There are many types/flavours of conjoint analysis, classified by response type, questioning approach, and design format. All flavours of conjoint analysis have the same basics but not all are as effective as others. That’s why Conjointly offers two key conjoint designs, called generic and brand-specific, and uses the most tested, developed, and theoretically sound response type – choice-based conjoint analysis (CBC). CBC’s predictive power far surpasses its alternatives, such as SIMALTO and self-explicated conjoint, making it the ideal choice for your next experiment. Don’t have a large marketing budget or the scope to conduct conjoint analysis? That’s OK: Conjointly does full conjoint analysis for you, affordably. Unlike desktop software tools, Conjointly does not require you to deep dive into the advanced methodology of conjoint analysis. Your business can rely on the full functionality of the software to deliver high-quality analysis and powerfully accurate results. Conjointly embodies an agile approach that puts you in control of the research process without the need. Conjointly is made unique by the following characteristics:
Our support team is ready to help with you with your studies if you need any assistance. OutputsConsider you are launching a new product and wish to address several research questions. Through the below example, we demonstrate how various outputs from your Conjointly survey report can be used to gain insights.
History of conjoint analysisConjoint analysis has its roots in academic research from the 1960s and has been used commercially since the 1970s. In 1964, two mathematicians, Duncan Luce and John Tukey published a rather indigestible (by modern standards) article called ‘Simultaneous conjoint measurement: A new type of fundamental measurement’. In abstract terms, they sketched the idea of “measuring the intrinsic goodness of certain characteristics of objects by measuring the goodness of an object as a whole”. The article did not mention data collection, products, features, prices, or other elements that we associate with conjoint analysis today, but it spurred academic interest in the topic and perhaps gave rise to the name “conjoint”. It not only kick-started the topic but also set the tone for future developments in the area. Over time, it has become technical to the point of inaccessibility to most people, led by American academics with a strong emphasis on the statistical workings of survey research. Green and Srinivasin (1978) agree that the theory of conjoint measurement was developed in Luce and Tukey’s paper but that “the first detailed, consumer-orientated" approach was Green and Rao’s (1971) ‘Conjoint Measurement for Quantifying Judgmental Data’. In 1974, Professor Paul E. Green penned ‘On the Design of Choice Experiments Involving Multifactor Alternatives’, cementing the impact of conjoint analysis in market research. Over the next few decades, conjoint analysis became an increasingly popular method across the globe with notable studies in the 1980s and 90s highlighting its growing adoption and development during this time (Wittink & Cattin 1989; Wittink, Vriens, and Burhenne 1994 cited in Green, Kreiger & Wind 2001). Conjoint surveys are continuously developing on a range of software platforms, through which many different flavours of conjoint analysis can be enjoyed. Today, conjoint analysis thrives as a widespread tool built on a robust methodology and is used by market researchers daily as an indispensable tool for understanding consumer trade-offs. A simple conjoint analysis example in ExcelTo further your understanding, you can download a conjoint analysis example in Excel, also available on Google Sheets (which you can copy to edit). This example covers: This example is limited to: If you’d like to experience a real online conjoint analysis software tool, sign up to view example reports and or to create a conjoint survey. FAQsConjoint analysis is a form of quantitative research. Respondents are asked to complete surveys with a number of product concepts which are presented in choice sets. Market research helps pre-test products before launch as it is costly to release products into market without testing because of high risk of failure. Whereas non-conjoint research methods are not well-suited for taking into account key market factors (demand and competition),conjoint surveys are use a more realistic methodology which is closer to an actual buying situation. Choice-based analysis (AKA discrete choice experimentation) is a type of response used in conjoint studies where respondents are tasked with choosing which option they would buy. It is considered the most reliable method of choosing responses as it is the most realistic in a market research context. Unlike standard conjoint, in adaptive conjoint studies questions are not pre-determined and instead the survey ‘adapts’ to respondents’ choices to create each question. It is suitable for studies where there are a large number of attributes that otherwise would not fit functionally in a standard conjoint exercise. Discrete choice analysis is examination of datasets that contain choices made by people from among several alternatives. Commonly, we want to understand what drove people to make these choices. For example, how does weather affect people’s choice of eating out, ordering food delivery, or cooking at home. Choice-based conjoint is another example of discrete choice analysis. Conjoint analysis is a survey-based technique of presenting respondents with several options (each described in terms of feature and price levels) and measuring their response to these options. When the measured response is their choice between these options (rather than ranking or rating each of these options), it is called choice-based conjoint (which is the most commonly-used type of discrete choice experiments).
A partworth (AKA partworth utility or preference score) is a numerical score that measures how much each product feature influences the respondent’s selection of a particular concept. Partworth utilities (AKA preference scores) are useful in describing average preferences for your customers (or sub-groups). For example, you can find that your customers in general prefer a particular colour, flavour or price (vs. another colour/flavour/price). Partworth utilities are the key output of Generic Conjoint because they help with feature selection. Conjoint preference share simulations are useful in showing that percentage of people will choose a particular colour/flavour/price given the choice of other products with different colour/flavour/price. Simulations are the key output of Brand-Specific Conjoint and Brand-Price Trade-Off because they help in predicting adoption, revenue, price elasticity, and cannibalisation. Yes, if you use modern techniques of analysis, such as Hierarchical Bayes (default in Conjointly), you get individual-level preference scores (model coefficients). These scores can be used in clustering responses and investigating segments of buyers. The best way to learn more about conjoint analysis is to set up your own study, which you can do when you sign up. You can also read about: |