A conjoint analysis is part of the so-called trade-off method, in which a respondent must make a choice between different alternatives and thus express his preferences. The method therefore portrays thought-through decision-making on for example:
- Product composition: The combination of product properties preferred by customers.
- Portfolio composition: The combination of products that potentially will provide the biggest market share.
- Pricing: The optimal pricing of individual products including cost of any features and add-on options.
- Brand value: What additional charge is a customer willing to pay for a given brand in comparison to others.
- Segments: What segments are on the market and how big are they in relation to product preferences, price sensitivity etc.
When and why should conjoint analyses be applied?
A conjoint analysis is fundamentally a method of analysis that uncovers customers’ preferences between different alternatives.
More often it’s all about what specific product combination is preferred to others. Thus, the combination of different product properties is simulated to reveal which combination the consumer finds most attractive.
Apart from creating optimal product combinations and prices, conjoint analyses are also used to uncover client- or user preference in other correlations, where it is unclear what the client/user prefers:
Examples of usage of conjoint analyses:
What dishes should go on the menu?
What activities should the municipality offer?
Which transportation should you choose?
What service should be offered?
Which factors should be changed to gain customers?
What medication is the Doctor prescribing?
A conjoint analysis reflects real choices
Conjoint- or trade-off analyses in general are comparable to situations in the supermarket, where consumers are faced with a multitude of products and must make a choice. Or as management have to pick out what suppliers and product we want to use. Neither as a private customer nor as professionals do we rank or rate all the properties and benefits but simply cut straight to the core and focus on the things that really matte. Based on that we make our choice. A conjoint analysis creates the same environment and is therefore a very good reflection on real life purchasing situations.
A conjoint analysis answers:
- How many customers would actually pick the organic ketchup?
- How many would prefer a 12-hour battery life on their laptop compared to 8 hours, and what are they willing to pay for the extra 4 hours?
- Will market shares increase if a new ketchup bottle, an upside-down version, is put on the market, or will it just take shares from our existing sales?
- What is the market’s price sensitivity for the new product we are releasing?
Many have tried to obtain the answers to the mentioned questions through market analyses, where consumers through ranking, rating or pricing questions provide their preferences. The ones who have been through this process know, that these methods rarely provide applicable answers. It is simply difficult for people to express their true priorities and the balance between them when asked directly. The conjoint analysis forces you to prioritise/choose as you would in a true purchasing situation. Thus, the method provides better results and that is of course the reason for the popularity of the conjoint analysis.
Create a market simulator with data from the conjoint analysis
The output from the conjoint analysis is a simulator reflecting the market situation according to the choices made by the respondents. The simulator provides different outputs depending on the purpose of the analysis and if the main topic is i.e. a new product, several competitive products are defined and simulate how to create your own optimal product / portfolio through different feature alternatives and prices. Every change reflects a new potential market share and price sensitivity which is immediately visible.
Similarly, it is possible to simulate the competitions probable future changes in product or price and what impact those changes may have on consumer preferences, and thus prepare you to respond accordingly.
Finally, analyses can be conducted via so called Latent Class analyses that identify the preference- and price segments of the market; the information gained can be used as the foundation for further go-to-market optimisation.
Different conjoint analyses
There are a number of conjoint analysis methods each with their own strengths and possibilities:
- Adaptive conjoint analysis (ACA)
- Choice-based conjoint (CBC)
- Adaptive choice-based conjoint (ACBC)
CBC and ACBC are clearly the most commonly applied out of all the different trade-off/conjoint analyses, as seen in the table below.
On mobiles: swipe left to scroll table below.
|CBC (Choice‑based Conjoint)||35||46||54||56||59||62||68||65||68||60||65||69||69||72|
|ACBC (Adaptive Choice)||6||9||10||10||11||11||11|
|MBC (Menu-based Choice)||2||2||2||2||2|
|ACA (Adaptive Conjoint Analysis)||25||20||15||12||11||10||6||6||5||4||4||2||3||2|
|CVA (Traditional Conjoint Analysis)||10||9||8||7||6||4||4||2||3||2||2||3||1||2|
|Proprietary version of conjoint analysis||12||10||10||11||12||9||9||11||6||9||9||3||7||6|
|Other approch not listed||10||6||3||7||6||10||9||6||5||4||4||5||3||3|
|BPTO (Brand-Price Tradeoff)||3||5||3||3||3||2||2||2||2||2||2||3||1||1|
*Among respondents who reported that their company conducts trade-off/conjoint/choice or some other preference modelling”. 2014 Sawtooth Software Customer Feedback Survey
Get a short introduction to the conjoint analysis method in the video below:
Contribution has completed conjoint analyses for both large and small companies, including start-ups, within among others:
- Staple goods
- Longterm consumer goods
- Industrial equipment
Are you wondering if or how you can use the conjoint analysis method to deal with the issues you are facing, or are you interested in specific quotes please do not hesitate to contact us.
We are not only efficient and competitive, we also have a strong reputation for understanding your business.