BrazilUncovering what customers want and need with conjoint analysis
For companies that want to find out exactly what their customer want, conjoint analysis is one of the most popular methods. Often conducted before an upgrade or new product launch, incorporating conjoint analysis within decision-making can provide a strategic advantage. Experts from Hammer explain how the method works and what benefit it brings.
Successful businesses know what their customers want (and need) and subsequently act on this. Finding out what your customers exactly want, however, can be quite challenging. Studies into the success versus failure rates of new products generally show that 75% to 95% of newly introduced products fail, underlining the importance of knowing your customers’ needs.
One of the most powerful research tools for identifying these needs is conjoint analysis.
What is conjoint analysis?
Conjoint analysis is a quantitative research method that enables to determine the preference and importance that customers place on various product features and features. It helps to understand how customers make (purchase) decisions.
The underlying principle of conjoint analysis is that any product can be broken down into multiple features that impact users’ perceived value. It is a statistical analysis that helps uncovering and understanding trade-offs customers would make by forcing them to choose out of several alternatives of product options/configurations.
Central in the analysis is the idea that features can increase or decrease the likelihood of an overall product offering being purchased; meaning preferences can be quantified.
With these insights, businesses can respond to the precise needs of customers by optimizing their product(s) and its elements. This results in a win-win situation for both the business and their customer.
How does conjoint analysis work?
Conjoint analysis is conducted by presenting varying product configurations (also referred to as options, bundles, etc.) as alternatives. Participants are given instructions to evaluate the product configurations and select the one they are most likely to purchase or what is most appealing. In each question, respondents must choose one of the alternatives presented to them.
Usually the number of alternatives per question is two to four. There also is the possibility to include the option ‘none’ as an alternative. This way it is not only measured which of the product offerings is most likely to be purchased, but also whether customers would actually buy it. Respondents are shown a series of choice sets and make trade-offs while proceeding through the questionnaire.
The selected alternatives give insight in which features and combinations between them are most frequently chosen. And vice versa, which configurations are less attractive to customers.
Running a conjoint analysis comprises six steps: 1) Determine the features and levels; 2) Create experimental design; 3) Create survey design; 4) Data collection; 5) Analysis; and 6) Reporting.
There are different types of conjoint analysis, each having its own specific application. The most common types are the choice-based conjoint analysis and adaptive conjoint analysis.
Application areas of conjoint analysis
Arguably the most researched topic in conjoint analysis is price elasticity/sensitivity. When pricing is the central topic, businesses can use conjoint analysis to optimize market share, profit, revenue, and to find pricing sweet spots. The insights learn businesses whether there are untapped potentials to be unlocked.
However, pricing policy isn’t the only application area of conjoint analysis. An overview of the wide range of business-related questions that conjoint analysis can help answer:
Optimal pricing policy
- How can we create freemium and paid versions based on customers’ valuation of the offerings?
- How do customers value (either relative/monetary) offerings and their separate features/features? How much are customers willing to pay for a premium feature?
- What role plays price in customer decision making?
- Are customers price sensitive and what are pricing sweet spots?
- Which product configuration should we offer to maximize revenue? And profit?
(New) product development & design
- Which specific features should be prioritized in R&D?
- Which attribute is most influential in customers’ decision making?
- How can we design ideal offerings?
Marketing/advertising strategies
- What marketing messages lead to the highest customer activation?
Packaging
- Which packaging design is most appealing?
- Does packaging impact willingness to purchase?
Competitive landscape & shares
- How does our offering compare to competitor’s offerings in terms of market shares? How does shifting in product configuration affect our market share?
- What product characteristics are most valued in the market and should we do to compete best against current offerings on the market?
- Which product configuration should we offer to maximize our customer base?
Repositioning existing products
- What are the best improvements we can make to our current products?
- Is a price increase justified (and profitable) by adding a new functionality to our product/service?
Running market simulations
To answer any of the above questions, thorough analysis of the collected data is key. A very effective way to do this is by using market simulations.
In a market simulator, various product configurations based on combinations of features and differing levels can be created in numerous scenario’s (or forecasts). This allows for running sensitivity analyses, examining customer shifting behavior, assessing which features are essential in decision making, and testing alternative strategies / options.
Businesses get answers to any ‘what if’ questions they have, based on which they can improve and optimize for instance their products, pricing, profit/revenue and customer base. In these simulations, preference data from respondents are used to compute the percentage of respondents preferring each product in a given scenario.
Simulations can be done for the total group of respondents, but also for specific segments (e.g. based on demographic data like country or age). This results in more precise insights per customer segment and provides opportunity to customize product offerings per segment.
Conclusion
In summary, conjoint analysis is a highly efficient and effective tool for uncovering the preferences of customers. After determining features and translating them into the research design, which is a crucial part of setting up the conjoint analysis, respondents choose their preferred alternative from a number of choice sets. The acquired insights can be used in a wide spectrum of (business) departments and industries.
