5 Examples of Conjoint Analysis Studies in the Real World

Introduction to Conjoint Analysis 

Imagine you are the marketing director for a rapidly growing tech company. You’ve developed an innovative new smartphone, but you’re uncertain about which features will resonate most with your target audience.

Should you prioritize battery life, camera quality, or perhaps price? Traditional surveys might give you a sense of what people say they want, but how can you be more confident about which features truly drive their purchasing decisions? Enter the world of conjoint analysis.

Conjoint analysis is a powerful tool in marketing that helps businesses understand customer preferences by presenting real-world looking purchase scenarios and analyzing the trade-offs consumers make.

This method goes beyond simply asking what features customers like; it simulates real-world buying scenarios where they must prioritize certain features over others. The insights derived from conjoint analysis studies can profoundly influence product development, pricing strategies, and marketing messages.

By utilizing conjoint studies, companies can make data-driven decisions that align closely with customer desires, leading to increased customer satisfaction and improved market performance. For instance, a well-designed conjoint analysis study might reveal that while customers express a desire for high-resolution cameras, their actual purchase decisions are more heavily influenced by battery life and pricing. This kind of nuanced insight is invaluable for making strategic business decisions.

Whether you are a seasoned marketing researcher or new to the field, understanding the power of conjoint analysis can transform the way you approach customer research and product development.

What Is a Conjoint Analysis Study? 

Conjoint analysis is a survey-based statistical technique used in marketing research to determine how people value different features (and often prices) that make up a product or service.

The method forces respondents to make trade-offs, mimicking real-life decision-making processes. This approach helps in identifying which attributes are most influential in the consumer’s choice and quantifies their relative importance.

One of the primary benefits of conjoint analysis is its ability to reveal the true preferences of consumers by having them evaluate various product combinations. Unlike traditional surveys that ask about each feature in isolation, conjoint studies present respondents with a set of products or services that vary in several attributes, often including price.

This setup requires respondents to make choices, as if they were buying whole products/services, thereby uncovering their implicit preferences and the trade-offs they are willing to make.

A typical conjoint analysis study involves several steps:

1. Selection of Attributes and Levels: Identify the key features (attributes) of the product and their variations (levels).
2. Design of Survey: Create survey questions that present different combinations of these attributes.
3. Data Collection: Administer the survey to a targeted sample group.
4. Data Analysis: Use statistical techniques to analyze the responses and determine the importance of each attribute and level.

For example, in a conjoint analysis study for a new smartphone, respondents might be shown several phone options with varying battery life, camera quality, screen size, and price.

They would be asked to choose which option they prefer out of each set. The collected data would then be analyzed to identify which features are most critical to consumers and how much they are willing to pay for each feature.

By leveraging conjoint analysis, businesses can gain deep insights into customer preferences, optimize their product offerings, and make strategic decisions that enhance market competitiveness.

This method’s ability to simulate real-world choices makes it an invaluable tool in the toolkit of marketing researchers and business strategists.

Five Examples of Conjoint Studies 

Example 1: Courtyard by Marriott 

Source: Courtyard by Marriott Designing a Hotel Facility with Consumer-Based Marketing Models

In the 1980s, Marriott Corporation faced a challenge in sustaining growth due to a shortage of prime locations for traditional Marriott hotels. To address this, Marriott sought to capture the attention of two key customer segments: frequent business travelers and occasional leisure travelers. Realizing that these segments valued specific hotel features, Marriott decided to design a new type of hotel to meet their needs and differentiate from competitors.

To achieve this, Marriott engaged pioneering researchers Paul Green and Jerry Wind, leading academics and also experts in conjoint analysis—a method that reveals consumer preferences for product features. Through preliminary research with business and leisure travelers, Marriott identified seven critical aspects of hotel design that significantly influenced customer satisfaction: External factors, Room quality, Food services, Lounge facilities, General services, Leisure amenities, and Security. Green & Wind designed a conjoint analysis that provided Marriott with price insights into the features valued most by their target customers, offering a blueprint for the new hotel concept.

Following these insights, Marriott introduced the Courtyard by Marriott concept. Designed with the specific preferences of its target segments in mind, the Courtyard hotels offered a more streamlined, yet highly appealing experience. Features like practical room layouts, tailored food services, and targeted amenities allowed Marriott to stand out in a crowded market and appeal directly to business and leisure travelers alike.

The successful national launch of Courtyard by Marriott underscored the effectiveness of Marriott's consumer-focused strategy. This approach not only met the needs of new customer segments but also reshaped Marriott's approach to new product development. The company subsequently applied conjoint analysis across other projects, enabling Marriott to innovate in lodging and beyond, with products tailored precisely to consumer demands. Courtyard's success became a model for Marriott’s growth, validating the strategic power of data-driven customer insights in hospitality.

Example 2: Dual Sliding Doors on Honda Odyssey Minivan 

In the early 1990s, Honda faced a misstep with its first-generation Odyssey minivan. Unlike its American competitors, Honda designed the 1995 Odyssey with a car-like structure, prioritizing compact styling and forward-hinged doors. This model reflected Honda’s philosophy of “The Honda Way”—a design approach centered on customer loyalty from Accord and Civic owners. However, it missed key features minivan buyers wanted, such as increased interior space, abundant cupholders, and easy access for multiple passengers. Chrysler, meanwhile, was dominating the market with models like the Dodge Caravan, Plymouth Voyager, and Town & Country, which featured ample space and sliding doors that facilitated easy loading and unloading.

Despite innovations like the fold-down “magic seat” for added cargo flexibility, the Odyssey lacked dual sliding doors, which customers strongly preferred to avoid accidental dings in tight parking spaces. As a result, Odyssey sales struggled, reaching only 25,000 units in 1995 compared to Chrysler’s hundreds of thousands of sales. Recognizing this issue, Honda returned to the drawing board, with Honda R&D conducting a conjoint analysis using Sawtooth Software to understand customer priorities for minivan features.

The conjoint study revealed that dual sliding doors were a top priority for minivan buyers, aligning with consumer preferences for spaciousness and convenience. With this data, Honda redesigned the Odyssey’s second generation, which launched in 1999. The new model featured dual sliding doors as standard, alongside improvements in overall space, seating, and practicality.

This redesign proved highly successful, leading to significantly increased sales and a restored market presence for Honda. The second-generation Odyssey set a new standard for the brand’s minivans, demonstrating how data-driven insights through conjoint analysis can help realign a product with consumer needs, even in a competitive and established market segment.

Example 3: Apple vs. Samsung Patent Trial of the Century 

In 2012, Apple filed a high-profile lawsuit against Samsung, claiming that Samsung had copied patented elements of Apple's technology, including hardware designs and features like “bounce-back” scrolling, tap-to-zoom, and two-finger gestures. Apple sought $2.5 billion in damages, arguing that Samsung's use of these features violated intellectual property laws. After three days of deliberation, a jury initially awarded Apple slightly over $1 billion in damages, confirming Samsung’s infringement on certain Apple patents. This “Patent Trial of the Century” highlighted critical issues in patent law, technology, and intellectual property.

To support its case, Apple employed MIT marketing professor John Hauser, an expert in conjoint analysis, to quantify consumer demand for the contested features. Using Sawtooth Software’s conjoint analysis tools, Hauser conducted two preference studies—one for smartphones and one for tablets. These studies examined how much consumers valued Apple’s patented features, translating those preferences into economic estimates to justify Apple's damage claims. Hauser’s analysis used a simulation to assess Samsung’s potential revenue gain from the patented features, based on consumers' willingness to pay (WTP) for these innovations.

The case underscored the diverse applications of conjoint analysis, typically used to enhance product development and market positioning, but here deployed as a tool in high-stakes litigation. Sawtooth Software’s methodologies, often used in global contexts for varied purposes, from coral reef conservation to consumer packaged goods, took center stage in Silicon Valley’s courtroom battle over tech innovation and competitive strategy.

Example 4: Smartphone Design in the Chinese Market (VIVO Mobile) 

VIVO Mobile Communication Co., a leading Chinese smartphone manufacturer, sought to maintain its competitive edge by gaining a deeper understanding of consumer preferences in smartphone configurations, especially regarding the trade-offs between RAM and ROM. To address this, VIVO partnered with Diagaid, an insights consultancy skilled in Sawtooth Software’s conjoint analysis—a tool for assessing consumer choices and trade-offs.

The challenge was to determine the optimal combinations of RAM and ROM for VIVO’s new product line. Internal sales data hinted at an interaction effect: as RAM increased, consumer demand for additional ROM decreased. To confirm and quantify this effect, VIVO utilized Sawtooth’s conjoint analysis to make data-driven decisions based on detailed consumer preferences.

Approximately 500 respondents participated in the study, evaluating various smartphone configurations to help VIVO understand how RAM and ROM trade-offs influenced their choices. Sawtooth’s conjoint tool revealed key findings. First, it confirmed a significant interaction between RAM and ROM, indicating that as RAM increased, the importance of additional ROM diminished for consumers. The tool also demonstrated high predictive accuracy, closely matching real market behavior. For example, it accurately predicted a stronger preference for "8GB RAM + 128GB ROM" over "6GB RAM + 256GB ROM," which VIVO validated through market testing. Sawtooth’s market simulation function further allowed VIVO to simulate consumer choices and focus on the most appealing configurations, aligning closely with actual consumer behavior in both online and offline channels.

Using Sawtooth Software provided several advantages: it enabled VIVO to understand complex RAM-ROM interactions, provided accurate market forecasts, and supported strategic decisions with confidence—leading to a 16% increase in revenue over competitors. The insights allowed VIVO to optimize its product lineup by focusing on configurations that maximized consumer appeal and market performance.

By leveraging Sawtooth’s conjoint analysis, VIVO gained a robust understanding of consumer preferences, leading to successful product configurations and improved market outcomes, demonstrating the strategic value of advanced conjoint analysis tools in competitive markets.

Example 5: How the Portland Trailblazers Won Back Their Fans

In the 2005 NBA season, the Portland Trail Blazers faced a significant crisis. With a disappointing 22-36 record and a Chapter 11 bankruptcy that placed the Rose Garden arena under creditor control, the Blazers had lost both fan support and financial stability. Attendance plummeted by over 15% from 2003, and even the luxury suites sat mostly empty. The franchise’s image suffered from negative publicity surrounding players’ off-court behavior, including drug use and other issues that alienated fans. In response, the Blazers’ management initiated a strategic turnaround plan that focused on ticketing strategies, rebranding, and rebuilding fan loyalty.

A key component of this strategy involved a research study conducted by academic Ronald Wilcox using conjoint analysis—a marketing research tool that evaluates consumer preferences for product features. The study aimed to pinpoint what factors would most effectively encourage fans to return, examining elements like ticket pricing, seat locations, and multi-game package options. Management also sought insights into the effectiveness of various promotions, such as giving early access to playoff tickets or including merchandise giveaways with ticket packages.

The research revealed that ticket pricing and seat quality were of primary importance to fans, while other elements, like the number of games in a package or promotional giveaways, mattered less. One particularly effective promotion was giving fans first access to buy playoff tickets, which enhanced fan loyalty without additional costs. Conversely, jersey giveaways were deemed ineffective, as most fans already owned jerseys.

Using these insights, the Blazers implemented a targeted promotional program, improved ticket packages, and removed players with reputational issues. This approach, alongside improved team performance, rebuilt fan enthusiasm and attendance, transforming the Rose Garden (now the Moda Center) into one of the NBA’s most energetic arenas. This case exemplifies how data-driven decisions and brand rehabilitation can restore a team’s connection with its fans.