Conjoint analysis is a market research tool that measures consumers’ value on certain products or services. Market researches can be undertaken perhaps via surveys, which can be rating, ranking, or choice-based.
Consumers make many buying decisions daily, and each decision involves a process of deliberation where multiple attributes affect the outcome.
Purchasing a house is a classic example, where consumers must consider attributes ranging from historical interest rates to the quality of local schools.
What’s more, different consumers will be sensitive to different attributes.
One might view price as the most important consideration, while another may consider a wine cellar to be non-negotiable.
To turn these preferences into quantitative data, conjoint analysis should be performed.
This is done in the form of a survey.
The survey provides valuable insight into what a consumer wants in a product or service and what they are willing to spend to get it.
For example, the consumer wanting to purchase a house might require an ocean view.
As a desired attribute, the consumer is willing to pay a higher price to attain it.
Methods of Conjoint Analysis
Conjoint analysis employs various techniques to elicit and analyze consumer preferences:
1. Choice-Based Conjoint (CBC):
CBC is one of the most common and widely used conjoint analysis methods. It presents respondents with a set of product profiles, each with a unique combination of attributes and levels. Respondents are asked to choose their preferred product from each set.
By analyzing the choices made by respondents across multiple sets, researchers can estimate the relative importance of each attribute and level and calculate part-worth utilities, which quantify the perceived value of each attribute level.
2. Profile-Based Conjoint:
In profile-based conjoint analysis, respondents evaluate and rate individual product profiles based on their preferences. Respondents assign ratings or scores to each profile, indicating their likelihood of choosing that product.
Researchers then use these ratings to estimate part-worth utilities and assess the importance of different attributes and levels.
3. Adaptive Conjoint Analysis (ACA):
ACA is an interactive method that adapts the choice sets presented to respondents based on their previous choices. It is designed to efficiently identify individual-level preferences and determine the optimal product configuration.
ACA starts with an initial set of attribute-level combinations and refines them based on the respondent’s choices, converging on the most preferred product.
4. MaxDiff (Maximum Difference Scaling):
MaxDiff is a variation of conjoint analysis that focuses on identifying the most and least important attributes and levels. Respondents are presented with sets of products and asked to indicate the best and worst attributes or levels in each set.
This approach is useful when researchers want to understand the relative importance of attributes rather than specific part-worth utilities.
Applications of Conjoint Analysis
Conjoint analysis has a wide range of applications across industries:
1. Product Development:
Businesses use conjoint analysis to design products or services that align with consumer preferences. It helps identify the optimal combination of features, pricing, and branding.
2. Pricing Strategies:
Conjoint analysis assists in determining the price elasticity of products and services. It helps businesses find the right balance between price and perceived value to maximize profitability.
3. Market Segmentation:
By analyzing consumer preferences, conjoint analysis enables businesses to identify distinct market segments with specific product preferences.
4. Advertising and Promotion:
Marketers use conjoint analysis to evaluate the impact of different advertising messages, promotional offers, and packaging designs on consumer choices.
5. Policy and Public Services:
Conjoint analysis can inform policy decisions and the design of public services by assessing citizen preferences for various policy options.
6. Healthcare:
In healthcare, conjoint analysis helps assess patient preferences for treatment options, healthcare plans, and medical services.
Conjoint analysis survey types
Businesses new to conjoint analyses should know that there are three ways to collect survey data:
Rating-based
Where participants give each attribute of a product or service a rating according to a predetermined scale.
Most commonly, this is a scale of 1-100.
Ranking-based
Where each element is ranked from most desirable to least desirable.
In a slight variation of the ranking-based survey, participants choose from a list of their most and least favorite elements and exclude the rest.
Choice-based
Where participants are presented with combinations of attributes and asked to choose the most relevant to their needs.
Choice-based questionnaires are a great way to gauge interest in a theoretical product or service, saving businesses the time and money from having to develop them first.
Identifying attributes in conjoint analysis
In identifying attributes, less is more.
When consumers are asked to assess more than 5 or 6 simultaneously, they suffer from cognitive overload and the integrity of the results is compromised.
Once attributes have been determined, levels must be assigned to each. For a home-buyer wanting to reduce commute time, the attribute may be split into values of 10, 20, and 30 minutes.
Regardless of the product or service undergoing the analysis, levels should provide tiered value to the consumer.
Conjoint analysis survey best practices
Poor question selection has the potential to render a survey worthless. Here are some best practices to ensure effectiveness:
Set expectations
That is, explain to the consumer what they need to do to answer the survey questions properly.
Ensure that questions are clearly worded and unambiguous.
Start with screener questions
Such as age, income, job title, or other demographic information.
By collecting this data upfront, businesses can determine whether consumers are a good fit for their products and adjust accordingly.
Consider the question structure
Questions should logically follow on from one another and be grouped thematically.
Wherever possible, pose situational questions.
Instead of simply asking a home buyer to choose from a list of houses, ask them what they would have done differently about their last home purchase.
Interpreting conjoint analysis results
Once the data has been collated, there are several ways to analyze it. Many businesses opt to use Microsoft Excel.
Others may opt to use software such as Qualtrics which offers survey data collection and analysis in the one package.
In either case, it’s important to analyze data in such a way that useful conclusions can be drawn. This helps guide future marketing decisions and guides product innovation by identifying features that need improvement or removal.
Conjoint analysis simulates real-world purchase decisions, providing insights into how consumers prioritize attributes when making choices.
2. Quantitative and Actionable Insights:
The results of conjoint analysis are quantifiable, allowing businesses to make data-driven decisions and optimize product offerings.
3. Attribute Importance Ranking:
Conjoint analysis helps identify the most critical attributes and levels, enabling businesses to focus on what matters most to consumers.
4. Customization for Various Industries:
Conjoint analysis can be tailored to suit different industries and research objectives, making it a versatile tool.
5. Efficient Resource Allocation:
By understanding consumer preferences, businesses can allocate resources effectively, optimizing marketing spend and product development efforts.
Limitations and Considerations
While conjoint analysis is a powerful tool, it comes with limitations:
1. Simplification of Reality:
Conjoint analysis simplifies complex purchasing decisions, and real-world choices may involve additional factors not captured in the analysis.
2. Assumption of Rationality:
The method assumes that consumers make rational decisions based on utility maximization, which may not always reflect actual consumer behavior.
3. Data Collection Challenges:
Conducting conjoint analysis can be resource-intensive, and collecting and analyzing data from a representative sample of respondents is crucial for accurate results.
4. Limited Context:
Conjoint analysis focuses on attribute-level trade-offs and may not consider broader contextual factors that influence consumer choices.
Case studies
Smartphone Purchase: When deciding on buying a new smartphone, consumers might consider attributes such as battery life, camera quality, screen size, brand reputation, and price. Using conjoint analysis, a tech company might discover that while younger consumers might prioritize camera quality, older consumers might prioritize battery life. This insight can be used to tailor marketing messages or even design phones targeted at specific segments.
Choosing a Holiday Package: A traveler might have to choose between different holiday packages based on attributes like destination, duration, activities included, accommodation type, and price. A travel agency could use conjoint analysis to find out which attributes are the most attractive to different types of travelers, allowing them to create tailored packages.
Picking a Streaming Service: Consumers might choose a streaming service based on the variety of content, user interface, price, or the availability of exclusive shows. Conjoint analysis can help streaming services understand which attributes to invest in, whether it’s acquiring more content or improving their user interface.
Selecting a Car: When buying a car, attributes such as fuel efficiency, safety features, brand reputation, design, and price come into play. Car manufacturers can use conjoint analysis to gauge which features are non-negotiable for consumers and which ones can be positioned as premium features.
Choosing a Health Insurance Plan: Attributes might include the range of medical services covered, monthly premium, co-pay amount, choice of doctors/hospitals, and additional benefits like dental or vision coverage. Conjoint analysis can help insurance companies design plans that are more appealing to different segments of the population.
Selecting a University or College: Students might consider attributes such as campus facilities, reputation, course variety, distance from home, tuition fees, and post-graduation opportunities. Conjoint analysis can help educational institutions understand what students value most when making their decision.
Buying Athletic Shoes: For someone buying running shoes, attributes like cushioning, weight, durability, brand, and design might be essential. A sportswear company could use conjoint analysis to determine which features are most crucial for their target market and develop shoes accordingly.
Choosing a Restaurant: For a dining experience, attributes might include type of cuisine, ambiance, price range, location, and reviews. Restaurants can use conjoint analysis to determine which aspects of their service are most vital for diners and focus on enhancing those.
Selecting a Credit Card: Consumers might evaluate credit cards based on attributes like interest rates, rewards program, annual fee, credit limit, and brand reputation. Conjoint analysis can help banks and financial institutions design credit card offers that resonate more with potential customers.
Picking an Online Course: Learners might choose online courses based on course content, instructor reputation, duration, certification provided, and cost. Conjoint analysis can help e-learning platforms understand which attributes potential students prioritize.
Key takeaways
Conjoint analysis is a survey and statistics-based means of performing market research with a focus on product or service features.
Conjoint analysis survey data can be collected in three different methods based on ratings, rankings, or choice. Each method asks the consumer to score product features (attributes) according to relative desirability.
Conjoint analysis is only as robust as the questions used in the survey. Each question must be clearly worded, relevant, situational, and follow in a logical sequence.
Key Highlights
Conjoint Analysis: A market research tool that quantifies consumers’ preferences and values on product or service attributes.
Consumer Decision Making: Consumers consider multiple attributes when making buying decisions, and their preferences can vary based on individual preferences.
Survey Types: Conjoint analysis can be done using rating-based, ranking-based, or choice-based surveys to understand consumer preferences.
Attributes and Levels: In conjoint analysis, attributes are identified, and levels are assigned to each attribute. Less than 5 or 6 attributes are recommended to avoid cognitive overload.
Survey Best Practices: To ensure survey effectiveness, set expectations, use screener questions, create logically sequenced and situational questions.
Data Analysis: After data collection, various methods like Microsoft Excel or software like Qualtrics are used to analyze the data. Useful conclusions guide marketing decisions and product innovation.
Importance: Conjoint analysis provides valuable insights into consumer preferences and willingness to pay, enabling businesses to understand product attributes that matter most to consumers. It aids in product development and marketing strategies.
Conjoint Analysis
Description
Analysis
Implications
Applications
Examples
1. Define Attributes and Levels (DAL)
Conjoint Analysis begins by defining the attributes and levels that describe the product or service being studied.
– Identify the key attributes (features) that influence consumer choices. – Specify the various levels or variations of each attribute.
– Ensures a clear understanding of the product or service features under consideration. – Defines the scope of the analysis and the factors to be evaluated.
– Defining product attributes for a new smartphone, including screen size, battery life, and camera quality. – Identifying hotel attributes for a hospitality study, such as location, room size, and amenities.
Attributes and Levels Example: Selecting attributes like price, brand, and screen size, each with multiple levels.
2. Create Choice Sets (CC)
Create choice sets or scenarios that present respondents with different combinations of attributes and levels.
– Generate sets of product or service profiles by combining attributes and levels systematically. – Ensure that the choice sets represent a range of realistic product configurations.
– Provides respondents with scenarios resembling real purchase decisions. – Enables the collection of preference data based on trade-offs between attributes.
– Developing choice sets for a survey on consumer preferences for laptops with varying specifications. – Designing scenarios for a transportation study, presenting different car options with features like price and fuel efficiency.
Choice Sets Example: Creating sets of smartphone profiles with varying combinations of screen size, battery life, and camera quality.
3. Collect Data through Choice Experiments (CE)
Collect data by having respondents evaluate and rank the choice sets presented to them, indicating their preferences.
– Administer choice experiments or surveys to respondents, presenting them with choice sets. – Record their selections, rankings, or ratings for each choice set.
– Gathers preference data by observing respondents’ choices and trade-offs between attributes. – Captures insights into consumer preferences and willingness to pay for specific attributes.
– Conducting online surveys where participants rank different product profiles based on attributes like price and features. – Collecting preference data for hotel room bookings by presenting respondents with various options.
Data Collection Example: Analyzing survey responses to determine which smartphone attributes influence purchase decisions.
4. Perform Conjoint Analysis (CA)
Perform Conjoint Analysis to analyze the data collected and derive insights into consumer preferences and trade-offs.
– Utilize statistical techniques, such as regression analysis or choice modeling, to estimate the importance and utility values of attributes and levels. – Generate part-worth utilities or preference scores for each attribute level.
– Quantifies the impact of attributes and levels on product or service choices. – Identifies which attributes have the greatest influence on consumer decisions.
– Analyzing survey data to determine the most influential factors in consumer choices among various car models. – Deriving insights into customer preferences for hotel bookings by assessing the importance of location, price, and amenities.
Conjoint Analysis Example: Calculating part-worth utilities for smartphone attributes to understand their relative importance.
5. Interpret and Report Findings (IR)
Interpret the findings and generate reports that communicate the insights derived from the Conjoint Analysis.
– Analyze the utility values, attribute importance rankings, and trade-off patterns to draw meaningful conclusions. – Prepare reports or presentations summarizing the key findings and their implications.
– Provides actionable insights for product design, pricing, and marketing strategies. – Informs decision-making by highlighting which attributes should be emphasized or improved.
– Creating a report for a smartphone manufacturer outlining the most influential features to prioritize in their next product release. – Presenting findings to a hotel management team to guide their marketing and pricing strategies based on customer preferences.
Findings Report Example: Communicating that battery life and camera quality are the most important factors influencing smartphone purchase decisions.
6. Implement Strategies and Decisions (ISD)
Implement strategies and decisions based on the insights gained from Conjoint Analysis to optimize product offerings.
– Use the insights to make informed decisions about product design, pricing, bundling, or marketing strategies. – Apply the findings to align offerings with consumer preferences and maximize market share or profitability.
– Guides product development by emphasizing attributes that drive consumer choices. – Supports pricing strategies by identifying the perceived value of specific features.
– Adjusting the design of a car model to focus on attributes that consumers prioritize, such as safety features and fuel efficiency. – Setting pricing tiers for a software subscription service based on attribute importance and willingness to pay.
Strategy Implementation Example: Introducing a smartphone with enhanced camera capabilities and marketing it as a key selling point.
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Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
