Every product decision your customers make involves trade-offs. They weigh features against price, brand against convenience, design against durability, and dozens of other variables that interact in ways traditional research methods cannot untangle. If you are trying to optimise a product configuration, set a price, prioritize your development roadmap, or understand how customers make choices among alternatives, you need a method that captures these trade-offs with precision. Conjoint analysis is that method.
Meraki Research designs and executes conjoint analysis studies that reveal how your customers value individual product features, how they trade off those features against each other and against price, and which combinations of attributes drive the strongest preference. We go beyond academic exercises to deliver conjoint studies that directly inform real business decisions: what to build, how to price it, and how to position it against the alternatives your customers are considering.
Our team has designed conjoint studies across consumer goods, technology, healthcare, financial services, and B2B industrial categories. We know when conjoint is the right tool, when a simpler approach will serve you better, and how to design the experiment so the data actually answers the question your leadership team is asking. That practical orientation is what separates a research firm from a software vendor.
Our Approach: The Meraki Conjoint Research Process
Conjoint analysis is powerful, but it is also methodologically demanding. A poorly designed conjoint study produces results that are technically impressive and strategically useless. Our process is built to prevent that outcome.
Phase 1: Attribute & Level Definition
The foundation of any conjoint study is defining the right attributes and levels. What are the product features, specifications, and characteristics that actually drive your customers' choices? What price range is realistic? What brand names should be included? We work with your team to identify the variables that matter, informed by qualitative research, competitive analysis, and category expertise. This is not a brainstorming exercise. Poorly chosen attributes produce meaningless data. We push back when clients want to test too many features, and we ensure every attribute and level is something respondents can realistically evaluate.
Phase 2: Experimental Design & Survey Development
Once attributes and levels are defined, we build the experiment. This involves creating the specific product concepts or profiles that respondents will evaluate, using statistical design principles that ensure we can isolate the individual contribution of each attribute. For choice-based conjoint, the most common approach, we design choice sets where respondents select their preferred option among a set of alternatives, mimicking real purchase decisions.
The survey instrument itself requires careful development. How we present the concepts visually, how we frame the choice task, and how we introduce the exercise all affect data quality. We design surveys that feel intuitive to respondents rather than like a statistics experiment, because engaged respondents produce better data. This survey design expertise draws on the same principles that guide our broader survey design and programming practice.
Phase 3: Respondent Recruitment & Data Collection
The respondents in a conjoint study must represent your actual buyers or target market. We recruit samples that match your customer profile on demographics, purchase behaviour, and category involvement. For B2B studies, this means reaching the actual decision-makers, which often requires the specialized recruitment approaches we use in B2B survey research. Sample sizes for conjoint typically range from 300 to 1,000+ depending on the number of segments you need to analyse and the complexity of the design. Data collection is managed through our online survey platform with rigorous quality controls at every stage.
Phase 4: Statistical Analysis & Utility Modeling
This is the analytical core of conjoint. We estimate part-worth utilities for each attribute level, revealing exactly how much value respondents place on each feature, each brand, and each price point. We determine the relative importance of each attribute in driving choice, a metric that tells you whether customers care more about price, performance, brand, or any other variable you tested.
For choice-based conjoint, we use hierarchical Bayesian estimation that produces individual-level utilities, meaning we can analyse preferences not just for the overall sample but for specific segments. This individual-level data is what makes conjoint so powerful for market segmentation research: you can identify distinct groups of buyers who value different things and tailor your product and pricing strategy accordingly.
Phase 5: Market Simulation & Strategic Recommendations
Utilities are interesting to analysts but not immediately actionable for decision-makers. We translate the utility data into market simulations that model real-world scenarios. What happens to your market share if you add a specific feature and raise the price by 15%? What if your competitor launches a stripped-down alternative at a lower price point? How does removing a feature that is expensive to deliver affect customer preference?
Our simulation tools let you test hundreds of "what-if" scenarios, comparing product configurations, pricing strategies, and competitive responses. We present these results as clear recommendations tied to your business objectives: maximize revenue, maximize share, or optimise the trade-off between the two. The final deliverable connects directly to decisions your team is preparing to make.
Types of Conjoint Analysis We Offer
Not all conjoint approaches are the same. We select the right type based on your research questions:
- Choice-Based Conjoint (CBC): The most widely used approach. Respondents choose their preferred product from a set of alternatives in each task. Best for simulating competitive purchase decisions and estimating market share.
- Adaptive Conjoint (ACBC): Combines screening and choice tasks to handle designs with many attributes. Best when you have a complex product with many configurable features.
- Menu-Based Conjoint: Respondents build their ideal product from a menu of options, each with a price. Best for configurable services, bundles, or subscription tiers.
- MaxDiff-Augmented Conjoint: Combines MaxDiff prioritization with conjoint choice tasks. Best when you need both feature importance ranking and trade-off data.
When Conjoint Analysis Is the Right Choice
Conjoint delivers its strongest value when:
- Multiple attributes interact and you need to understand the trade-offs customers make between features, price, brand, and other variables.
- Product configuration decisions require you to choose which features to include, bundle, or offer as upgrades.
- Pricing must be optimised alongside product design, not in isolation. Conjoint is the gold standard for pricing within the broader pricing research toolkit.
- Market simulation is needed to forecast share under different competitive scenarios before committing resources.
- Segmentation by preference would reveal meaningful strategic opportunities, because different customer groups value different things.
Conjoint is not the right tool when you are testing a simple price change on an established product with no feature variation. For that, Van Westendorp or Gabor-Granger will give you faster, more cost-effective answers. It is also not ideal when your product category is so unfamiliar to respondents that they cannot make meaningful trade-off judgments. We will recommend the right approach, even when it is not the most complex one.
What We Deliver
Every conjoint study from Meraki Research produces:
- Attribute importance scores showing the relative weight of each feature, brand, price, and other variables in driving customer choice.
- Part-worth utility reports detailing the value respondents place on each level of each attribute, at both aggregate and segment levels.
- Market simulation tool configured with your data, allowing your team to model product configurations, pricing scenarios, and competitive responses on demand.
- Optimal product profiles identified through simulation, with expected share, revenue, and margin estimates under realistic competitive assumptions.
- Segment-level analysis revealing how preferences differ across customer groups, with strategic implications for product line architecture and targeted marketing.
- Executive strategy presentation translating the analytical results into clear, actionable recommendations for product, pricing, and competitive positioning decisions.
A standard choice-based conjoint study typically takes five to eight weeks from kickoff to final delivery. Adaptive or menu-based designs may require an additional one to two weeks for the more complex design and analysis work.
Industry Applications
Consumer Packaged Goods Optimize product formulations, packaging, and pricing by understanding how consumers trade off taste, ingredients, brand, pack size, and price at the point of purchase.
Technology & Software Configure subscription tiers, hardware specifications, and feature bundles that maximize adoption and revenue. Conjoint is particularly valuable for pricing products with many configurable levels.
Healthcare & Medical Devices Understand how physicians, patients, and payers trade off clinical features, ease of use, brand reputation, and cost when selecting therapies or devices.
Financial Services Design account structures, fee models, and benefit packages that attract target segments while optimising revenue per customer.
Automotive Evaluate trim levels, optional features, powertrain alternatives, and pricing tiers for vehicle configurations that maximize buyer appeal and profitability.
B2B & Industrial Quantify the value of technical specifications, service levels, delivery terms, and brand trust in complex B2B purchasing decisions where multiple stakeholders influence the choice.