- What questions does this article answer?
- MaxDiff Analysis: Clear Priorities Even for Small Differences
- Conjoint Analysis: Evaluating Feature Combinations and Simulating Strategies
- Product Configurators: Simulating Purchase Decisions Realistically
- Likert Scale: Easily Measure Direct Assessments
- TURF Analysis: Identifying Product Combinations with Maximum Reach
Measuring Preferences with These Methods
What characteristics make a product successful? Which features are indispensable for the target audience? And how do you develop a pricing strategy that works? In market research, there are proven methods to find out precisely that: you should be familiar with MaxDiff analysis, conjoint analysis, product configurators, and Likert scales. Each of these methods offers specific advantages—depending on the question, goal, and industry.
What questions does this article answer?
- What methods are suitable for reliably measuring customer preferences?
- How can I optimally configure my product and which method supports me in doing so?
- What approaches assist in determining a marketable price for my product?
- How do MaxDiff, Conjoint, Product Configurator, and Likert Scale work in measuring preferences?
MaxDiff Analysis: Clear Priorities Even for Small Differences
When faced with many decision criteria, MaxDiff analysis helps identify the most relevant factors. Participants choose the most and least important attribute at each step. This creates an accurate ranking of key attributes without inflation of importance, as MaxDiff requires clear decisions from respondents, revealing what truly matters. The analysis provides metric data. These data show exact distances, make differences measurable, and allow for precise, comparative analyses.
When is MaxDiff analysis useful?
When many options need to be compared
MaxDiff remains clear and efficient even with a large number of items.
When everything seems equally important
The method prevents participants from agreeing to all statements without setting priorities – so-called scale inflation is specifically avoided.
When subtle differences need to be visible
Even minimally altered messages or similarly rated features can be reliably differentiated.
When direct evaluations would be biased
MaxDiff reduces social desirability and acquiescence bias as there is no traditional scale rating.
When the survey should be simple and cost-effective
The method can be easily integrated into online interviews or self-completion surveys, with minimal interview duration.
When comparable, metric results are needed
The resulting utility values clearly show how strongly individual options are preferred – objectively and directly comparable.
MaxDiff remains clear and efficient even with a large number of items.
When everything seems equally important
The method prevents participants from agreeing to all statements without setting priorities – so-called scale inflation is specifically avoided.
When subtle differences need to be visible
Even minimally altered messages or similarly rated features can be reliably differentiated.
When direct evaluations would be biased
MaxDiff reduces social desirability and acquiescence bias as there is no traditional scale rating.
When the survey should be simple and cost-effective
The method can be easily integrated into online interviews or self-completion surveys, with minimal interview duration.
When comparable, metric results are needed
The resulting utility values clearly show how strongly individual options are preferred – objectively and directly comparable.
What Works? MaxDiff Brings Clarity to the Claim Chaos
A manufacturer of hair care products wants to determine which claims on the packaging are most effective. In a MaxDiff study, consumers evaluate over 20 possible, sometimes very similar claims—such as those related to natural ingredients, environmental compatibility, or effectiveness. The analysis reveals that the phrases "Without microplastics" and "Intensively nourishes without weighing down" are significantly prioritized over the variants "Free from microplastics" and "Lightly and intensively nourished." Although the claims only vary minimally, MaxDiff highlights preference differences that often go unnoticed with scale-based surveys.
Conjoint Analysis: Evaluating Feature Combinations and Simulating Strategies
Conjoint analysis demonstrates how different product features interact. Respondents evaluate combinations of features or entire product packages, going through realistic purchasing decision scenarios. This reveals which combination of price, function, and additional service is most appealing. Conjoint analysis provides metric data, highlighting the impact of individual product features and enabling precise simulations of market shares, price reactions, and product combinations.
When is conjoint analysis useful?
When preferences need to be translated into market shares, prices, and revenues
Conjoint reveals which product features make the difference and provides data to calculate market shares, price sensitivities, as well as profit and revenue potentials.
When the interplay of product features matters
The method shows interactions between individual features and which combinations particularly convince target groups.
When purchasing behavior for predefined product offers needs to be forecasted
Conjoint analysis is useful when purchasing behavior in existing markets needs to be examined or when target groups choose between fixed product variants – such as tariffs or active ingredient combinations that cannot be arbitrarily adjusted or combined.
When competitive responses or market changes need to be simulated
Conjoint analysis shows how target groups react in dynamic market situations, for instance, when competitors adjust prices or introduce new variants.
Conjoint reveals which product features make the difference and provides data to calculate market shares, price sensitivities, as well as profit and revenue potentials.
When the interplay of product features matters
The method shows interactions between individual features and which combinations particularly convince target groups.
When purchasing behavior for predefined product offers needs to be forecasted
Conjoint analysis is useful when purchasing behavior in existing markets needs to be examined or when target groups choose between fixed product variants – such as tariffs or active ingredient combinations that cannot be arbitrarily adjusted or combined.
When competitive responses or market changes need to be simulated
Conjoint analysis shows how target groups react in dynamic market situations, for instance, when competitors adjust prices or introduce new variants.
Tabs, concentrates, top yield – how conjoint analysis aids in assortment planning
A provider of sustainable detergents is testing new product concepts with different delivery forms, active ingredient formulations, and price levels. The conjoint analysis shows: The combination of a concentrated formula and tab dosing achieves a high willingness to pay. For fine-tuning, the company simulates price-sales functions and profit curves for different product and price variants and competitive scenarios. The result: A solid basis for decision-making in product assortment planning.
Product Configurators: Simulating Purchase Decisions Realistically
Product configurators simulate real purchasing decisions. Respondents interactively assemble their desired product, such as in terms of features, design, or price. This method creates a realistic decision-making scenario while remaining user-friendly even for complex products. Many are familiar with configurators from everyday life, which ensures high acceptance and often makes participation enjoyable.
Market researchers gain more than just selection decisions. They identify differences between target groups and additionally capture behavioral data, such as which combinations are tested, when someone stops the process, or which features are particularly important. This results in deep insights into preferences, decision-making logic, and market potential.
Market researchers gain more than just selection decisions. They identify differences between target groups and additionally capture behavioral data, such as which combinations are tested, when someone stops the process, or which features are particularly important. This results in deep insights into preferences, decision-making logic, and market potential.
When are product configurators useful?
When products offer freely combinable features
Ideal for modular solutions that allow customers to configure a product according to their individual needs.When there is a wide range of options
Even with complex products that include numerous add ons, the method remains efficient and scalable.When real decision making processes need to be reflected
Configurators reveal how target groups actually make choices. Beyond stated preferences, user behavior such as click patterns, selection paths, and decision time can be analyzed.
Ideal for modular solutions that allow customers to configure a product according to their individual needs.When there is a wide range of options
Even with complex products that include numerous add ons, the method remains efficient and scalable.When real decision making processes need to be reflected
Configurators reveal how target groups actually make choices. Beyond stated preferences, user behavior such as click patterns, selection paths, and decision time can be analyzed.
Three User Types, Three Strategies – What Configurators Reveal About Target Audiences
A manufacturer uses a configurator in market research to better understand target audiences. The analysis reveals three distinct user types: those who quickly select simple presets and focus on price, others who customize deliberately and are willing to pay more for comfort features, and those who vary greatly, appear uncertain, and need support. From this, the company deduces how it can offer differentiated levels of configuration, pre-bundles, and guidance.
Likert Scale: Easily Measure Direct Assessments
The Likert scale captures opinions, evaluations, and attitudes using a graduated scale that ranges from agreement to disagreement. It is particularly well suited for quick, easy to understand surveys that deliver clearly interpretable results.
Its application goes beyond assessing technical product features. Softer factors such as brand image, personal attitudes, and emotional responses can also be measured effectively. The scale is especially useful when different product attributes vary in relevance and need to be structured accordingly. It offers a simple yet robust way to measure nuanced preferences.
When is the Likert scale useful?
When opinions or attitudes need to be measured directly
Well-suited for evaluating statements, slogans, or simple features.
When only a few aspects are in focus
Ideal for short, compact questionnaires with clear questions.
When quick evaluation is important
Responses can be immediately interpreted. No statistical models are necessary.
When analog interviews are necessary or desired
The scale is suitable for telephone or paper-pencil interviews.
When a rough estimate is sufficient
Especially in early innovation phases and initial iterations, scaled responses provide good insights into preferences and attitudes.
Well-suited for evaluating statements, slogans, or simple features.
When only a few aspects are in focus
Ideal for short, compact questionnaires with clear questions.
When quick evaluation is important
Responses can be immediately interpreted. No statistical models are necessary.
When analog interviews are necessary or desired
The scale is suitable for telephone or paper-pencil interviews.
When a rough estimate is sufficient
Especially in early innovation phases and initial iterations, scaled responses provide good insights into preferences and attitudes.
First Design Check: Likert Scale Provides Quick Orientation
A soft drink manufacturer is testing initial design drafts. The aim is to evaluate both visual design elements and messages, tone, and brand alignment. The goal is to determine how well the drafts align with the attitudes and values of consumers. Ten criteria form the basis for evaluation on a 5-point scale. The results feed directly into the next design iteration. The pragmatic research approach allows for flexible implementation in online interviews as well as in personal conversations.
TURF Analysis: Identifying Product Combinations with Maximum Reach
A strong product range requires smart portfolio decisions. Which variants should be offered? Which combinations reach the largest number of buyers? TURF analysis helps identify, from a broad set of options, the precise selection that delivers the highest market reach. TURF stands for Total Unduplicated Reach and Frequency. The method measures how many unique individuals find at least one of the offered products appealing. This makes it clear which combinations provide the greatest coverage and revenue potential.
Rather than evaluating individual products in isolation, TURF always looks at combinations. It calculates how many people are reached by a specific set of offerings, counting each person only once. This allows companies to optimize assortments, portfolios, and communication strategies with precision. In market research, TURF is widely used when resources such as shelf space, budget, or production capacity are limited. The method is particularly well suited for selecting flavors, packaging designs, or messaging concepts. Purchase frequency and projected sales volumes can also be incorporated to further refine decisions.
When is the TURF analysis useful?
- When a limited selection must be made from many offers
- When assortments or portfolios need optimization
- When target groups differ significantly in terms of preferences
- When space or advertising budgets are limited
- When sales and revenue targets need to be simulated
Fruity. Mild. Popular. How yogurt varieties are strategically combined
A dairy company intends to revamp its range of fruit yogurts. There are eight flavors to choose from, but the refrigerated shelf only has space for four varieties. Through an online survey, consumers indicate which flavors they like and how often they would purchase them. The TURF analysis reveals which combination of four flavors achieves the greatest reach. Not every flavor with a high individual rating contributes to the best combination. Surprisingly, strawberry, vanilla, cherry, and mango appeal to more people than other mixes. The evaluation also incorporates data on purchase frequency, providing a clear basis for assortment planning.
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