What’s the best way to understand the market potential for your new product or service? Or the best way to determine which features resonate most with your target audience? Feature testing is a critical stage in product development and your choice of survey methods can significantly impact the research insights gathered. Understanding when to use direct preference questions, ranking exercises, or more advanced approaches like Maximum Difference Scaling (Max Diff) or (Discrete Choice) Conjoint Analysis is essential for obtaining actionable insights.
To help, we put together various scenarios a product team might encounter and how you can apply different feature testing methods to address key questions that arise in each situation.
Scenario 1: Identifying the Most Attractive Features for a New Product
In the early stages of product development, it is essential that you identify which features are most likely to attract users. The key questions in this scenario are:
Approach: Direct Preference Questions
Direct preference questions, such as “Which of these features is most appealing?” or asking respondents to select their top three features, provide a simple and effective method for gathering quick insights. This approach is particularly useful for capturing initial impressions and identifying leading features with minimal effort from respondents.
However, this method may oversimplify the evaluation process, potentially leading to inconclusive or fragmented results. If preferences are split, further investigation may be required to pinpoint which features truly drive consumer interest.
Scenario 2: Optimizing Feature Set for a Product Upgrade
When you’re refining an existing product, the goal is often to optimize the feature set by retaining, improving, or removing certain features. The key questions here include:
Approach: Ranking Exercise
A ranking exercise allows respondents to rank all features in order of appeal, helping to establish a detailed hierarchy of preferences. This approach is particularly useful when working with a smaller set of features, offering a clear order of importance that simplifies decision-making for product enhancements. Ranking helps to understand not just what is preferred, but also shows the relative position of each feature.
However, it is important to consider that ranking can be more demanding for survey respondents, especially when the list of features is long – leading to respondent fatigue and potentially fragmented data. Thus, it is important that you balance the number of features being ranked with the cognitive load placed on respondents.
Scenario 3: Balancing Trade-Offs Between Multiple Features
As your products often consist of numerous features, understanding how consumers prioritize and trade off between them is important. The key question in this scenario is:
Approach: Max Diff Analysis
A Max Diff exercise is particularly useful when you are dealing with a larger set of features, where it is important to understand trade-offs between features. By asking respondents to choose the best and worst features from a subset of options, Max Diff forces respondents to make more thoughtful and discriminative choices, enabling product teams to prioritize those that drive the most value. Max Diff not only provides the order of preference but also quantifies the strength of those preferences, offering a more robust understanding of how strongly each feature stands relative to others.
This method reduces cognitive load and minimizes respondent fatigue, resulting in clearer and more actionable insights. While Max Diff is more complex to set up and analyze compared to simpler methods, the depth and reliability of the data it provides often justify the additional effort and cost.
Scenario 4: Evaluating Feature Combinations and Willingness to Pay
In some cases, it's not just about which features are your target audience prefers but also about understanding how different feature combinations influence purchase decisions and what users are willing to pay for these features. The key questions include:
Approach: Discrete Choice Conjoint Analysis
Discrete Choice Conjoint Analysis is highly effective for understanding how different combinations of features influence purchase intent and determining the willingness to pay for these features. This method simulates real-world buying decisions by presenting respondents with various feature combinations and price points, analyzing their choices to provide insights into the most compelling and valuable product bundles.
By understanding how your users value different features and what they are willing to pay, the product team can develop product bundling and pricing strategies that align with consumer perceptions of value.
However, while Conjoint Analysis is a powerful research technique, it is more complex to set up and analyze compared to simpler methods. It requires higher costs and specialized tools, and the surveys can be longer, which may be more demanding for respondents.
Getting Started
Choosing the right survey method depends on the your specific product development needs :
By aligning the feature testing approach with the specific questions, the product team needs to answer, you can gather actionable insights that inform product development strategies, ensuring that the final product resonates with your target audience and meets market demands.
And by partnering with our feature testing research experts, you can ensure the entire process is seamless. To get started, contact Phase 5 today!