Low Fidelity
Low Fidelity
Low Fidelity
Validation Survey
Overview
A Validation Survey is a structured, quantitative test designed to measure customer sentiment, prioritisation, and likelihood to act on a proposition. At Future Foundry, we use this method when we need clear, data-driven evidence on whether an idea, feature, or pricing model resonates with a target audience. Unlike open-ended discovery surveys, a Validation Survey forces customers to make trade-offs—ranking their priorities, choosing between options, or responding to specific value propositions. This helps us move beyond broad insights and towards clearer signals of desirability and purchase intent. It’s particularly useful when refining feature sets, pricing strategies, or determining the most compelling aspects of a proposition before investing in prototypes or MVPs. However, while surveys provide directional insight, they are still self-reported data and should be paired with behavioural tests.
A Validation Survey is a structured, quantitative test designed to measure customer sentiment, prioritisation, and likelihood to act on a proposition. At Future Foundry, we use this method when we need clear, data-driven evidence on whether an idea, feature, or pricing model resonates with a target audience. Unlike open-ended discovery surveys, a Validation Survey forces customers to make trade-offs—ranking their priorities, choosing between options, or responding to specific value propositions. This helps us move beyond broad insights and towards clearer signals of desirability and purchase intent. It’s particularly useful when refining feature sets, pricing strategies, or determining the most compelling aspects of a proposition before investing in prototypes or MVPs. However, while surveys provide directional insight, they are still self-reported data and should be paired with behavioural tests.
A Validation Survey is a structured, quantitative test designed to measure customer sentiment, prioritisation, and likelihood to act on a proposition. At Future Foundry, we use this method when we need clear, data-driven evidence on whether an idea, feature, or pricing model resonates with a target audience. Unlike open-ended discovery surveys, a Validation Survey forces customers to make trade-offs—ranking their priorities, choosing between options, or responding to specific value propositions. This helps us move beyond broad insights and towards clearer signals of desirability and purchase intent. It’s particularly useful when refining feature sets, pricing strategies, or determining the most compelling aspects of a proposition before investing in prototypes or MVPs. However, while surveys provide directional insight, they are still self-reported data and should be paired with behavioural tests.
Process
We begin by designing the survey with specific validation goals in mind, ensuring that the questions force clear choices rather than generic positive responses. This could involve ranking exercises, forced-choice selections, or hypothetical commitment scenarios (e.g., “Which of these would you pay for?”). The survey is distributed through email lists, social media, or embedded into existing digital experiences. We ensure a statistically significant response rate, filtering out biased or unqualified responses to ensure reliability. Once data is collected, we analyse patterns in preferences, objections, and willingness to pay. If a large percentage of respondents indicate strong intent, that’s a signal to move forward with higher-fidelity testing. If responses are weak or inconsistent, we refine the offer and reposition before progressing.
We begin by designing the survey with specific validation goals in mind, ensuring that the questions force clear choices rather than generic positive responses. This could involve ranking exercises, forced-choice selections, or hypothetical commitment scenarios (e.g., “Which of these would you pay for?”). The survey is distributed through email lists, social media, or embedded into existing digital experiences. We ensure a statistically significant response rate, filtering out biased or unqualified responses to ensure reliability. Once data is collected, we analyse patterns in preferences, objections, and willingness to pay. If a large percentage of respondents indicate strong intent, that’s a signal to move forward with higher-fidelity testing. If responses are weak or inconsistent, we refine the offer and reposition before progressing.
We begin by designing the survey with specific validation goals in mind, ensuring that the questions force clear choices rather than generic positive responses. This could involve ranking exercises, forced-choice selections, or hypothetical commitment scenarios (e.g., “Which of these would you pay for?”). The survey is distributed through email lists, social media, or embedded into existing digital experiences. We ensure a statistically significant response rate, filtering out biased or unqualified responses to ensure reliability. Once data is collected, we analyse patterns in preferences, objections, and willingness to pay. If a large percentage of respondents indicate strong intent, that’s a signal to move forward with higher-fidelity testing. If responses are weak or inconsistent, we refine the offer and reposition before progressing.
Requirements
This experiment requires access to an engaged audience—existing customers, target market email lists, or digital advertising to drive responses. The survey must be structured to force meaningful responses, avoiding leading questions or vague sentiment analysis. The outcome isn’t just about getting responses—it’s about translating those responses into a clear go or no-go decision on what comes next.
This experiment requires access to an engaged audience—existing customers, target market email lists, or digital advertising to drive responses. The survey must be structured to force meaningful responses, avoiding leading questions or vague sentiment analysis. The outcome isn’t just about getting responses—it’s about translating those responses into a clear go or no-go decision on what comes next.
This experiment requires access to an engaged audience—existing customers, target market email lists, or digital advertising to drive responses. The survey must be structured to force meaningful responses, avoiding leading questions or vague sentiment analysis. The outcome isn’t just about getting responses—it’s about translating those responses into a clear go or no-go decision on what comes next.
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