Study Parameters
Studies present a single yes/no question to participants on cards which can contain a title, image, description, and price to add context to the question or can be varied to explore their effects. Machine learning techniques analyze responses, assessing question card attributes and participant demographics, to identify features with real predictive importance for increasing the likelihood of positive responses to the question. Only participants with predefined characteristics are permitted to participate, ensuring that the study aligns with the target audience. Via the Linesol app, participants answer questions (min. 10 seconds) receiving $0.05 per response ($18 per hour), fostering thoughtful, authentic replies for actionable insights.
Question Card Features
Define a yes/no question like 'Would you buy this product?' and enhance it with question card features such as Titles, Images, Descriptions, and Prices. This approach enables the exploration of how different design elements impact the likelihood of positive responses to the question. Each participant responds to a specific configuration, and their anonymized user data is collected for analysis.
User Features
Select from a variety of user feature categories, such as Age, Purchasing Behavior, and Environmental Concern. Predefined categories are available, or new ones can be specified to suit the study's needs. For each category, a range of features can be selected to include in the study. The participant demographic will be defined by these selected features, ensuring that only users with compatible characteristics can participate. By incorporating multiple user features, the study effectively evaluates how these different characteristics influence the likelihood of positive responses to the question.
Results
Data Analysis Report
Unlock unparalleled insights with advanced analytics. Using machine learning on datasets from over 200 participants, this analysis ranks the most influential features on the likelihood of a positive response to your question. The report also includes price optimization insights and suggested improvements, delivering clear and actionable results.
Impact of Question Card Features
- Explore how various question card features, including price, title, description, and image, influence the likelihood of positive responses.
Optimal Price Determination
- Identify the optimal price point to maximize revenue per product view by analyzing a range of prices included in the question card features.
Impact of User Features
- Discover how diverse user characteristics impact the likelihood of positive responses. This analysis offers valuable insights into demographic behaviors, empowering refined marketing strategies and optimized resource allocation for maximum effectiveness.
Suggested Improvements
- Discover how correlated features are impacting the accuracy of the results. With insights from a multicollinearity report, refine feature selection and remove non influencial features to improve model accuracy and clarity, delivering more reliable, actionable results in future studies.
Use Cases
Unlock unlimited possibilities with endless combinations of yes/no questions, question card features, and user features. This powerful and scalable flexibility allows for the deployment of a wide range of applications—each delivering actionable insights tailored to your needs. Whether you're running an A/B Monadic Test, Pre-Roll Test, Conjoint Study, Key Driver Analysis, Price Sensitivity assessment, Segmentation model, or even a Single Implicit Test, our technology offers a modern, adaptive alternative - at just $0.10 per participant. Explore the examples below to see how these use cases are implemented and discover how they can replace traditional quantitative research methods—enhancing speed, scalability, and data-driven decision-making at every stage.
- Psychology
- Economics
- Sociology
- Anthropology
- Communications
- Product Appeal
- Slogan Effectiveness
- Campaign Relevance
- Product Positioning
- User Interface Design
- Response Evaluation
- Image Evaluation
- Engagement Appeal
- Bias Detection
- Content Relevance
