Reproducing Consumer Survey Responses with 2.2% Error Margin Using AI
Laboro.AI has successfully reproduced consumer survey results with an average error of 2.2% through AI simulation in a joint validation with a major retailer. Following this, the company is launching 'Future Research (Beta)'.
📋 Article Processing Timeline
- 📰 Published: April 15, 2026 at 00:50
- 🔍 Collected: April 14, 2026 at 16:31
- 🤖 AI Analyzed: April 19, 2026 at 15:07 (118h 35m after Collected)
Laboro.AI Co., Ltd. (Chuo-ku, Tokyo, Representative Director & CEO Tetsuo Shiibashi, Representative Director, COO & CTO Hiromasa Fujiwara; hereinafter referred to as 'the Company'), which operates 'Custom AI Development', has succeeded in reproducing actual survey results with an average error of 2.2%*1 in a comparative validation between market research (questionnaire surveys) conducted jointly with a major domestic retail company and AI simulation research. This result falls within the 3-5% margin of error standardly accepted in market research and statistical testing, and can be evaluated as having a level of accuracy equivalent to actual surveys.
Following this achievement, the product 'Future Research (Prototype Version)' announced in June 2025 will be updated and launched as 'Future Research (Beta Version)', an AI simulation market research service focused on quantitative surveys. At the same time, we announce the start of recruitment for 10 exclusive early-access partner companies.
*1 The average error value of the response ratio of the TOP 2 (top 2 items) in a question asking purchase intention (7-point evaluation scale) for a total of 446 products conducted in the past.
Background and Purpose
Against the backdrop of a market environment where consumer needs are rapidly diversifying and trends are experiencing shorter cycles, corporate product development and marketing require precise consumer understanding with greater speed than ever before. However, traditional consigned and self-service market research, mainly relying on questionnaire surveys, which is one of the methods, has had the following challenges and risks.
■ Challenges of Consigned Research
- Risk of changing consumer needs due to time lags: In some cases, it takes more than a month from planning to delivery, and by the time the results are out, trends and consumer needs have already changed.
- Risk of one-shot research and development: In the case of surveys aimed at product development, as the survey period is prolonged, opportunities for improvement decrease, leading to products being launched in an incomplete state.
- Risk of lost opportunities due to limits of comprehensiveness: Due to cost and question limits per survey, not all ideas can be thoroughly validated, causing missed opportunities to execute promising plans.
■ Challenges of Self-Service Questionnaire Research
- Risk of lacking expertise in design and analysis: Because there is no expert guidance, unless the person in charge is experienced, the survey design will be insufficient, failing to yield the intended insights.
- Risk regarding response data quality: When many invalid responses, such as false answers or survey fatigue, are mixed in, self-cleaning of the data is difficult.
To resolve these challenges and risks in market research, our Company has initiated joint validations with companies to develop a service to grasp consumer needs precisely, cost-effectively, and timely by utilizing AI at each stage of the research process.
Image of utilizing AI at each stage of the research process.
Overview of Joint Validation and Results with a Major Retail Company
In the joint validation with the major retail company, aiming to confirm practicality in the actual product development site, we compared the survey results (measured values) of consumer questionnaires for a total of 446 past products with the response results generated by AI simulation research to verify its reproducibility. Furthermore, in this simulation, we did not train the AI on the measured values; instead, we had the AI generate response results purely based on conditions such as the attributes of the survey targets, question structures, and product concept information. As a result, we confirmed the following outcomes.
1. Average error of 2.2% against actual surveys
As a result of comparing the response ratio selecting the TOP 2 (top 2 items) in the question asking purchase intention for each product (7-point evaluation scale), the average error across all products was 2.2%, confirming that the AI simulation research reproduced consumers' purchase intentions with high accuracy.
2. Accuracy of commercialization judgment at 92.4%
To verify the degree of agreement with the commercialization judgment based on the company's actual standards, we adopted the 95% confidence interval of the measured values as a benchmark and compared the judgment results. As a result, judgments matched for 92.4% of all products, confirming that AI simulation research can derive decisions that can withstand actual business operations.
Our Company evaluates that this joint validation has demonstrated the possibility that AI simulation research can reproduce actual surveys with a certain degree of certainty, and can help supplement and resolve the aforementioned challenges and risks of market research.
Overview of joint validation with a major retail company.
Regarding the Launch of 'Future Research (Beta Version)'
Based on the knowledge gained through this joint validation, our Company has updated 'Re...
Following this achievement, the product 'Future Research (Prototype Version)' announced in June 2025 will be updated and launched as 'Future Research (Beta Version)', an AI simulation market research service focused on quantitative surveys. At the same time, we announce the start of recruitment for 10 exclusive early-access partner companies.
*1 The average error value of the response ratio of the TOP 2 (top 2 items) in a question asking purchase intention (7-point evaluation scale) for a total of 446 products conducted in the past.
Background and Purpose
Against the backdrop of a market environment where consumer needs are rapidly diversifying and trends are experiencing shorter cycles, corporate product development and marketing require precise consumer understanding with greater speed than ever before. However, traditional consigned and self-service market research, mainly relying on questionnaire surveys, which is one of the methods, has had the following challenges and risks.
■ Challenges of Consigned Research
- Risk of changing consumer needs due to time lags: In some cases, it takes more than a month from planning to delivery, and by the time the results are out, trends and consumer needs have already changed.
- Risk of one-shot research and development: In the case of surveys aimed at product development, as the survey period is prolonged, opportunities for improvement decrease, leading to products being launched in an incomplete state.
- Risk of lost opportunities due to limits of comprehensiveness: Due to cost and question limits per survey, not all ideas can be thoroughly validated, causing missed opportunities to execute promising plans.
■ Challenges of Self-Service Questionnaire Research
- Risk of lacking expertise in design and analysis: Because there is no expert guidance, unless the person in charge is experienced, the survey design will be insufficient, failing to yield the intended insights.
- Risk regarding response data quality: When many invalid responses, such as false answers or survey fatigue, are mixed in, self-cleaning of the data is difficult.
To resolve these challenges and risks in market research, our Company has initiated joint validations with companies to develop a service to grasp consumer needs precisely, cost-effectively, and timely by utilizing AI at each stage of the research process.
Image of utilizing AI at each stage of the research process.
Overview of Joint Validation and Results with a Major Retail Company
In the joint validation with the major retail company, aiming to confirm practicality in the actual product development site, we compared the survey results (measured values) of consumer questionnaires for a total of 446 past products with the response results generated by AI simulation research to verify its reproducibility. Furthermore, in this simulation, we did not train the AI on the measured values; instead, we had the AI generate response results purely based on conditions such as the attributes of the survey targets, question structures, and product concept information. As a result, we confirmed the following outcomes.
1. Average error of 2.2% against actual surveys
As a result of comparing the response ratio selecting the TOP 2 (top 2 items) in the question asking purchase intention for each product (7-point evaluation scale), the average error across all products was 2.2%, confirming that the AI simulation research reproduced consumers' purchase intentions with high accuracy.
2. Accuracy of commercialization judgment at 92.4%
To verify the degree of agreement with the commercialization judgment based on the company's actual standards, we adopted the 95% confidence interval of the measured values as a benchmark and compared the judgment results. As a result, judgments matched for 92.4% of all products, confirming that AI simulation research can derive decisions that can withstand actual business operations.
Our Company evaluates that this joint validation has demonstrated the possibility that AI simulation research can reproduce actual surveys with a certain degree of certainty, and can help supplement and resolve the aforementioned challenges and risks of market research.
Overview of joint validation with a major retail company.
Regarding the Launch of 'Future Research (Beta Version)'
Based on the knowledge gained through this joint validation, our Company has updated 'Re...