Asian market research encompasses a vast complexity and countless intricacies. As more and more U.S.- and E.U.-based marketers, researchers and consultancies turn to Asian online panels, data quality becomes critical to their success in both local and regional markets. The region's significant multicultural diversity, variations in languages and digital literacy add further difficulty to an already challenging business landscape. 

2025 is no different than any other year, bringing challenges with fraudulent responses, points of friction, survey abandonment and unreliable data threatening the quality and trustworthiness of survey outcomes.

Region-Specific Insights and Strategies

Put simply, Asia is beyond diverse, and data quality is affected by a plethora of different factors. These include, but are not limited to:

• Cultural Norms - In countries like Japan, China and South Korea respondents give answers they believe to be polite rather than completely truthful. Data quality is also affected by positive response bias in poorer Asian nations, especially in face-to-face interviews where there are numerous examples of underreporting any consumer dissatisfaction. 
• Language diversity - Countries like India have over 22 official languages. In Indonesia there are over 100 different dialects. The challenge here is therefore accurate translation and hyper-localisation of surveys. Even in a single language regional expressions, idioms, and literacy levels vary extensively.
• Trust and participation barriers - In Asian markets, trust with data collection agencies remains low due to government surveillance, misuse, or scams. Building trust is therefore critical and often requires partnerships with local institutions to emphasise anonymity and data protection protocols.
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Synthetic Solutions?

A report from Qualtrics using data from over 3,000 market researchers across 14 countries found that 71% are in agreement that in less than three years, synthetic responses will constitute over half of data collection. Synthetic data is artificially generated through a series of complex algorithms that can ultimately simulate future scenarios. Synthetic data mimics real-world data, and a massive 87% of researchers have reported high satisfaction with its use. 

Currently however, studies have  also found that 74% of companies are yet to establish tangible value from AI. Furthermore speakers at Asia Research’s recent seminar in Singapore, stated that the current situation is “like a hammer looking for a nail.”

Uptake of synthetic data in Asian market research is still a bit behind its Western counterparts, and more relevantly some studies have shown that synthetic data often fails to recognise minority opinions, cultural nuances or spontaneous reactions - potentially making it less suited to Asian marketing research industry.

Fraudulent Responses and the Solution

There are varying estimates on the frequency of fraud in market research with some as high as 30-40% and global market losses in 2024 of roughly $350m. The greatest risk of fraud is not the occasional liar, but rather organised attacks on individual panels and surveys by gangs or bots. Detecting fraud can be performed by collecting data on suspect accounts. This is primarily from:

• ・Device data
• ・Geolocation data
• ・Behavioural data
• ・Profile data
• ・Identity verification from initial join forms

Once this data has been gathered and it has been determined whether a respondent is legitimate, AI works to create a cyclical process whereby decisions about validity are made more efficiently. 

Other Aspects of Data Quality

Ensuring data quality also means ensuring respondent retention. The best ways of doing this involve:

• ・Utilising carefully designed surveys that do not waste time

• ・Offering loyalty rewards matching value of data provided

• ・Ensuring security and anonymity of data to prevent misuse

Panelists should also be vetted and verified before so that any potential fraudsters are identified and removed from sample populations prior to participation.

Choosing a Reliable Asian Panel Provider

Choosing the right panel provider primarily involves looking at data quality, panel size and diversity, and information security quality. This involves a wide array of factors starting at questionnaire design through to sampling, and onto data cleaning and tabulation. Anti-fraud measures and duplicate prevention are also high on the list. You should also evaluate data collection methods, ensuring they are both qualitative and quantitative as well as consider your budget. Additionally, holding a fairly deep understanding of the panel’s use of AI and whether it is in fact genuine AI or merely automation is of utmost importance.

For more details contact us or download our free e-book to learn more about “Our Panel Quality Management" to make smarter research decisions today!