Synthetic Data in Market Research: What It Means for Translation Workflows

The rise of synthetic data is reshaping how market research is conducted. For those of us in the translation industry—where we deal daily with survey instruments, open-ends, verbatims, and reporting—the introduction of artificially generated data brings both opportunities and challenges. Understanding these implications is crucial for delivering accurate, culturally relevant insights across languages.

What Is Synthetic Data in Market Research?

Synthetic data is artificially generated information that mirrors the structure and statistical properties of real survey responses without being tied to an individual respondent. In practice, it may be used to:

• Test questionnaires and logic before fieldwork begins
  

• Augment limited or hard-to-reach sample groups
  

• Protect respondent privacy in sensitive studies
  

• Train AI models for coding and predictive analytics
  

It is not intended to fully replace live respondent data, but rather to supplement and streamline the research lifecycle.

Implications for Translation Workflows

1. Questionnaire Pre-Testing and Pseudo-Translation

Synthetic respondent profiles can be generated by AI and used to run through draft questionnaires to detect flaws. The same can be done with multilingual versions by pseudo-translating the questionnaires (using AI to generate translations), and then generating foreign language respondent profiles.

Recommendation: Foreign language content often breaks survey programming designed to handle English content. This is an excellent way to use AI to test survey programming prior to translation saving time and cost in the translation process. Particularly valuable if you’re fielding in multiple target countries / languages.

2. Back-Translation and Review Challenges

Synthetic open-ends tend to have modeled phrasing that doesn’t always sound natural. When sent through back-translation or in-country review, reviewers may flag these “odd” phrasings as errors—even though they reflect the synthetic generation process, not mistranslation. Depending on the quality of the synthetic data this can also cause issues in coding, potentially leading to bad data.

Recommendation: Be transparent with reviewers and coding teams about which content is synthetic to avoid issues. 

3. Translation Memory (TM) Contamination

Market research translation workflows rely heavily on reusing past translations via Translation Memory technology. If synthetic data is added to TMs, they can propagate inaccurate or incorrect translations.

Recommendation: Maintain a clear separation between TMs used for production and any content containing synthetic data. This isn’t to say that synthetic data can’t be captured and reused, it should just be managed differently than human generated content.

4. Nuance and Authenticity Risks

Synthetic open-ends often miss the cultural nuance, slang, or unexpected turns of phrase that real respondents provide. For translations that feed into insights reporting, this lack of “voice of the customer” can reduce authenticity.

Recommendation: Use synthetic responses for workflow testing, scenario modeling, and AI training—but rely on real verbatims and quality translations when insights depend on capturing authentic voice.

Balancing Efficiency and Authenticity

Synthetic data has the potential to speed up processes, protect privacy, and reduce costs, but it cannot replicate the richness of human responses. For translation teams, the balance lies in:

• Efficiency: Using synthetic content for pre-testing and process validation
  

• Authenticity: Prioritizing real respondent data for insights and reporting
  

• Clarity: Ensuring linguists and reviewers know what type of data they are handling
  

Final Thoughts

As synthetic data becomes a standard tool in the market researcher’s toolbox, translation workflows must adapt. The key is not to resist this shift, but to establish clear workflows, labeling conventions, and data separations that preserve both efficiency and linguistic integrity.

At the end of the day, synthetic data can make translation workflows smarter and more secure—but only when used with care and transparency. 

If you’re interested in the general application of AI to market research translation workflows you can read more about that here: https://www.languageintelligence.com/post/when-to-use-machine-translation-tools-within-your-global-surveys

If you’re interested in learning more about AI-Assisted Human Translation, you can read about that here: https://www.languageintelligence.com/ai-assisted-human-translation