Once seen as an industry resistant to change, market research has embraced transformative technologies in recent years, with AI leading in reshaping traditional methods. Yet, diversity in the data remains elusive, presenting both an opportunity and challenge for researchers. As the founder of ThinkNow, a company at the forefront of multicultural insights, I’ve witnessed firsthand how critical accurate representation is in understanding diverse consumer behavior.

One way we are addressing this disparity is by creating synthetic samples. Over the years, we’ve developed ThinkNow Synthetic—a synthetic sample solution that leverages artificial intelligence to enhance diversity in data collection. However, for synthetic data to advance diversity, the quality of the training data is paramount. This article examines how AI, particularly synthetic sampling, can revolutionize the industry by producing more inclusive and representative datasets, while also highlighting the differences between synthetic sampling and traditional methods like weighting.

The Evolution of Synthetic Sample

Traditional sampling techniques in market research often fall short when it comes to representing hard-to-reach demographics such as Hispanic, Black, AANHPI, and LGBTQIA+ communities. Even with diligent panel recruitment efforts, certain populations remain underrepresented. ThinkNow Synthetic was born out of this necessity, using large language models (LLMs) trained on multicultural data to create synthetic responses that mirror real-world diversity.

The process begins with training the model on diverse datasets, like the General Social Survey (GSS) and ThinkNow’s proprietary data collected from our panel, DigaYGane.com. This ensures that the synthetic sample reflects the population in question and produces responses representing a wide range of cultural experiences. Our approach enhances the inclusiveness of the data and reduces biases often associated with AI-generated responses.

Synthetic Sampling vs. Weighting

A common misconception in market research is to equate synthetic sampling with weighting. While both aim to adjust the data to reflect population diversity better, they employ fundamentally different methodologies. Weighting, as many researchers are familiar with, takes a small sample size and extrapolates the results to a larger population. This can inflate the representation of underrepresented groups but doesn’t truly increase the diversity of responses. Essentially, weighting adjusts the numbers, not the underlying richness or authenticity of the data.

In contrast, synthetic sampling, particularly ThinkNow Synthetic, is designed to create entirely new data points based on the learned behavior of respondents from diverse communities. For example, if you are conducting a study among bicultural Latinos and face difficulty recruiting sufficient respondents, our AI model can generate synthetic responses that mimic those of a bicultural Latino based on actual data collected from our panel. This method doesn’t simply inflate responses but creates new, culturally nuanced data that enriches the overall dataset.

This difference is significant. Weighting amplifies a limited dataset, while synthetic sample expands it by simulating a broader range of responses. This approach has the potential to dramatically increase representation without sacrificing data accuracy.

The Role of Diverse Training Data

The success of any synthetic sample solution hinges on the quality and diversity of its training data. If the training data used to create synthetic responses is skewed or biased, the results will reflect those same biases. Training LLMs on rich, multicultural datasets ensures that synthetic responses are representative and culturally relevant, effectively mitigating the biases often found in AI-generated content.

ThinkNow Synthetic’s hybrid model combines panel data and synthetic responses to create complete and representative datasets. When a client comes to us with a quantitative study needing 1,000 completed responses, for example, we can provide 500 actual survey responses from our diverse panel and supplement the remaining 500 using synthetic data generated by our AI. This hybrid approach preserves the integrity of the study while reducing costs and accelerating delivery.

Applications and Future Potential

Synthetic sampling is still in its early stages, but the potential applications are vast. From understanding consumer trends to informing policy decisions, synthetic sample can provide a fuller picture of societal behaviors across diverse populations. By filling gaps in datasets with culturally relevant synthetic responses, ThinkNow Synthetic helps clients make more informed decisions that reflect the reality of the communities they serve.

This approach also addresses a major challenge in market research: the underrepresentation of marginalized groups. As brands seek to engage diverse audiences, producing accurate and inclusive data authentically has become a business imperative. Synthetic sampling offers a path forward, equipping researchers with the tools to understand these audiences more deeply.

Conclusion

AI-powered synthetic sample has the potential to revolutionize diversity in market research. However, this is only possible if the training data is as diverse as the populations we aim to represent. At ThinkNow, we are committed to using our years of expertise and rich multicultural datasets to ensure that synthetic sampling doesn’t just mimic diversity but truly reflects it. By combining synthetic data with real-world panel responses, we are creating a new era in market research—one where inclusivity and accuracy go hand in hand.

The future of market research is diverse, and with synthetic sample, we’re ensuring that no voice is left unheard.

This blog post was originally published on HispanicAd.com

Synthetic samples can accelerate data-driven decision-making by providing timely access to accurate and relevant consumer opinion data sets. In a hyper-competitive environment where time to market can be a decisive factor, augmenting online panels with synthetic data can be a game changer. But what are synthetic samples, and how are they changing how researchers collect and analyze data?

What is a Synthetic Sample?

Synthetic samples have been used in various forms for nearly fifty years, but the latest advancements, driven by artificial intelligence (AI), are poised to propel this methodology mainstream. This advanced technique uses AI to generate survey data that mimics real-world responses, giving researchers an alternative to human-powered panels.

How Do Synthetic Samples Work?

Ironically, human panels are foundational to synthetic data creation, as real-world data, often collected through traditional surveys and multicultural panels, serves as the building blocks for these synthetic datasets. After the data is collected, AI language models analyze this data and generates additional responses that mimic the characteristics of the original data. This hybrid approach allows for a combination of real and synthetic data, generating a more comprehensive and diverse pool of information.

Advantages of Synthetic Samples

1. Cost-Effectiveness: Synthetic samples significantly reduce the costs associated with large-scale surveys. By generating data artificially, the need for large physical samples (human sample) is minimized, resulting in considerable savings.
2. Speed and Scale: Synthetic response generation is nearly instantaneous, enabling the rapid creation of large, diverse datasets. This accelerated data collection is invaluable for studies demanding swift insights to inform strategic decisions.
3. Data Augmentation: Synthetic samples can fill in missing data and expand the range of possible questions, creating more complete and detailed datasets.
4. Cultural Relevance: By using multicultural data in training AI models, synthetic samples can authentically represent diverse and underrepresented communities. This is crucial for campaigns aiming to resonate with specific and varied audiences.
5. Bias Mitigation: AI models can be trained to mitigate bias, ensuring fair and representative data.

Practical Applications of Synthetic Samples

Synthetic samples have vast and varied applications. In market research, they allow for a more accurate and comprehensive understanding of consumer trends. In social sciences, they facilitate the study of behaviors and trends with representative historical and current data from diverse populations. For policy-making, they provide a solid foundation for informed decisions reflecting the views of all communities.

Challenges and Considerations

Despite their many advantages, synthetic samples also present challenges. Generating high-quality synthetic data can be computationally intensive, requiring significant resources. Working with a skilled firm is critical. It is also important that AI models are well-trained and validated to avoid generating inaccurate or biased data. Furthermore, transparent communication about the use of synthetic data is crucial for building trust with consumers and stakeholders.

Conclusion

Synthetic samples transform market research by providing faster, more accurate, and culturally relevant data. By combining the best of traditional data with advanced AI capabilities, these samples offer a powerful tool for understanding and reaching diverse audiences with smarter and more effective campaigns.

In today's digital era, market research has significantly evolved, adopting innovative strategies to enhance participation and data quality. One of the most notable trends is gamification applied to online panels. Gamification involves integrating game elements, such as competitions, points, levels and rewards, into non-game activities to motivate and engage participants.

Why Gamification Works

Gamification transforms the online panel experience in a few key ways. First, it boosts engagement and completion rates. By turning surveys into challenges or games, respondents are more engaged and less fatigued which reduces survey drop-off rates. This leads to more comprehensive and accurate data sets.

Additionally, gamification fosters a healthy competitive environment among participants. Real-time leaderboards and instant rewards motivate respondents to put in their best effort, leading to more thoughtful and engaged responses. This ultimately improves the quality of data collected.

Another crucial benefit of gamification is its ability to attract and retain a more diverse and younger audience. Millennials and Gen Zers favor digital experiences and instant gratification and are particularly drawn to games and interactive activities. This allows researchers to tap into a wider, more diverse audience, enriching the data pool with fresh perspectives.

Gamification Examples in Online Panels

• Points and Rewards System: Panelists earn points for completing surveys or referring friends, which they can redeem for prizes such as gift cards, PayPal credits, physical products, raffle coupons or donations to charitable organizations. Another technique is to create badges that serve as rewards when panelists achieve certain objectives, such as participating for a set time, responding to a certain number of surveys or completing profiling questionnaires.
• Progress Bar: Gamification leverages human’s natural desire to track progress. Just like the satisfaction of checking off a completed task on a list, online panels with gamification elements like displaying a status bar showing progress toward certain goals provides a similar sense of accomplishment as participants move through the survey. This reinforces engagement and motivates them to continue.
• Participation Ranking: Create a leaderboard where users can see their position compared to other participants based on accumulated points. Panelists in the Top 3, for example, are rewarded with points or other incentives and are recognized through other means such as social media or the panel's monthly newsletters.
• Challenges and Contests: Encouraging panelists to participate in quick challenges and activities to earn extra points within a limited time disrupts the status quo. Here, gamification goes beyond surveys to include more creative tasks like commenting on a forum or social media post, uploading photos or completing questionnaires.
• Forums and Social Media: Creating safe spaces where participants can interact, share experiences and solve challenges together while earning points for their participation builds a sense of community. By doing so, participants are more likely to invite friends to join and share their achievements on social media to earn additional points.

Gamification Works

Gamification has been a game changer for the market research industry. By incorporating engaging elements, online panels have transformed static one dimensional surveys into multifaceted interactive experiences that boost participation and increase participant loyalty, resulting in better data and better outcomes for participants and brands.

The modern consumer has evolved. Today, they're not simply passive buyers but active cultural participants who engage with brands that resonate with their values, identities, and lived experiences. This shift has driven the rise of cultural marketing, a nuanced approach that seeks to connect with consumers through their cultural lens.

However, to truly comprehend this cultural lens, marketers must harness the power of market research to gather rich insights directly from consumers. Using online sample is among the most prevalent and efficient ways to gather these insights.

Cultural Marketing in the U.S.

America is a diverse nation, filled with many different cultures. This diversity presents both challenges and opportunities for marketers tasked with understanding how shared values, customs and beliefs shape people’s lives and influence consumer behavior. This cultural intelligence helps brands tailor their messaging, products, and services to resonate more deeply with specific audiences.

Benefits and Considerations for Online Sample

Online panels can help brands understand and target specific cultural groups. However, this market research tool isn’t without its challenges. Let’s look at the benefits first.

Benefits:

• Accessibility and Cost-Effectiveness: Online samples are generally more accessible and cost-effective than traditional methods like face-to-face interviews or focus groups. This allows researchers to reach diverse cultural groups, even those geographically dispersed.
• Targeted Insights: Researchers can design online surveys and tasks specifically tailored to understand the values, preferences, and behaviors of different cultural groups. This data can inform content creation, messaging, and distribution strategies.
• Quantifiable Data: Online samples enable large-scale data collection, providing statistically significant results for better decision-making. This can be especially valuable for understanding niche or underrepresented cultural segments.
• Participant Diversity: Online platforms offer access to a wider range of participants, potentially including individuals who might not otherwise participate in traditional research due to time constraints or social anxieties.

Challenges and Considerations:

• Sampling Bias: Online samples are not immune to sampling bias. If not carefully designed, they may overrepresent certain demographics, leading to inaccurate conclusions about specific cultural groups.
• Cultural Nuances: Online surveys and tasks might not capture the intricacies of cultural attitudes and behaviors. Researchers must be mindful of language, design, and context to avoid misinterpretation.
• Digital Divide: Not all cultural groups have equal access to the internet or digital literacy. This can lead to the underrepresentation of certain populations in online samples.
• Ethical Concerns: Data privacy and participant anonymity are crucial considerations. Researchers need to ensure transparency and ethical data collection practices.

Overall, online sample is a valuable tool for cultural marketing when conducted by experienced market research agencies familiar with online samples’ benefits and limitations so data collection can be implemented with sensitivity and cultural awareness.

En el panorama digital actual, la mayoría de nosotros hemos interactuado con bots, programas informáticos que imitan la conversación humana y automatizan tareas. Los chatbots pueden, por ejemplo, manejar consultas en el sitio web, monitorear los canales de redes sociales y recopilar datos de encuestas. Los bots son omnipresentes y se utilizan en una variedad de aplicaciones, incluida la investigación de mercado. Este blog aborda preguntas frecuentes sobre los bots en los paneles de investigación de mercado.

¿Cómo funcionan los bots en los paneles de investigación de mercado?

De alguna manera, los bots están revolucionando la industria de la investigación de mercado. En lugar de depender de las metodologías de encuesta tradicionales, los bots pueden ayudar a realizar encuestas de forma rápida y más eficiente, lo que potencialmente ahorra tiempo y dinero a los clientes. Al incrustar bots en destinos populares para los consumidores, como las plataformas de redes sociales, les facilita la participación en las encuestas. Los bots también se pueden usar para analizar grandes cantidades de datos de manera rápida y eficiente, proporcionando información muy necesaria en una fracción del tiempo. Pero, con los beneficios vienen los riesgos.

¿Cuáles son los riesgos potenciales de usar bots en paneles de investigación de mercado?

Los bots pueden representar varias amenazas para la integridad y confiabilidad de los paneles de investigación de mercado, lo que potencialmente compromete la calidad de los datos y la validez de los conocimientos. Existen bots malintencionados que pueden hacerse pasar por panelistas y responder  encuestas rápidamente, lo que resulta en respuestas engañosas que no representan las opiniones de la población objetivo. En segundo lugar, los bots pueden poner en peligro la privacidad del consumidor, lo que podría provocar fraudes y robos de identidad. Los bots pueden recopilar datos personales de los encuestados sin su conocimiento o consentimiento. Las preocupaciones sobre la privacidad de los datos y las prácticas de investigación ética pueden dañar la reputación de una empresa y socavar la confianza de los clientes en la integridad del panel.

¿Qué métodos se utilizan para identificar bots?

Se pueden utilizar varios métodos para identificar bots en paneles de investigación de mercado, que incluyen:

• Análisis de comportamiento: Los investigadores de mercado pueden analizar el comportamiento de los encuestados para detectar signos de bots. Por ejemplo, los bots suelen completar encuestas de forma rápida y sin errores, mientras que los humanos suelen mostrar cierto nivel de vacilación o error.
• Herramientas de detección: Algoritmos avanzados, códigos CAPTCHA y otros desafíos de verificación y auditorías de datos son solo algunas de las herramientas sólidas disponibles para detectar y eliminar bots de los paneles de investigación de mercado.

¿Qué está haciendo ThinkNow para combatir los bots?

Para combatir las crecientes preocupaciones sobre los bots en el panel de investigación de mercado, ThinkNow está duplicando las medidas de calidad y seguridad existentes y nuevas. A continuación, se enumeran algunos protocolos para el pre y post registro:

• Autenticación de dos factores: Los bots están diseñados para operar de manera totalmente automatizada y predecible. La autenticación de dos factores introduce una variable dinámica, en este caso, un código generado aleatoriamente que un bot no puede replicar.
• Geolocalización: La geolocalización garantiza que las respuestas a las encuestas provengan de personas físicamente ubicadas dentro de las áreas geográficas especificadas, garantizando que la muestra refleje con precisión la diversidad de opiniones, comportamientos y preferencias de diferentes regiones y culturas.
• Llamada de bienvenida: La verificación por teléfono garantiza la identidad del panelista, el ajuste cultural y la precisión lingüística, lo que evita registros fraudulentos y garantiza una muestra representativa.
• Grupos de redes sociales: Los grupos de redes sociales proporcionan una capa adicional de verificación de la identidad de los panelistas. Al ser parte de un grupo verificado, se reduce la probabilidad de participación de perfiles falsos o bots, mejorando la autenticidad de los participantes y, por lo tanto, sus respuestas.
• Geo verificación a través del boletín de noticias: La distribución del boletín valida las direcciones de correo electrónico de los panelistas, manteniendo una lista de contactos actualizada. Los píxeles de seguimiento incrustados en los boletines proporcionan ubicaciones aproximadas de los usuarios a través de direcciones IP.

Se utilizan controles adicionales del panel antes y después de la encuesta para detectar y disuadir a los bots, como redirecciones de seguridad S2S, redirecciones de cifrado SHA-1, huellas dactilares del dispositivo y reconciliación de datos.

Conclusión

Los bots seguirán siendo una preocupación creciente en la investigación de mercado a medida que la inteligencia artificial continúe avanzando. Al tomar medidas para mitigar los riesgos asociados con el uso de bots, las empresas pueden garantizar que sus datos de investigación de mercado sean precisos, confiables y éticos.

In today’s digital landscape, most of us have interacted with bots – computer programs that mimic human conversation and automate tasks. Chatbots can, for example, handle website inquiries, monitor social media channels, and collect survey data. Bots are ubiquitous and used in a variety of applications, including market research. This blog addresses frequently asked questions about bots in market research panels.

How do bots work in market research?

In some ways, bots are revolutionizing the market research industry. Instead of relying on traditional survey methodologies, bots can help to conduct surveys quickly and more efficiently, potentially saving clients time and money. By embedding bots on popular destinations for consumers, like social media platforms, it makes it easier for them to participate in surveys. Bots can also be used to analyze large amounts of data quickly and efficiently,  providing much-needed insights in a fraction of the time. But, with the benefits come the risks.

What are the bots that threaten online market research panels?

Bots can pose several threats to the integrity and reliability of market research panels, potentially compromising data quality and the validity of insights. There are malicious bots that can pose as panelists and quickly respond to surveys, resulting in deceptive responses that do not represent the opinions of the target population.

Secondly, bots can jeopardize consumer privacy, which could lead to fraud and identity theft. Bots may collect personal data from respondents without their knowledge or consent. Concerns about data privacy and ethical research practices can damage a company’s reputation and undermine clients’ confidence in the panel's integrity.

What methods are used to identify bots?

Several methods can be used to identify bots in market research panels, including:

• Behavioral analysis - market researchers can analyze the behavior of respondents to detect signs of bots. For instance, bots often complete surveys quickly and error-free, whereas humans typically exhibit some level of hesitation or error.
• Detection tools – advanced algorithms, CAPTCHA codes and other verification challenges and data audits are just a few of the robust tools available to detect and remove bots from market research panels.

What is ThinkNow doing to combat bots?

To combat growing concerns over bots in our market research panels, ThinkNow is doubling down on existing and new quality and security measures. Listed below are a few protocols for pre and post-registration:

• Two-factor authentication: Bots are designed to operate in a fully automated and predictable manner. Two-factor authentication introduces a dynamic variable, in this case, a randomly generated code that a bot cannot replicate.
• Geolocation: Geolocation ensures that survey responses originate from individuals physically located within the specified geographical areas, guaranteeing that the sample accurately reflects the diversity of opinions, behaviors, and preferences of different regions and cultures.
• Welcome Call: Phone verification ensures panelist identity, cultural fit, and linguistic accuracy, preventing fraudulent registrations and ensuring a representative sample.
• Social media groups: Social media groups provide an additional layer of verification of the identity of panelists. By being part of a verified group, the probability of participation by fake profiles or bots is reduced, improving the authenticity of participants and, therefore, their responses.
• Geo verification through newsletter: Newsletter distribution validates panelist email addresses, maintaining an up-to-date contact list. Tracking pixels embedded in newsletters provide approximate user locations via IP addresses.

Additional panel controls are used pre and post-survey to detect and deter bots, such as S2S security redirects, SHA-1 encryption redirects, device fingerprint and data reconciliation.

Conclusion

Bots will continue to be a growing concern in market research as artificial intelligence continues to advance. By taking steps to mitigate the risks associated with bot usage, companies can ensure that their market research data is accurate, reliable, and ethical.

Los paneles de investigación en línea en América Latina a menudo subrepresentan a los grupos de bajos ingresos, a pesar de constituir una proporción más grande de la población. Por otro lado, las personas con ingresos más altos a menudo están sobrerrepresentadas en comparación con aquellas de ingresos más bajos. Esta discrepancia puede plantear problemas potenciales, pero los investigadores de mercado pueden mitigar estos riesgos considerando cuidadosamente la composición de sus paneles y tomando medidas para garantizar que obtienen datos precisos y fiables.

Factores que afectan la Representación Socioeconómica

Muchas variables influyen en las disparidades en los paneles de investigación en línea. Principal entre ellas, en América Latina, está el hecho de que las personas de contextos socioeconómicos más bajos podrían no participar tan activamente en encuestas de investigación de mercado por varias razones:

• Acceso limitado a Internet: Las encuestas en línea son un método común para la recopilación de datos en la investigación de mercado, pero las personas de contextos socioeconómicos más bajos podrían tener acceso limitado o nulo a Internet en sus hogares. Esto dificulta su participación en las encuestas en línea.
• Altos costos de Internet: Los servicios de Internet no siempre son asequibles y a menudo se adquieren en paquetes individuales a través de empresas de telecomunicaciones, con datos limitados. El acceso compartido a Internet en el hogar es una rareza, lo que dificulta que todos los miembros de la familia tengan acceso.
• Limitaciones de tiempo: Las personas con recursos limitados a menudo tienen jornadas laborales largas y múltiples responsabilidades, lo que deja poco tiempo libre. Participar en encuestas podría no ser una prioridad cuando están ocupados con el trabajo y el cuidado de la familia.
• Falta de incentivos: Algunas encuestas ofrecen incentivos económicos o regalos como recompensa por la participación. Las personas de fondos socioeconómicos más bajos podrían percibir estos incentivos como insuficientes para justificar el tiempo y el esfuerzo económico requerido.
• Falta de confianza: Algunas personas pueden desconfiar de las encuestas y ser escépticas sobre proporcionar información personal o financiera en línea. Esta desconfianza podría ser más pronunciada en aquellos menos familiarizados con la tecnología y el entorno en línea.
• Falta de identificación: Las preguntas de la encuesta podrían estar formuladas de manera que no se relacionen con las experiencias y preocupaciones de aquellos de fondos socioeconómicos más bajos. En consecuencia, podrían no sentirse representados y no tener interés en participar.
• Barreras culturales y lingüísticas: Las encuestas podrían diseñarse en un idioma inaccesible para todos, y las diferencias culturales pueden influir en la interpretación de las preguntas.

Mejorando la Representación en los Paneles de Investigación en Línea

Para fomentar la participación de personas con antecedentes socioeconómicos más bajos en las encuestas de investigación de mercado, es esencial considerar y abordar las posibles barreras que enfrentan. Esto se puede lograr ofreciendo incentivos relevantes, empleando métodos diversos de recopilación de datos y diseñando encuestas inclusivas y culturalmente sensibles. Tomar estas medidas mejorará con el tiempo la representación socioeconómica en los paneles de investigación en línea de América Latina.

Para obtener más información sobre cómo motivar a los panelistas y adaptar el lenguaje de los cuestionarios, consulta el blog de ThinkNow.