How do we robustly validate new co-viewing models?

To robustly validate a new co-viewing model (which estimates multiple viewers watching together), you need to test its predictions against known ground truth and across diverse scenarios. For example, if the model estimates how many people are jointly watching a stream, you'd compare its output to real observations - like data from households or lab tests where actual co-viewing counts are known - to gauge accuracy. It's important to evaluate the model with different content types and audience segments: a model might perform well for family TV viewing but should also be validated on live sports or kids' shows, for instance. Use statistical checks (error rates, confidence intervals) and benchmark against any existing co-viewing metrics or prior models. In short, treat the new model like a hypothesis: continuously compare its estimates to reality and refine it until it reliably reflects real-world co-viewing patterns before rolling it out widely.

Does synthetic sample inhibit insights from real-world edge cases?

Synthetic sample data (such as simulated or AI-generated respondents) can be useful for scale or modeling, but it may obscure insights that come from real-world edge cases. By design, synthetic data tends to reflect the patterns of the majority in the training data. That means unusual behaviors or outlier opinions--often those "edge cases" critical for innovation or risk detection--might be underrepresented or missed entirely. In a real sample, a few unique participants might highlight a rare issue or novel use case that a synthetic approach wouldn't produce. So if your research relies on capturing those fringe scenarios, leaning too heavily on synthetic sample can indeed inhibit your ability to see them. A hybrid approach (using synthetic data for broad trends but real respondents for depth) is one way to balance efficiency with authenticity in insights.

What safeguards prevent prompt leakage and hidden PII exposure in research?

Researchers protect against prompt leakage and hidden PII exposure by anonymizing sensitive data and using secure, controlled AI processes. A critical safeguard is to strip out or pseudonymize personally identifiable information (names, emails, etc.) from data before it's uploaded or sent to an AI model, so the prompts themselves contain no real identifiers. Teams also often use on-premises or private large language models (rather than public cloud APIs) for analysis, ensuring that prompts and responses aren't leaking to third-party servers. Additionally, strict access controls and audit trails are put in place — only authorized personnel can view the raw data or AI outputs — and everything is reviewed for accidental PII before findings are shared. Many modern AI tools even offer features like prompt encryption or PII detection filters, which organizations enable to further prevent any hidden personal data from slipping through.

How do we prove measurement quality forecasts ad yield and inventory value?

Prove that better measurement quality translates into higher ad yield and inventory value by demonstrating a clear link between improved metrics and revenue outcomes. In practice, this means collecting evidence that when you use more accurate or comprehensive measurement (for example, a new attention metric or cross-platform reach metric), it correlates with or predicts better ad performance and pricing. Run side-by-side comparisons or pilot tests: for instance, split your ad inventory or campaigns so that some use the advanced measurement and others use the standard one, and then show that the segment with quality metrics achieved higher fill rates or CPMs (cost per thousand impressions). If advertisers pay more or renew more often when they see these quality metrics, that's proof of value. Over time, analyzing historical data can also help: demonstrate that inventory scored highly on the new measurement ultimately delivered greater ROI for advertisers, which justifies a premium. By presenting these data-backed results - ideally including statistical lifts or case studies - you can confidently claim that strong measurement directly drives better yield and makes each ad impression more valuable.

How can we leverage lean testing while maintaining confidence and rigor?

Lean testing can be a powerful approach if you keep each experiment focused and methodologically sound, ensuring you don't sacrifice rigor for speed. The key is to design "small" tests that still have clear hypotheses, control groups (when applicable), and proper metrics. For example, instead of one large study, you might run a series of micro-experiments or A/B tests that each examine a specific tweak (like a new ad message or minor product feature) on a modest sample. Evaluate each quickly but thoughtfully; if multiple lean tests all point to the same insight, you gain confidence in the result. Maintain rigor by analyzing statistical significance or effect sizes appropriately (even if samples are smaller) and by documenting assumptions. Also, peer review your lean test plans within the team to catch any design flaws early. In short, you leverage lean testing by iterating rapidly, but you maintain trust in the findings by upholding sound experimental principles throughout.

Material

Name: Material
Address: 440 Park Ave South, New York, New York, 10016, US
Telephone: (310) 553-0550

Material builds consumer intelligence into brands and experiences to strengthen customer relationships and drive business impact.

Full Service

Qualitative Research

Quantitative Research

Featured in Greenbook's Buyer's Guides

VISIT OUR WEBSITE

Last updated on October 6th, 2026

Why choose Us

Intelligent Growth

Behavioral Analytics

Brand Marketing & CRM

Customer Experience

Product Design

Features and attributes

What is Material?

Material is a global growth intelligence company. For 50 years, Material has built behavioral science into the insights, strategies, brands, experiences and platforms that move markets. Its culture-shaping design studio, Aruliden, gives that work form, from identity and product to packaging and environments. Material is building the intelligence layer ambitious companies use to understand demand and act on it faster. Learn more at materialgrowth.com.

Material Services and Expertise

Explore market research solutions, industries, markets, and business issues covered by Material

Research Solutions

Customer Experience Research Advertising Research Advertising Effectiveness Research Advertising Communication Strategy Research Advertising Content Analysis Research Advertising Concept Testing Brand Identity Research Brand Image Tracking Brand Loyalty / Satisfaction Research Brand Positioning Research Corporate Image / Identity Research Brand Research - Line / Franchise Extension Business-to-Business Research Consumer Market Research Consumer Attitude & Usage Research Consumer Trends Research Demographic Analysis Geo-Demographic Research Consumer Lifestyle & Value Trends Psychological / Motivational Research Psychographic Research Consumer Purchase Behavior Research Customer Loyalty Research Customer Satisfaction Research Service Quality Needs & Measurement Research International Market Research with Consumers Media Audience Research Media Research Packaging Testing Measurement Product Market Research Concept Development Research New Product Post-Launch Tracking Product Optimization Research Product Testing Crowdtesting User Testing Promotion Development & Evaluation Research Shopper Insights Shopper Journey Mapping Public Reputation Research Strategic Market Research Market Measurement & Evaluation Studies Market Segmentation Web Analytics & Website Analysis Website Usability & UX VR - Virtual Shelf testing

Research Services

Full Service Market Research Full Service Quantitative Market Research Full Service Qualitative Market Research Hybrid & Mixed Methodology Market Research Tracking Research Market Research Consultants Advanced Analytics Data Collection Field Services Data Collection Field Audits Segmentation Analytics Statistical Analysis Emotional Measurement Data Collection Management Online Quantitative Data Collection Methods Questionnaire Design Questionnaire Coding Online Qualitative Data Collection Methods Open-End & Verbatim Response Coding Ethnography Gamification & Research Games Insight Communities (MROC)Crowdsourcing & Co-creation Implicit Association Research Interviews with Ethnic Groups Interviews with Executives & Professionals Interviews in Foreign Language Interviews with International Audiences Interviews with Consumers Interviews with Low Incidence Audiences Interviews with Healthcare Professionals Mail Questionnaires & Surveys One-on-One & In-Depth Interviews (IDI)Mobile Ethnography Mobile Insight Communities Interviews with IT Professionals Mobile Panels Mobile Qualitative Research Mobile Surveys Forecasting & Predictive Analytics Passive Data Collection on Mobile Devices Marketing & Sales Effectiveness Modeling Market Simulation & Modeling Prediction Markets Online Focus Groups Online In-Depth & One-on-One Interviews Qualitative Social Media Research

Business Issues

Business Issue: Brand / product / service launch Business Issue: Brand repositioning Business Issue: Develop, revitalise market Business Issue: Gain new customers Business Issue: Global, multinational branding Business Issue: Increase awareness Business Issue: Increase sales, volume Business Issue: Increase market share

Published on Greenbook

1 result

Data Science

Putting Synthetic Data to Work: Balancing Effort, Trust and Impact

5 min read

Explore how synthetic data enhances human insights, filling gaps, testing ideas, and shaping consumer understanding with best practices and key use ca...

Arun Kumar

Global Lead, Data & Analytics at Material

March 7, 2025

Read article

www.materialgrowth.com Send an Email (310) 553-0550

New York, NY

United States

440 Park Ave South

16th Fl

New York, NY,
United States

10016

Metro: New York (NY)

View on map

United States, United Kingdom, India, Singapore, Australia

Tiffany Francis

VP, Brand & Communications

Emma Erion-Brewer

Associate Director, Marketing

Table of content

Why Choose Us?Features and attributes What is Material?Services and Expertise Published on Greenbook Top Questions

Market Research FAQs with Expert Insights

Greenbook experts provide answers to a wide range of questions, both common and uncommon, about market research and analytics that connect to the kinds of services and expertise Material offers.

Prepared by Greenbook editors. Technology was used in the editorial process.