Quantitative research for smarter brand decisions

Beyond brand tracking, we can connect: media spend/SOV, share of search, app telemetry/downloads, CRM/loyalty & CLTV, sales/revenue, CX/NPS, social sentiment & reviews, channel analytics and macro indicators.

We protect quantitative research data quality through rigorous survey sampling and fraud prevention processes. We work with pre-qualified sample partners, blend panels where appropriate, apply interlocking quotas and maintain minimum completes per cell to support reliable weighting.

To verify participant authenticity, we use geoIP validation, cross-panel IP and respondent deduplication, digital fingerprinting and secure survey links. We also prevent bots and fraudulent responses using reCAPTCHA, honeypots, copy-and-paste restrictions, speed and straight-lining detection, verbatim quality reviews and a composite Quality Score.

Throughout fieldwork, our teams monitor real-time data collection dashboards, achievement alerts and supplier performance to identify issues early and ensure high-quality, reliable quantitative insights

Quality is built into every stage of our quantitative research process, from project setup through survey programming, data collection, analysis and reporting.

Clear ownership across project, production and analysis teams ensures close control of sample, survey programming, data handling and output delivery. We apply robust quality assurance protocols at every stage, combining automated validation checks with expert human review to maintain data accuracy, consistency and trend integrity. Controlled versioning, early data visibility during fieldwork, and validation between datasets, charts and reports help us identify issues early and ensure outputs are reliable, decision ready and fit for purpose.

We use statistical best practices to ensure quantitative research findings remain reliable and accurately interpreted, even when some markets or audience segments have smaller sample sizes.

We apply weighting and advanced analysis only when sample sizes are statistically robust, and we suppress low-base results to avoid drawing unreliable conclusions. Built-in quality checks, including statistical significance testing and cross-format data validation, help identify anomalies before results are delivered.

Where limitations exist, we clearly label sample size considerations and confidence levels in dashboards and reports so stakeholders can distinguish between statistically robust findings and directional insights.

We design AI with governance, transparency and human oversight at its core. Every AI capability in our products and workflows is validated against defined quality standards, supported by robust security and compliance controls and includes human review for high-impact decisions to ensure outputs remain accurate, reliable and transparent.

We believe in informed benchmarking as opposed to comparison against norms. We use relevant benchmarking based on a campaign’s strategic intent and context, not generic, mixed purpose norms that can mislead. This approach ensures quantitative research findings are interpreted against comparable situations rather than mixed-purpose datasets that can produce misleading conclusions, helping you make more confident, evidence-based business decisions.

We operate in line with recognised market research, data privacy and information security standards, as well as our clients' information security (InfoSec), privacy and compliance requirements, with vendor due diligence, confidentiality, data handling and regulatory controls. We’ll list specific certifications (e.g., ISO/SOC) on the site once confirmed by Legal/IT.

We conduct quantitative research across the globe and have over 35 years’ experience partnering with large brands on studies covering 50+ markets. We will work in any market that that passes our feasibility restrictions check. For example, if there are sanctions-related constraints, then certain markets may require alternative research methods to ensure quality.