Qualitative research tells you why. Quantitative research tells you how many, how often, and how much. Surveys, analytics, A/B tests, benchmark studies, and unmoderated testing at scale give product teams the numbers they need to prioritise, prove impact, and convince stakeholders — which issues affect most users, which segments behave differently, and whether a redesign actually moved the metric.
Quantitative UX research is easy to get wrong. Badly worded surveys produce confident but meaningless answers. Small or biased samples mislead. Analytics without context show what happened but not whether it matters. And numbers presented without a clear story rarely change decisions. Good quantitative researchers design studies carefully, choose the right sample, and connect the data to real user behaviour.
The best quantitative research agencies combine statistical rigour with qualitative insight, so teams know both what is happening and why. For a deeper look at methods, read our quantitative research masterclass.
For NPCI's BHIM, NetBramha ran user research and testing across Metro, Tier 2, and Tier 3 cities with diverse demographics, gathering both quantitative and qualitative data to validate design decisions, alongside competitive benchmarking of key journeys. After the redesign, BHIM's users grew 30% to more than 128 million and monthly transactions rose 323% across 2025 — measurable outcomes the research helped make possible.
For Niyo, NetBramha combined qualitative and quantitative methods to understand travel and forex behaviour and benchmark the product against competitors. For HoABL, a data-informed, behaviour-driven redesign addressed high bounce rates and short sessions, increasing engagement 2.6×.
Your users span very different segments — BHIM research covered Metro, Tier 2, and Tier 3 cities.
Numbers and stories are both needed — BHIM and Niyo combined quantitative and qualitative data.
You need to prove impact — HoABL's engagement rose 2.6× after a data-informed redesign.
Advanced statistical modelling — for conjoint, segmentation modelling, or econometrics, confirm in-house capability.
Analytics implementation — confirm who sets up tracking and experimentation tools.
Nielsen Norman Group is known for research methods and UX benchmarking guidance, useful when measurement must be done properly.
Methodology matters — their methods are widely respected.
You need UX benchmarks — they help define and track UX metrics.
Your team wants training — they run well-known courses.
Design execution — they advise; confirm who acts on findings.
Availability — confirm consulting capacity.
Huge pairs design with data, analytics, and experimentation, useful for high-traffic platforms that can test changes with real users.
Traffic supports testing — they run experiments at scale.
Analytics data is rich — they design around data.
Personalisation matters — they design for segments.
Standalone research — confirm research-only engagements.
Small scope — their model suits larger programmes.
Deloitte Digital combines customer research with data and analytics consulting, useful for enterprises building measurement into large programmes.
Data infrastructure is involved — they connect research to analytics platforms.
Programmes are large — they staff at scale.
Executives need business cases — they connect data to strategy.
UX depth — confirm the design research team's role.
Cost — large consultancy rates apply.
frog connects research and market insight to strategy, useful for sizing opportunities before investing in new products.
New markets are explored — they connect insight to strategy.
Investment decisions are pending — they support business cases.
Hardware is involved — they research physical and digital.
Budget — their model suits large programmes.
Product-level UX metrics — confirm detailed UX measurement is included.
Designit's studios across regions make it useful for running studies in several markets and carrying findings into delivery.
Markets are multiple — their studios span regions.
Build follows research — Wipro's capacity supports delivery.
Enterprise systems are involved — they handle complex products.
Boutique attention — confirm team composition.
Quantitative depth — ask for directly relevant studies.
Do they design studies properly? Ask how they write survey questions, choose metrics, and avoid bias.
How do they choose samples? Ask about sample sizes, segments, and how they reach hard-to-reach users.
Do they combine numbers with why? Ask how they pair quantitative results with qualitative insight.
Can they measure impact over time? Ask how they set baselines and track UX metrics after launch.
Will the results change decisions? Ask how they present data so stakeholders act on it.
How much does quantitative UX research cost?
A focused survey or benchmark study typically runs $10,000–$40,000. Multi-segment or multi-market programmes typically run $40,000–$150,000.
Should I use quantitative or qualitative research?
Use qualitative research to understand why and discover problems. Use quantitative research to measure how widespread they are and prove impact. Most good programmes use both.
How long does a quantitative study take?
Surveys and benchmark studies take 3–6 weeks. Larger multi-market programmes take 6–12 weeks.
UX practice and digital product expectations differ by region — what signals quality and trust to a buyer in the US or UK is different from Dubai or Singapore. An agency with direct market experience brings depth that cross-industry portfolios cannot substitute.
Quantitative Research at NetBramha →
