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Built on transparent methodology.
Every huq estimate is tested against independent, real
world footfall and we publish how far off we are. We
measure that gap with MAPS, twice a year, by country.
92%
Median MAPE vs landlord-reported footfall
500M+
Malls tested across 8 countries
1,000+
Full evaluation runs published yearly
One honest number: how far off are we?
We measure MAPE, Mean Absolute Percentage Error: the average gap between huq's footfall estimate and the landlord-reported figure for the same place. Lower is better. We test it across hundreds of real malls, publish the result by country twice a year, and treat a median below 30% as a healthy operating bound.
Measured against independent landlord-reported footfall
Published by country, twice a year
Backed by continuous automated checks between runs
Measured against the market. Results published.
Median absolute error versus landlord-reported figures, per country. A median below 30% is considered healthy our best market, Germany, lands at 13.5%.
Worked examples, real sites.
We compare model output against independently collected footfall, site by site. Here are two, measured against operator-supplied counts.
Estimates, held to a published standard.
huq outputs are modelled estimates of real-world activity, built from anonymised behavioural signals, and we're explicit about what that means.
01
Estimates, never counts
Every figure is a modelled estimate, presented with its published error rate, never dressed up as an exact headcount.
02
Re-validated every release
Each model release is re-tested against independent benchmarks, and the error is recalculated and republished.
03
To inform, not to decide
Our data is built to inform professional judgement: strong evidence for a decision, not a substitute for one.
Four commitments behind the numbers.
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Data quality
Continuous monitoring of signal, sample stability and coverage across every location and time window.
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Independent verification
Model outputs benchmarked against trusted third-party footfall and validation sources.
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Privacy first
Anonymised, aggregated and deduplicated by design, so individuals are never identified. GDPR compliant for commercial use.
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Continuous improvement
Methodology refined on a rolling basis as new signals, validation sources and modelling techniques mature.
More than Footfall
Understanding places requires multiple behavioural dimensions six output families combined into a single intelligence layer.
Built on broad, continuous coverage.
22
Countries with a Ficus in EU,ME & APAC (Limited)
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1000+
Location Tested per Country
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Continuous
Collection, Not Panels Or Periodic Counts
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10+ Years
Historical In our Longest Running Markets
Common questions about methodology.
How huq collects, validates and applies behavioural intelligence, and what makes it different.
See the intelligence behind
every insight.
Discover how huq transforms mobility data into trusted intelligence for decision-making, investment, planning, and performance measurement.
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Empowering strategic teams with the world's most accurate and accessible location intelligence.
Contact
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