INDIVD ANALYTICS — A NEW DATA-CENTRIC APPROACH TO RETAIL

The research engine for retail — deployed on your floor.

We run innovative retail research on live visitor journeys and turn what it proves into changes you can defend — the same stack that runs your dashboard runs our published field studies.

Explore the platform → Every analysis is aggregated, anonymized and method-backed.

Real number · footfall concentration

42% of an average retail store's daily traffic lands in just six hours — a skew most weekly planning never accounts for.

Aggregated across anonymized retail locations. We dig into the numbers behind it in the featured analyses below. Aggregated & anonymized public figures — methodology in our papers and in-depth analyses. Computed on the Indivd Analytics platform.

Featured analyses

Deep dives with real data, reproducible methods and honest uncertainty.

Footfall

A large-format retailer's real traffic scale

~58,500/day (mean) · ~71,000/day (peak)

location_visits · aggregated · anonymized

A large-format retail location moves ~58,500 visitors/day on average (~71,000 on a peak day). Aggregated and anonymized; methodology in the accompanying paper.

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Dwell

Where a long-dwell format keeps you

Dwell up to ~54 min

dwell time · bounce · aggregated

Some retail floors hold zones for tens of minutes of dwell while letting others pass through in under a minute — a gap that is merchandising, not chance. Aggregated for privacy.

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Seasonality

The estate, read weather-compensated

≈8M planned visitor-trips/week

location_visits · store-hours-gated · background-corrected

Across the active retail estate, planned visitor-trips run in the low single-digit millions per week on a store-hours-gated, background-corrected basis (the canonical basis is ≈7.98M/week across 271 locations, forecast not observed) — with the weather separated from the signal so a movement is explained, not just reported.

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Deep diveZone trends

Which zones are heating up — and cooling down

Zone-level trends

zone week-over-week · aggregated

Across anonymized locations, some zones consistently gain share week over week while others shed it — the difference is where demand moved. Aggregated, method in our papers.

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Insights

Aggregated and anonymized patterns across our retail sample — methodology in the papers.

Sep 24New paper: a catalog of 14 feed-integrity checks for retail-visitation pipelinesPublished the methodology behind our data-integrity check catalog — covering all-zero/flat feeds, missing-calendar anomalies, gross-over-prediction events, and DAY-vs-HOUR reconciliation. Each check grounded in a real platform incident with an honest fail-closed verdict scheme; no fabricated coverage. Aggregated, anonymized, review-gated.Methodology

Sep 21Paydays drive footfall — ~+28% pooled estimateFirst causal estimate from the explainable-hybrid program: payday footfall increases by +28.3% [95% CI 14.5–43.8] across SE+CH markets (n=12,214 store-days, 143 stores). Per-market: SE +39.2%; CH +20.2% (CI crosses zero). Phase-2 extension pre-registered to resolve a disclosed ±1-day window sensitivity. Aggregated, anonymized, method-backed.Footfall

Sep 17The busiest hour is midnightIn a transit-adjacent retail format, the six busiest clock hours carry 42% of daily footfall — and the single busiest hour is 00:00, peaking at ~4,516 visitors/hour, not a trading hour. The daytime block (10:00–17:00) accounts for barely 29% of the day. Single-location case study; aggregated, anonymized, review-gated.Footfall

Sep 17Thursday out-earns the weekendThe peak trading day in a transit-adjacent retail format is Thursday, at ~2,762 visitors/hour — 18.1% above Saturday and 16.1% above Sunday. The weekend-peak assumption fails when a store lives on a flight schedule, not a retail calendar. Single-location case study; aggregated, anonymized, review-gated.Seasonality

Aug 29A store that peaks on someone else's clockAcross anonymized stores, a large share of daily traffic lands in just a few hours — often outside the expected trading peak. Aggregated pattern.Footfall

Aug 29The rhythm of a store lives in the dataAggregated trading rhythms often peak on a weekday, not the weekend — a signal the calendar alone would miss. Anonymized.Seasonality

In-depth analysis

Long-form, methodical write-ups on the data, the statistics and what they mean for retail — the home of our in-depth analysis. For formal, downloadable papers see the Papers page.

Aug 29Introducing weather-compensated visitor trendsHow Indivd Analytics separates the weather from the signal — a quiet Tuesday in context.Analysis type

Aug 29Dwell time by zone: the work of a measuring deviceA full walkthrough of one retail floor's dwell data — zone by zone.Deep dive

Papers

Formal, citable research and full-length reports — available as downloadable PDFs on the Papers page.

Papers · research archive

Research papers & full reports

Browse and download the formal scientific papers, methodology notes and in-depth reports we publish — PDFs, ready to read and cite. Browse the Papers page →

The data behind these analyses is computed on the Indivd Analytics platform — the visitation analytics stack behind dozens of retail locations. If you'd like a walkthrough with your own data, we're happy to get in touch.

Start with a proof of value

How a retailer begins with Indivd Analytics — on your own store set.

We don't expect you to take our word for it. A retail analytics engagement opens with a rep-led proof of value run against your own stores — real data, real methods, no obligation — then, if it holds up, a short pilot, then a full contract. A member of the Indivd team walks you through what the platform shows for your locations, using the same runbook we use for every evaluation.

Request a proof of value → or get in touch with our team.

This is the evaluation path — requesting it does not grant platform access. Current customers sign in to the platform directly.