Placer.ai is a location-analytics platform that turns anonymized mobile-device movement into foot-traffic intelligence for retail, mixed-use, and commercial real estate. For a venue, a shopping center, or a custom trade area drawn on a map, Placer.ai estimates visits, visit frequency, dwell time, capture rates, and the demographic and psychographic profile of who is actually showing up — then benchmarks all of it against competitors and historical trends. Where a rent roll tells you who is paying today, Placer.ai tells you whether the demand that supports those rents is growing, flat, or eroding.
Retail and mixed-use investors and developers underwrite on demand, not just on the building. A center can be 95% leased and still be quietly failing if anchor visits are sliding and the trade area is shifting — and you will not see it in the rent roll until renewals come in soft. Placer.ai surfaces that demand signal early: it quantifies how many people visit a property, how often, how far they traveled, who they are, and how the asset compares to its competitive set. That is exactly the evidence a CRE team needs to support — or kill — a rent-growth assumption before it goes into a model.
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Draw a custom trade area or use Placer.ai's true trade area derived from actual visitor origins, then retrieve where visitors come from, how far they travel, and the demographic and household-income profile of the captured population.
Replaces guessed 3- and 5-mile radius assumptions with the trade area the property actually draws from. An automation can pull the measured trade-area profile into a retail or mixed-use deal screen, so demand assumptions in the model reflect real catchment rather than a circle on a map.
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When a new retail or mixed-use property enters your pipeline, this workflow enriches it with Placer.ai trade-area, visit, and demographic figures, then builds a demand snapshot on the deal card before an analyst opens it. If your partner API is live, the figures pull directly; if not, the workflow ingests the latest Placer.ai report export for the venue and parses the same numbers.
1n8n receives the property address and resolves the matching Placer.ai venue or custom trade area
2Partner-API path: n8n calls the Placer.ai partner API for trade-area profile, trailing-12-month visits, and year-over-year visit trend
3Fallback path: n8n ingests the most recent Placer.ai PDF/CSV export for the venue (uploaded or pulled from a watched Drive folder) and parses visits, trade area, and demographics
4Extract top visitor origins, captured household income, dwell time, and the YoY visit delta
Every retail and mixed-use lead arrives with a measured demand snapshot attached. Analysts review visitation evidence on first look instead of scheduling a separate Placer.ai pull mid-diligence, and weak-demand deals are flagged before they consume analyst time.
Connect Placer.ai to your workflows with powerful triggers and actions
Fire a workflow when a new Placer.ai export lands in a watched Drive or email destination, kicking off ingestion and downstream enrichment.
When the monthly Placer.ai portfolio export arrives, automatically refresh visit trends and raise decline alerts without manual handling.
Fire the competitive-benchmark assembly when a deal advances to investment-committee preparation in the CRM.
On entering IC prep, generate the Placer.ai-backed market-and-demand section so the memo is ready for the deal lead's review.
Retrieve the true or custom trade area for a venue — visitor origins, travel distance, and demographic and household-income composition. Requires a provisioned Placer.ai partner API connection.
When a retail target enters the pipeline, pull the measured trade-area profile so the deal screen reflects the property's real catchment instead of a radius assumption.
Retrieve total visits, unique visitors, frequency, and dwell time for a venue over a date range. Requires the Placer.ai partner API.
Populate the demand line of an underwriting model with trailing-12-month measured visits rather than a leasing-broker estimate.
Retrieve a property's or tenant's visit trend with year-over-year and month-over-month comparisons. Requires the Placer.ai partner API.
Run a monthly portfolio refresh that flags any asset whose visits decline beyond a threshold, surfacing renewal risk early.
Retrieve visits, capture rate, and visit share for a subject property against a defined competitive set. Requires the Placer.ai partner API.
Auto-build the competitive-position section of an IC memo from measured visit share instead of assembling it by hand.
Parse a Placer.ai PDF or CSV report export — trade area, visits, demographics, benchmark — that your existing Placer.ai seats already produce, extracting the figures into structured fields. Available immediately, no partner API needed.
Before partner-API provisioning clears, drop the venue's latest Placer.ai report into a watched folder and have the workflow extract visits and trade-area data to enrich the deal card.
Write extracted Placer.ai figures — visits, YoY trend, top origins, visitor income — into custom fields or a snapshot block on a CRM deal record.
Attach a measured demand snapshot to every new retail and mixed-use deal so analysts see foot-traffic evidence on first review.
Get started in approximately 1 hour for the export-ingestion fallback and first deal-enrichment workflow; 2-4 hours for the monitoring and IC-benchmark suite, plus partner-API swap-in once access is provisioned
Talk to your Placer.ai account team about partner API access. Because the API is provisioned through their enterprise/partner program rather than a developer portal, confirm whether your plan includes it and what the timeline is. In parallel, identify the report exports your existing seats can already produce — this is the path you will build on immediately while any API provisioning is in flight.
Do not block the project on API provisioning. Start with the export-ingestion fallback so the workflows deliver value now, then swap in the partner-API calls when access clears — the downstream automation stays the same.
Set up a watched destination — a Google Drive folder or a dedicated inbox — where Placer.ai exports land. In n8n, build a workflow triggered by a new file in that destination that parses the export (PDF text extraction or CSV parse) and maps visits, trade-area, demographics, and benchmark figures into a flat structured object.
Standardize your Placer.ai export format and date range so the parser sees a consistent layout. A reusable 'Placer Export Parser' sub-workflow keeps the field-mapping logic in one place across every workflow that consumes it.
When partner API access is live, add HTTP Request nodes for the trade-area, visits, visit-trend, and benchmark endpoints, authenticated with the credentials your Placer.ai account team issues. Point them at the same downstream mapping so the partner-API path produces the identical structured object your fallback already feeds.
Build the API and fallback paths to converge on one shared 'demand object' shape. That way switching a workflow from fallback to API is a one-node change, and you can run both during a transition period to validate the parsed figures against the API.
Create the first end-to-end flow: trigger (new retail/mixed-use deal) → resolve venue/trade area → pull demand object (API or fallback) → AI summary node → CRM write. Test it against a real property in a market you know and confirm the visits, trade area, and YoY trend land correctly on the deal card.
Add an explicit 'data source' field to the snapshot (partner API vs. parsed export, with the export date) so analysts always know how fresh the foot-traffic figures are.
Add cron triggers for the recurring flows — monthly portfolio visit-trend monitoring and on-demand IC benchmark assembly. Configure the decline-alert threshold and competitive sets, then activate and watch the first runs to confirm the deltas and alerts behave as expected.
Log a run summary (assets refreshed, declines flagged, source path used) to Slack or a sheet after each scheduled run so a parsing break or a stale export is obvious immediately.
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