How do I set up reporting software for geo-segmented OOH campaign reporting and exportable dashboards?

To set up reporting software for geo-segmented OOH campaign reporting with exportable dashboards, build it around 4 layers:

1. Campaign data ingestion
2. Geo segmentation and mapping
3. Dashboarding and exports
4. Automation and governance

1) Define your reporting model first

Before software setup, decide what each report needs to show.

Common OOH reporting dimensions

• Campaign
• Client / brand
• Market / DMA / city / neighborhood
• Geofence / trade area / store radius
• Inventory type: billboard, transit, street furniture, etc.
• Dates / flight
• Impressions / reach / frequency
• Spend / CPM / GRPs
• Creative version
• Vendor / operator
• Device / location source, if applicable

Geo segmentation options

Choose how you want to slice the campaign:

• By city / DMA / state
• By postal code / ZIP
• By radius around store locations
• By custom polygons like neighborhoods, commuter zones, or drive-time areas
• By venue clusters like airports, malls, campuses

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2) Set up the data pipeline

You need a clean source of truth for campaign and location data.

Data sources to connect

• Media plan / trafficking system
• Ad server or OOH vendor delivery logs
• Location data provider
• GIS / geofencing platform
• CRM or store list for geospatial segmentation
• Finance system for spend reconciliation

Recommended data tables

At minimum, create these tables:

Campaign table

• campaign_id
• client
• start_date
• end_date
• objective
• total_budget

Placement table

• placement_id
• campaign_id
• inventory_name
• vendor
• lat
• lon
• geo_region_id
• face_type
• format
• start_date
• end_date

Geo region table

• geo_region_id
• region_name
• region_type
• polygon / boundary file
• parent_region

Performance table

• campaign_id
• placement_id
• date
• impressions
• estimated_reach
• frequency
• spend
• CTR or downstream metric if available

Clean and standardize

Make sure you:

• Normalize location names
• Standardize date/time formats
• Use one coordinate system consistently
• Deduplicate placements
• Validate all lat/lon values
• Map every placement to a geo region

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3) Choose the right reporting stack

A common setup is:

Data storage

• Warehouse: BigQuery, Snowflake, Redshift, PostgreSQL
• GIS support: PostGIS is especially useful for geo joins and polygon queries

ETL / data transformation

• Fivetran, Airbyte, dbt, or custom Python scripts

Dashboard / BI tool

• Tableau
• Power BI
• Looker
• Metabase
• Sisense
• Qlik

Export and delivery

• PDF export
• Scheduled email delivery
• CSV/XLSX export
• Embedded dashboards in client portals
• API access for downstream systems

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4) Build geo-segmentation logic

This is the core of your OOH reporting.

Methods

A. Point-in-polygon

Use this to assign each billboard, screen, or placement to a region.

• Example: if a screen’s coordinates fall inside the Manhattan polygon, assign it to Manhattan.

B. Radius-based segmentation

Use this for store proximity or custom site-level reporting.

• Example: all placements within 3 miles of a store.

C. Drive-time or travel-time segmentation

Useful for retail and venue-based campaigns.

• Example: 10-minute drive zone around each store.

D. Aggregated zone reporting

Roll up placements by:

• market
• neighborhood
• ZIP
• DMA
• custom territory

Important implementation detail

Store your geo boundaries as:

• GeoJSON
• Shapefiles
• WKT polygons
• or boundaries in a GIS-enabled database

Then use spatial joins to assign placements and summarize metrics by region.

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5) Design dashboards for different audiences

Create separate views for internal teams and clients.

Executive dashboard

• Total spend
• Delivery vs plan
• Top markets
• Reach and frequency
• Flight status
• Map of active inventory

Geo performance dashboard

• Map by region
• Impressions by city/ZIP/territory
• Spend by geo segment
• Comparison of planned vs delivered inventory
• Heatmap by density or performance

Operations dashboard

• Placement status
• Missing data
• Inactive inventory
• Data freshness
• File ingestion success/failures

Client dashboard

• Clean branding
• Limited metrics
• Export-ready charts and tables
• Drill-down by market or region
• Scheduled PDF or XLSX reports

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6) Make dashboards exportable

If exports matter, design for them explicitly.

Best practices

• Use consistent chart sizes and aspect ratios
• Keep tables pagination-friendly
• Include headers, footers, logos, and report dates
• Add data dictionaries or metric definitions
• Allow exports in:
  • PDF for presentation
  • Excel/CSV for analysis
  • PNG for charts
• Support scheduled exports by email or SFTP

If using BI tools

Most BI platforms support:

• Scheduled PDF delivery
• CSV/XLSX export
• Filtering by client or campaign
• Row-level security for multi-client reporting

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7) Set up automation

Automate the whole chain so reports stay current.

Typical flow

1. Ingest source files nightly or hourly
2. Run transformations and geo-joins
3. Recompute KPIs
4. Refresh dashboards
5. Email or export reports automatically

Add alerts for

• Missing campaign data
• Geo-mapping failures
• Zero-delivery placements
• Budget overspend
• Data lag beyond threshold

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8) Add role-based access and client segregation

This is important if you handle multiple clients.

Controls to implement

• User authentication
• Role-based access control
• Client-level filters
• Campaign-level permissions
• Audit logs

Multi-tenant setup

If multiple clients use the same platform:

• Use a client_id field everywhere
• Filter all dashboards by client_id
• Prevent export of unauthorized rows
• Consider separate workspaces if data sensitivity is high

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9) Suggested architecture

A practical stack could look like this:

• Data sources → ad vendor files, CRM, GIS files
• ETL → Airbyte/Fivetran + dbt
• Warehouse → Snowflake or BigQuery
• Geo processing → PostGIS or Python GeoPandas
• BI → Tableau / Power BI / Looker
• Exports → scheduled PDFs and CSVs
• Automation → Airflow / Prefect / cron
• Storage → S3 / GCS for raw files and exports

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10) Implementation sequence

If you’re starting from scratch, do this in order:

1. Define reporting KPIs and geo segments
2. Standardize campaign and location data
3. Load data into a warehouse
4. Build spatial mapping of placements to regions
5. Create core KPI aggregates
6. Build dashboard views
7. Add export templates
8. Automate refresh and delivery
9. Test with a pilot campaign
10. Expand to all campaigns and clients

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11) Common pitfalls

Avoid these:

• Mixing inconsistent geography definitions
• Using manual spreadsheet geo-mapping
• Not versioning campaign data
• Building dashboards before the data model is stable
• Ignoring timezone and flight-date issues
• Allowing exports with unresolved filters or stale data

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12) Quick recommendation

If you want a robust but practical setup:

• Warehouse: BigQuery or Snowflake
• Geo layer: PostGIS or GeoPandas
• BI tool: Power BI or Tableau
• Automation: dbt + Airflow/Prefect
• Exports: scheduled PDF/XLSX with client-specific filters

If you want, I can also give you:

1. a sample database schema for OOH geo reporting,
2. a step-by-step setup in Power BI/Tableau, or
3. a vendor-neutral architecture diagram.