How does PreMover data improve direct marketing campaigns?

PreMover data identifies households during major life transitions: when they are actively making purchasing decisions for services, products, and providers. This timing-based targeting improves conversion rates.

Does BrightCat data include personal identity information?

No. BrightCat data identifies properties and movement signals at the household level. It does not include personal identity data. Outreach is triggered at the address level.

How is PreMover data used for campaign targeting?

Marketing teams use PreMover signals to identify high-intent households and trigger campaigns based on property-level events: reaching customers when decisions are active, not passive.

What types of campaigns benefit from PreMover data?

Any campaign targeting households in transition: telecom installation, insurance quoting, home services, financial products, and retail offers tied to moving events.

How often is PreMover data updated?

PreMover data is updated weekly, capturing new listings and status changes across 5.8M+ Canadian properties.

Pre-Mover Data for Direct Marketing

The problem

Timing is everything: and most campaigns are late

Most campaigns use old data and broad segments. But people make real decisions in a narrow window, right when life changes.

By the time your mailer arrives, the choice has already been made.

How you win

Target demand at peak intent

Move-Based Campaigns

Reach households during major life transitions: when every service is up for decision.

Higher Conversion

Engage when decisions are active: not passive. Timing beats targeting every time.

Geographic Targeting

Target based on property location and movement signals: where demand is forming.

Campaign Timing

Align outreach with real-world events. Stop guessing when to reach customers.

Property data fields for direct marketing campaigns

Specific BrightCat fields and products relevant to this vertical. Every field listed below is queryable through Snowflake SQL, MCP natural language, or available in the weekly flat-file delivery.

Signal	Product	Field / Logic	What it tells you
Pre-mover signal	Listings	listing_status = NEW	Address-level targeting at peak intent
Property address	Listings	Full parsed address components	Direct mail and email campaign targeting
Property value	Listings	asking_price	Segmentation by household value
Property type	Listings	property_type, bedrooms, sqft	Household profiling for offer matching
Geographic targeting	Listings	postal_code, city, province	Campaign geo-targeting and regional segmentation
Timing signal	Listings	days_on_market	Optimal campaign window based on listing age

Sample query

-- Direct marketing: this week's mover signals by postal code
SELECT postal_code, city, province,
       COUNT(*) AS mover_signals,
       ROUND(AVG(asking_price), 0) AS avg_value,
       STRING_AGG(DISTINCT property_type, ', ') AS property_types
FROM BRIGHTCAT_LISTINGS.PRODUCT.listings_weekly
WHERE listing_status = 'NEW'
  AND file_date = (SELECT MAX(file_date)
                   FROM BRIGHTCAT_LISTINGS.PRODUCT.listings_weekly)
GROUP BY postal_code, city, province
HAVING COUNT(*) >= 3
ORDER BY mover_signals DESC;

Abstract example. Exact column names provided with access provisioning.

The consequence

If your timing is off, your campaign fails

Most campaigns arrive too late, miss the decision window, and compete for attention.

Timing drives conversion: not segmentation.

Most marketers compete for attention after the decision has been made. By then, the window is closed.

Reach households when decisions are being made

Timing is everything: and most campaigns are late

Move from segmentation to timing-based targeting

Target demand at peak intent

Built for address-level targeting

Property data fields for direct marketing campaigns

Sample query

Built on real market signals

Trigger campaigns based on real-world signals

If your timing is off, your campaign fails

About property data for direct marketing

Reach demand at the moment it forms