Luxury Automobile Audience
Daily-refreshed U.S. in-market luxury auto buyer signals with brand-level intent flags for 19 nameplates, scored intent strength, demographic and household wealth enrichment, and deterministic HEM + MAID identity for direct activation.
Category: Automotive | Type: Data Share | Refresh: Daily | Geography: U.S. National
Overview
OEM marketing teams face a growing challenge: media costs are rising, third-party cookies are gone, and the window to reach an in-market luxury buyer is short. Most data products force you to choose between intent signals and consumer demographics, and neither tells you which specific brands a person is considering or which records are worth prioritising.
The Luxury Automobile Audience brings all of this together in a single Snowflake-native data share. Every record represents a U.S. consumer with observed browsing intent toward luxury vehicle purchase. Each record includes:
- A hashed email (HEM) and mobile ad IDs (MAIDS) for direct activation across digital channels
- An intent score (0–1) so you can focus on the strongest buyers first
- Sustained intent tracking showing how many consecutive days a person has been actively researching
- Brand-level intent flags for 19 luxury brands - Mercedes-Benz, BMW, Tesla, Porsche, Ferrari, Audi, Lexus, Land Rover, Volvo, Cadillac, Lincoln, Jaguar, Lamborghini, Rolls-Royce, Aston Martin, Bentley, Maserati, McLaren, and Bugatti
- Auto financial services signals identifying consumers who are simultaneously researching financing or leasing
- Demographic enrichment including household income, net worth, age range, gender, and homeownership status
- Geography down to ZIP
Because this dataset lives in Snowflake, your data and media teams can query it directly alongside your own CRM data - no file transfers, no third-party matching fees, no data movement. Activate it immediately through your measurement clean rooms, feed it to your DSPs via HEM, or join it to your existing audience segments.
The dataset refreshes daily with a rolling 28-day window, so you always have a current view of who is in-market right now.
Primary Use Cases
National brand campaigns: Activate HEM-matched audiences across CTV, programmatic display, and paid social to reach in-market buyers without relying on modeled or cookie-based signals. Filter to high SIGNAL_STRENGTH records (e.g., >= 0.7) to focus premium spend on the strongest prospects.
Conquest targeting: Filter for Multi-Brand intenders showing interest in competitor nameplates (e.g., BMW_INTENT = TRUE AND MERCEDES_BENZ_INTENT = TRUE) to reach cross-shoppers before they commit to a rival brand.
Finance campaign targeting: Use SECONDARY_TOPIC_COUNT to isolate buyers who are actively researching auto financing alongside vehicle models, enabling coordinated offers from both the brand and its financing team in a single campaign.
Demographic audience building: Combine AGE_RANGE, GENDER, INCOME_RANGE, NET_WORTH, and HOMEOWNER to build model-specific audiences. For example: male homeowners aged 45–54 with high net worth for flagship sedan campaigns, or 25–34 multi-brand intenders for entry-luxury conquest.
Retention and loyalty defense: Suppress your existing customers and isolate net-new intenders at peak wealth tiers with high SIGNAL_STRENGTH to protect your most valuable segments from competitor poaching.
Regional and local dealer activation: Filter by STATE, CITY, and ZIP to localize national audiences for dealer-level campaigns and regional media buys.
Measurement and attribution: Join HEM or MAIDS back to post-campaign sales data inside your Snowflake clean room to measure true incremental lift with no file exports or third-party matching required.
Audience overlap analysis: Match your CRM file against this dataset via HEM to understand what share of your known customers are currently in an active repurchase window.
What Makes This Dataset Unique
This listing combines five data layers that OEM teams typically source from multiple separate vendors, pre-joined and refreshed daily:
| Data Layer | What You Get |
|---|---|
| Identity | HEM for deterministic matching to your CRM, DSP, and clean room with no probabilistic modeling. MAIDS (mobile advertising IDs) for programmatic and mobile activation. State, city, and ZIP for geo-targeting. |
| Intent Score | Signals derived from billions of daily browsing events, scored on a continuous 0 to 1 scale via SIGNAL_STRENGTH. Higher scores indicate deeper, more sustained research behaviour. SUSTAINED_INTENT_DAYS tracks how many consecutive days a person has been actively researching, so you can distinguish someone who browsed once from someone deep in a purchase journey. |
| Brand-Level Intent Flags | Per-record boolean flags for each of the 19 luxury brands. BRAND_INTEREST_DAILY classifies each person as Single-Brand or Multi-Brand, revealing whether they are loyally researching one make or actively cross-shopping competitors. SECONDARY_TOPIC_COUNT identifies consumers simultaneously researching related topics such as auto financing. |
| Household Wealth | Net worth and household income ranges at the record level, enabling direct premium-tier segmentation without modeled wealth proxies or geo-demographic look-ups. |
| Demographics | Age range, gender, and homeownership status on every record, enabling model-specific creative targeting and audience segmentation without a separate data purchase. |
Data Dictionary
| Column | Type | Description |
|---|---|---|
INTENT_DATE | DATE | Date the intent signal was observed. The dataset contains a rolling 28-day window; use this column to filter by recency or track intent trends over time. |
HEM | VARCHAR | SHA-256 hashed email. The primary identifier for deterministic matching to DSPs, clean rooms, and CDPs. Trial accounts receive a partially masked value. |
MAIDS | ARRAY | Mobile advertising IDs associated with this consumer. Use LATERAL FLATTEN to extract individual IDs for programmatic and mobile activation. Trial accounts receive only the first ID. |
SIGNAL_STRENGTH | FLOAT | Intent score on a continuous 0 to 1 scale. Higher values indicate deeper, more sustained research behaviour. Use this to prioritize spend toward the strongest prospects (e.g., >= 0.7). |
SUSTAINED_INTENT_DAYS | INTEGER | Number of consecutive days this consumer has shown active luxury auto intent. Higher values indicate someone further along in the purchase journey rather than a casual browser. |
SECONDARY_TOPIC_COUNT | INTEGER | Count of additional research topics observed alongside vehicle intent (e.g., auto financing, leasing). A value greater than zero indicates broader purchase-readiness behaviour. |
BRAND_INTEREST_DAILY | VARCHAR | Classifies the consumer as Single-Brand (researching one make) or Multi-Brand (actively cross-shopping). Multi-Brand intenders are prime targets for conquest campaigns. |
MERCEDES_BENZ_INTENT | BOOLEAN | TRUE if the consumer is actively researching Mercedes-Benz. |
BMW_INTENT | BOOLEAN | TRUE if the consumer is actively researching BMW. |
AUDI_INTENT | BOOLEAN | TRUE if the consumer is actively researching Audi. |
VOLVO_INTENT | BOOLEAN | TRUE if the consumer is actively researching Volvo. |
PORSCHE_INTENT | BOOLEAN | TRUE if the consumer is actively researching Porsche. |
LEXUS_INTENT | BOOLEAN | TRUE if the consumer is actively researching Lexus. |
LAMBORGHINI_INTENT | BOOLEAN | TRUE if the consumer is actively researching Lamborghini. |
FERRARI_INTENT | BOOLEAN | TRUE if the consumer is actively researching Ferrari. |
LAND_ROVER_INTENT | BOOLEAN | TRUE if the consumer is actively researching Land Rover. |
CADILLAC_INTENT | BOOLEAN | TRUE if the consumer is actively researching Cadillac. |
JAGUAR_INTENT | BOOLEAN | TRUE if the consumer is actively researching Jaguar. |
ROLLS_ROYCE_INTENT | BOOLEAN | TRUE if the consumer is actively researching Rolls-Royce. |
ASTON_MARTIN_INTENT | BOOLEAN | TRUE if the consumer is actively researching Aston Martin. |
BENTLEY_INTENT | BOOLEAN | TRUE if the consumer is actively researching Bentley. |
MASERATI_INTENT | BOOLEAN | TRUE if the consumer is actively researching Maserati. |
MCLAREN_INTENT | BOOLEAN | TRUE if the consumer is actively researching McLaren. |
LINCOLN_INTENT | BOOLEAN | TRUE if the consumer is actively researching Lincoln. |
TESLA_INTENT | BOOLEAN | TRUE if the consumer is actively researching Tesla. |
BUGATTI_INTENT | BOOLEAN | TRUE if the consumer is actively researching Bugatti. |
CITY | VARCHAR | City of residence. Use with STATE and ZIP for regional and dealer-level campaign targeting. |
STATE | VARCHAR | Two-letter U.S. state code. |
ZIP | VARCHAR | Five-digit ZIP code. Use for geo-targeted campaigns and dealership trade area analysis. |
ZIP4 | VARCHAR | ZIP+4 code for higher-precision geographic targeting. |
GENDER | VARCHAR | Gender: M (male), F (female), or U (unknown). |
AGE_RANGE | VARCHAR | Household age range (e.g., 25-34, 35-44, 45-54, 55-64, 65 and older). |
INCOME_RANGE | VARCHAR | Household income range (e.g., $75,000 to $99,999, $100,000 to $149,999, $250,000+). |
NET_WORTH | VARCHAR | Household net worth range (e.g., $750,000 to $999,999, More than $1,000,000). |
HOMEOWNER | VARCHAR | Homeownership status: 'true', 'false', or NULL. Homeowners correlate with higher wealth and purchase readiness. |
Sample Queries
1. High-Value Prospect Identification
Find individuals showing the strongest buying signals: sustained interest over multiple days, cross-category research, and multi-brand consideration.
SELECT
hem,
maids,
signal_strength,
sustained_intent_days,
secondary_topic_count,
brand_interest_daily,
state,
city,
age_range,
income_range,
net_worth
FROM
signal_products.luxury_automobile_audience
WHERE
intent_date = (
SELECT MAX(intent_date)
FROM signal_products.luxury_automobile_audience
)
AND signal_strength >= 0.7
AND sustained_intent_days >= 3
ORDER BY
signal_strength DESC
LIMIT 1000;2. Brand Competitive Landscape by State
Compare brand intent share across states on the most recent day. Useful for regional media planning and dealership-level targeting.
SELECT
state,
COUNT(*) AS total_intenders,
SUM(mercedes_benz_intent::INT) AS mercedes_benz,
SUM(bmw_intent::INT) AS bmw,
SUM(audi_intent::INT) AS audi,
SUM(tesla_intent::INT) AS tesla,
SUM(porsche_intent::INT) AS porsche,
SUM(land_rover_intent::INT) AS land_rover,
SUM(lexus_intent::INT) AS lexus,
SUM(volvo_intent::INT) AS volvo,
SUM(cadillac_intent::INT) AS cadillac,
SUM(lincoln_intent::INT) AS lincoln,
SUM(ferrari_intent::INT) AS ferrari,
SUM(lamborghini_intent::INT) AS lamborghini,
SUM(jaguar_intent::INT) AS jaguar,
SUM(rolls_royce_intent::INT) AS rolls_royce,
SUM(aston_martin_intent::INT) AS aston_martin,
SUM(bentley_intent::INT) AS bentley,
SUM(maserati_intent::INT) AS maserati,
SUM(mclaren_intent::INT) AS mclaren,
SUM(bugatti_intent::INT) AS bugatti
FROM
signal_products.luxury_automobile_audience
WHERE
intent_date = (
SELECT MAX(intent_date)
FROM signal_products.luxury_automobile_audience
)
AND state IS NOT NULL
GROUP BY
state
HAVING
total_intenders >= 50
ORDER BY
total_intenders DESC;3. Intent Momentum: Who Is Heating Up?
Identify individuals whose signal strength is increasing over the window. A rising score indicates deepening research behaviour, ideal for timely outreach or retargeting.
WITH recent AS (
SELECT
hem,
intent_date,
signal_strength,
LAG(signal_strength) OVER (
PARTITION BY hem
ORDER BY intent_date
) AS prev_strength
FROM
signal_products.luxury_automobile_audience
WHERE
intent_date >= DATEADD(
DAY,
-7,
(SELECT MAX(intent_date) FROM signal_products.luxury_automobile_audience)
)
)
SELECT
hem,
COUNT(*) AS days_active,
MIN(signal_strength) AS earliest_strength,
MAX(signal_strength) AS latest_strength,
MAX(signal_strength) - MIN(signal_strength) AS strength_change,
SUM(CASE WHEN signal_strength > prev_strength THEN 1 ELSE 0 END) AS days_increasing
FROM
recent
GROUP BY
hem
HAVING
days_active >= 3
AND strength_change > 0
ORDER BY
strength_change DESC
LIMIT 500;4. Demographic Profile of Multi-Brand Intenders
Understand the demographic makeup of people considering multiple luxury brands. These cross-shoppers are often the most valuable audience for conquest campaigns.
SELECT
age_range,
gender,
income_range,
net_worth,
COUNT(*) AS intender_count,
ROUND(AVG(signal_strength), 3) AS avg_signal_strength,
ROUND(AVG(sustained_intent_days), 1) AS avg_sustained_days,
ROUND(
SUM(CASE WHEN secondary_topic_count > 0 THEN 1 ELSE 0 END)
/ COUNT(*),
3
) AS pct_with_secondary_signals,
SUM(CASE WHEN homeowner = 'true' THEN 1 ELSE 0 END) AS homeowner_count
FROM
signal_products.luxury_automobile_audience
WHERE
intent_date = (
SELECT MAX(intent_date)
FROM signal_products.luxury_automobile_audience
)
AND brand_interest_daily = 'Multi-Brand'
AND age_range IS NOT NULL
AND gender IS NOT NULL
GROUP BY
age_range,
gender,
income_range,
net_worth
HAVING
intender_count >= 10
ORDER BY
intender_count DESC;5. Daily Audience Size and Quality Trends
Track how the audience evolves day over day. Useful for monitoring data freshness and understanding seasonal or campaign-driven shifts in luxury auto interest.
SELECT
intent_date,
COUNT(DISTINCT hem) AS unique_intenders,
ROUND(AVG(signal_strength), 4) AS avg_signal_strength,
ROUND(AVG(sustained_intent_days), 2) AS avg_sustained_days,
SUM(CASE WHEN brand_interest_daily = 'Multi-Brand' THEN 1 ELSE 0 END) AS multi_brand_count,
ROUND(
SUM(CASE WHEN brand_interest_daily = 'Multi-Brand' THEN 1 ELSE 0 END)
/ COUNT(*),
3
) AS multi_brand_pct,
SUM(CASE WHEN secondary_topic_count > 0 THEN 1 ELSE 0 END) AS with_secondary_signals,
SUM(CASE WHEN sustained_intent_days >= 3 THEN 1 ELSE 0 END) AS sustained_3_plus_days
FROM
signal_products.luxury_automobile_audience
GROUP BY
intent_date
ORDER BY
intent_date;6. ZIP-Level Targeting for Dealership Campaigns
Aggregate intent signals at the ZIP code level to identify hotspot areas for local dealer campaigns, direct mail, or geo-targeted digital ads.
SELECT
state,
city,
zip,
COUNT(DISTINCT hem) AS unique_intenders,
ROUND(AVG(signal_strength), 3) AS avg_signal_strength,
SUM(CASE WHEN sustained_intent_days >= 3 THEN 1 ELSE 0 END) AS sustained_intenders,
SUM(CASE WHEN brand_interest_daily = 'Multi-Brand' THEN 1 ELSE 0 END) AS multi_brand_intenders,
SUM(CASE WHEN homeowner = 'true' THEN 1 ELSE 0 END) AS homeowners,
SUM(tesla_intent::INT) AS tesla,
SUM(bmw_intent::INT) AS bmw,
SUM(mercedes_benz_intent::INT) AS mercedes_benz
FROM
signal_products.luxury_automobile_audience
WHERE
intent_date = (
SELECT MAX(intent_date)
FROM signal_products.luxury_automobile_audience
)
AND zip IS NOT NULL
GROUP BY
state,
city,
zip
HAVING
unique_intenders >= 10
ORDER BY
unique_intenders DESC
LIMIT 200;7. Mobile Ad Targeting - Extract MAIDS for Programmatic Activation
Flatten the MAIDS array for high-intent individuals. Ready for ingestion into DSPs or mobile ad platforms.
SELECT
a.HEM,
m.VALUE::STRING AS maid,
a.SIGNAL_STRENGTH,
a.BRAND_INTEREST_DAILY,
a.STATE,
a.AGE_RANGE,
a.INCOME_RANGE
FROM SIGNAL_PRODUCTS.LUXURY_AUTOMOBILE_AUDIENCE a,
LATERAL FLATTEN(INPUT => a.MAIDS) m
WHERE a.INTENT_DATE = (SELECT MAX(INTENT_DATE) FROM SIGNAL_PRODUCTS.LUXURY_AUTOMOBILE_AUDIENCE)
AND a.SIGNAL_STRENGTH >= 0.6
AND a.MAIDS IS NOT NULL
ORDER BY a.SIGNAL_STRENGTH DESC
LIMIT 5000;8. Conquest Targeting - Brand-Specific Audience Extraction
Build an audience of people interested in a competitor brand (e.g. BMW) who also show interest in your brand (e.g. Mercedes-Benz). Ideal for conquest campaigns aimed at cross-shoppers.
SELECT
HEM,
MAIDS,
SIGNAL_STRENGTH,
SUSTAINED_INTENT_DAYS,
STATE,
CITY,
ZIP,
AGE_RANGE,
INCOME_RANGE,
NET_WORTH,
HOMEOWNER
FROM SIGNAL_PRODUCTS.LUXURY_AUTOMOBILE_AUDIENCE
WHERE INTENT_DATE = (SELECT MAX(INTENT_DATE) FROM SIGNAL_PRODUCTS.LUXURY_AUTOMOBILE_AUDIENCE)
AND BMW_INTENT = TRUE
AND MERCEDES_BENZ_INTENT = TRUE
AND SIGNAL_STRENGTH >= 0.6
ORDER BY SIGNAL_STRENGTH DESC;9. High-Net-Worth Ultra-Luxury Segment
Isolate affluent homeowners showing interest in ultra-luxury marques (Rolls-Royce, Bentley, Ferrari, Lamborghini, Bugatti, McLaren, Aston Martin, Maserati). Ideal for luxury lifestyle partnerships and high-value dealer events.
SELECT
HEM,
MAIDS,
SIGNAL_STRENGTH,
SUSTAINED_INTENT_DAYS,
STATE,
CITY,
AGE_RANGE,
INCOME_RANGE,
NET_WORTH,
ROLLS_ROYCE_INTENT::INT
+ BENTLEY_INTENT::INT
+ FERRARI_INTENT::INT
+ LAMBORGHINI_INTENT::INT
+ BUGATTI_INTENT::INT
+ MCLAREN_INTENT::INT
+ ASTON_MARTIN_INTENT::INT
+ MASERATI_INTENT::INT AS ultra_luxury_brand_count
FROM SIGNAL_PRODUCTS.LUXURY_AUTOMOBILE_AUDIENCE
WHERE INTENT_DATE = (SELECT MAX(INTENT_DATE) FROM SIGNAL_PRODUCTS.LUXURY_AUTOMOBILE_AUDIENCE)
AND (ROLLS_ROYCE_INTENT = TRUE
OR BENTLEY_INTENT = TRUE
OR FERRARI_INTENT = TRUE
OR LAMBORGHINI_INTENT = TRUE
OR BUGATTI_INTENT = TRUE
OR MCLAREN_INTENT = TRUE
OR ASTON_MARTIN_INTENT = TRUE
OR MASERATI_INTENT = TRUE)
AND HOMEOWNER = 'true'
AND NET_WORTH IN ('$500,000 to $749,999', '$750,000 to $999,999', 'More than $1,000,000')
ORDER BY ultra_luxury_brand_count DESC, SIGNAL_STRENGTH DESC
LIMIT 1000;10. Brand Affinity Heatmap: Which Brands Are Cross-Shopped Together?
Counts how often each pair of brands appears on the same individual. Reveals natural competitive clusters and cross-shopping patterns.
WITH brand_flags AS (
SELECT *
FROM SIGNAL_PRODUCTS.LUXURY_AUTOMOBILE_AUDIENCE
WHERE INTENT_DATE = (SELECT MAX(INTENT_DATE) FROM SIGNAL_PRODUCTS.LUXURY_AUTOMOBILE_AUDIENCE)
AND BRAND_INTEREST_DAILY = 'Multi-Brand'
)
SELECT 'Mercedes-Benz & BMW' AS brand_pair, SUM(CASE WHEN MERCEDES_BENZ_INTENT = TRUE AND BMW_INTENT = TRUE THEN 1 ELSE 0 END) AS overlap FROM brand_flags
UNION ALL SELECT 'Mercedes-Benz & Tesla', SUM(CASE WHEN MERCEDES_BENZ_INTENT = TRUE AND TESLA_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'BMW & Tesla', SUM(CASE WHEN BMW_INTENT = TRUE AND TESLA_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'BMW & Audi', SUM(CASE WHEN BMW_INTENT = TRUE AND AUDI_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'Mercedes-Benz & Audi', SUM(CASE WHEN MERCEDES_BENZ_INTENT = TRUE AND AUDI_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'Porsche & Ferrari', SUM(CASE WHEN PORSCHE_INTENT = TRUE AND FERRARI_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'Tesla & Lincoln', SUM(CASE WHEN TESLA_INTENT = TRUE AND LINCOLN_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'Land Rover & Volvo', SUM(CASE WHEN LAND_ROVER_INTENT = TRUE AND VOLVO_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'Lexus & Cadillac', SUM(CASE WHEN LEXUS_INTENT = TRUE AND CADILLAC_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'Ferrari & Lamborghini', SUM(CASE WHEN FERRARI_INTENT = TRUE AND LAMBORGHINI_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'Rolls-Royce & Bentley', SUM(CASE WHEN ROLLS_ROYCE_INTENT = TRUE AND BENTLEY_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
UNION ALL SELECT 'Aston Martin & McLaren', SUM(CASE WHEN ASTON_MARTIN_INTENT = TRUE AND MCLAREN_INTENT = TRUE THEN 1 ELSE 0 END) FROM brand_flags
ORDER BY overlap DESC;Data Compliance and Privacy
- All consumer data sourced in compliance with applicable U.S. privacy regulations including CCPA/CPRA
- Default share delivers SHA-256 HEM only. No plaintext PII is exposed
- Opt-out suppression applied at source. Do-not-sell records are excluded prior to delivery
- Intended for marketing activation only; prohibited uses include credit, employment, insurance, and housing eligibility decisions
- All buyers must accept Snowflake Marketplace Provider and Consumer Policies upon access request
Provider Information
| Field | Details |
|---|---|
| Provider | Blackpearl Group |
| Listing Type | Data Share |
| Data Residency | United States |
| Update Frequency | Daily |
| Support Contact | [email protected] |
Available Snowflake Regions
| Cloud Provider | Region |
|---|---|
| AWS | Canada (Central) |
| US East (N. Virginia) | |
| US East (Ohio) | |
| US West (Oregon) | |
| Azure | Canada Central (Toronto) |
| Central US (Iowa) | |
| East US (Virginia) | |
| East US 2 (Virginia) | |
| Mexico Central | |
| South Central US (Texas) | |
| West US 2 (Washington) | |
| GCP | US Central 1 (Iowa) |
| US East 4 (N. Virginia) |
Updated about 1 hour ago