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 LayerWhat You Get
IdentityHEM 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 ScoreSignals 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 FlagsPer-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 WealthNet worth and household income ranges at the record level, enabling direct premium-tier segmentation without modeled wealth proxies or geo-demographic look-ups.
DemographicsAge range, gender, and homeownership status on every record, enabling model-specific creative targeting and audience segmentation without a separate data purchase.

Data Dictionary

ColumnTypeDescription
INTENT_DATEDATEDate 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.
HEMVARCHARSHA-256 hashed email. The primary identifier for deterministic matching to DSPs, clean rooms, and CDPs. Trial accounts receive a partially masked value.
MAIDSARRAYMobile 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_STRENGTHFLOATIntent 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_DAYSINTEGERNumber 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_COUNTINTEGERCount 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_DAILYVARCHARClassifies 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_INTENTBOOLEANTRUE if the consumer is actively researching Mercedes-Benz.
BMW_INTENTBOOLEANTRUE if the consumer is actively researching BMW.
AUDI_INTENTBOOLEANTRUE if the consumer is actively researching Audi.
VOLVO_INTENTBOOLEANTRUE if the consumer is actively researching Volvo.
PORSCHE_INTENTBOOLEANTRUE if the consumer is actively researching Porsche.
LEXUS_INTENTBOOLEANTRUE if the consumer is actively researching Lexus.
LAMBORGHINI_INTENTBOOLEANTRUE if the consumer is actively researching Lamborghini.
FERRARI_INTENTBOOLEANTRUE if the consumer is actively researching Ferrari.
LAND_ROVER_INTENTBOOLEANTRUE if the consumer is actively researching Land Rover.
CADILLAC_INTENTBOOLEANTRUE if the consumer is actively researching Cadillac.
JAGUAR_INTENTBOOLEANTRUE if the consumer is actively researching Jaguar.
ROLLS_ROYCE_INTENTBOOLEANTRUE if the consumer is actively researching Rolls-Royce.
ASTON_MARTIN_INTENTBOOLEANTRUE if the consumer is actively researching Aston Martin.
BENTLEY_INTENTBOOLEANTRUE if the consumer is actively researching Bentley.
MASERATI_INTENTBOOLEANTRUE if the consumer is actively researching Maserati.
MCLAREN_INTENTBOOLEANTRUE if the consumer is actively researching McLaren.
LINCOLN_INTENTBOOLEANTRUE if the consumer is actively researching Lincoln.
TESLA_INTENTBOOLEANTRUE if the consumer is actively researching Tesla.
BUGATTI_INTENTBOOLEANTRUE if the consumer is actively researching Bugatti.
CITYVARCHARCity of residence. Use with STATE and ZIP for regional and dealer-level campaign targeting.
STATEVARCHARTwo-letter U.S. state code.
ZIPVARCHARFive-digit ZIP code. Use for geo-targeted campaigns and dealership trade area analysis.
ZIP4VARCHARZIP+4 code for higher-precision geographic targeting.
GENDERVARCHARGender: M (male), F (female), or U (unknown).
AGE_RANGEVARCHARHousehold age range (e.g., 25-34, 35-44, 45-54, 55-64, 65 and older).
INCOME_RANGEVARCHARHousehold income range (e.g., $75,000 to $99,999, $100,000 to $149,999, $250,000+).
NET_WORTHVARCHARHousehold net worth range (e.g., $750,000 to $999,999, More than $1,000,000).
HOMEOWNERVARCHARHomeownership 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

FieldDetails
ProviderBlackpearl Group
Listing TypeData Share
Data ResidencyUnited States
Update FrequencyDaily
Support Contact[email protected]

Available Snowflake Regions

Cloud ProviderRegion
AWSCanada (Central)
US East (N. Virginia)
US East (Ohio)
US West (Oregon)
AzureCanada Central (Toronto)
Central US (Iowa)
East US (Virginia)
East US 2 (Virginia)
Mexico Central
South Central US (Texas)
West US 2 (Washington)
GCPUS Central 1 (Iowa)
US East 4 (N. Virginia)