US Off-Price Luxury Fashion Audience — 12.6M Records
₽76,640.50 ₽383,202.50Price range: ₽76,640.50 through ₽383,202.50
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Large-scale US consumer audience aligned with off-price luxury fashion retail. 12.6M deduplicated records with email, name, ZIP, and gender. Sold as-is for research and modeling use.
Description
Published February, 2026
Product Slices & Pricing Tiers
| Tier Name | Coverage | Description | Price |
|---|---|---|---|
| Metro Core Markets Pack | ~3.5M records | Consumers located in top US fashion retail metros (Los Angeles, New York City, San Francisco Bay Area, Chicago, Miami, Houston). Strong female skew, dense ZIP coverage, ideal for urban market analysis and retail modeling. | $1,500 |
| California Fashion Audience | ~1.4M records | California-based off-price luxury fashion shoppers concentrated in LA, SF Bay Area, Silicon Valley, San Diego, and surrounding suburbs. High relevance for West Coast retail, ecommerce, and brand expansion research. | $1,200 |
| East Coast Fashion Corridor | ~2.2M records | Consumers across New York, New Jersey, Massachusetts, Pennsylvania, DC metro, and Florida. Suitable for regional demand analysis, audience overlap studies, and retail footprint planning. | $1,500 |
| Female-Only Fashion Audience | ~10.9M records | Female-identifying consumers nationwide with strong alignment to off-price and flash-sale fashion retail. Optimized for gender-focused modeling, research, and audience development. | $2,000 |
| Top 20 ZIP Prefixes Pack | ~6.1M records | High-density ZIP-prefix clusters representing the strongest retail demand zones nationwide. Useful for geographic weighting, DMA modeling, and urban/suburban comparison studies. | $2,500 |
| Email-Domain Segments (Gmail / Yahoo) | ~10.8M records | Split audience files by dominant email domains (Gmail-only or Yahoo-only). Commonly used for deliverability testing, cohort comparison, and legacy vs modern user modeling. | $1,000 |
| Full US Audience (Complete Dataset) | 12,633,040 records | Entire deduplicated US fashion-aligned audience with email, name, ZIP, gender, and limited DOB. Best suited for comprehensive research, modeling, and audience intelligence use. | $5,000 |
This dataset contains 12,633,040 deduplicated US consumer records representing a large, geographically coherent audience strongly aligned with off-price and flash-sale luxury fashion retail behavior during the 2020–2022 period.
The audience profile closely matches the known characteristics of value-conscious, fashion-engaged US shoppers, with particularly strong coverage across California, New York, Texas, Florida, Illinois, and other major metropolitan markets.
The dataset is optimized for market research, audience analysis, geographic modeling, and lookalike development, rather than individual-level targeting.
Data Fields
Each record may include the following fields:
Email address
First name
Last name
ZIP code
Gender
Date of birth (limited availability)
Coverage highlights:
100% email presence
100% ZIP code coverage
~88% female audience
Nationwide US distribution with dense urban clustering
| Field | Description | Filled | Empty/Invalid | Fill Rate |
|---|---|---|---|---|
| Email address | 12,633,040 | 0 | 100.0% | |
| first_name | First name | 12,633,037 | 3 | 100.0% |
| last_name | Last name | 12,632,884 | 156 | 100.0% |
| zip | Zip code | 12,633,040 | 0 | 100.0% |
| country | Country (US) | 12,633,040 | 0 | 100.0% |
| gender | Gender (F/M) | 12,452,093 | 180,947 | 98.6% |
| dob | Date of birth | 382,468 | 12,250,572 | 3.0% |
Audience Characteristics
Predominantly female fashion shoppers
Strong representation in urban and suburban retail markets
Concentration in high-value retail regions (CA, NY, TX, FL, IL)
Email domain distribution consistent with established consumer cohorts
Behaviorally aligned with off-price, outlet, and flash-sale fashion retail
Age data is sparsely available and should be treated as non-representative; the dataset is best used without strict age segmentation.
GENDER DISTRIBUTION
| Gender | Count | Percentage |
|---|---|---|
| Female | 10,912,204 | 87.6% |
| Male | 1,539,889 | 12.4% |
| Total | 12,452,093 | 100% |
DATE OF BIRTH BREAKDOWN
| Status | Count | Percentage |
|---|---|---|
| Empty/Not provided | 12,250,572 | 96.97% |
| Valid with age | 381,993 | 3.02% |
| Invalid format | 475 | 0.00% |
| Total | 12,633,040 | 100% |
AGE DISTRIBUTION (Valid DOBs Only)
| Age Category | Count | Percentage |
|---|---|---|
| Under 18 | 34,057 | 8.9% |
| 18-24 | 1,210 | 0.3% |
| 25-34 | 13,562 | 3.6% |
| 35-44 | 139,157 | 36.4% |
| 45-54 | 93,270 | 24.4% |
| 55-64 | 57,423 | 15.0% |
| 65+ | 43,314 | 11.3% |
| Total (Valid DOBs) | 381,993 | 100% |
ZIP CODE PREFIXES BY REGION
| Region | States | Zip Prefixes | Total Records |
|---|---|---|---|
| West (CA dominant) | CA | 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 | 2,886,492 |
| Southeast (FL/GA) | FL, GA, SC, NC, AL | 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 | 1,718,787 |
| Midwest (IL/Great Lakes) | IL, IN, OH, MI, WI | 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 | 1,183,292 |
| Northeast (NY/NJ) | NY, NJ, CT, MA, PA | 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 | 1,275,422 |
| South Central (TX) | TX, OK, AR, LA | 70, 71, 72, 73, 74, 75, 76, 77, 78, 79 | 1,654,459 |
| Mid-Atlantic | VA, WV, MD, DC, DE | 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 | 1,545,579 |
| New England | MA, CT, RI, VT, NH, ME | 00, 01, 02, 03, 04, 05, 06, 07, 08, 09 | 802,571 |
| Mountain/Southwest | AZ, NV, UT, CO, NM | 80, 81, 82, 83, 84, 85, 86, 87, 88, 89 | 693,930 |
| Pacific Northwest | WA, OR, ID, AK | 98, 99, 97 | 337,709 |
Top 10 Individual Zip Prefixes
| Rank | Prefix | Region | Records | Major Cities |
|---|---|---|---|---|
| 1 | 92xxxx | CA (Los Angeles) | 446,945 | Los Angeles, Long Beach |
| 2 | 60xxxx | IL (Chicago) | 430,755 | Chicago, Aurora, Naperville |
| 3 | 33xxxx | FL (Miami) | 392,904 | Miami, Fort Lauderdale |
| 4 | 11xxxx | NY (NYC) | 391,066 | New York City, Brooklyn |
| 5 | 90xxxx | CA (LA West) | 367,599 | West LA, Beverly Hills |
| 6 | 10xxxx | NY (NYC) | 364,291 | Manhattan, Bronx |
| 7 | 30xxxx | GA (Atlanta) | 341,328 | Atlanta, Marietta |
| 8 | 94xxxx | CA (SF Bay) | 328,757 | San Francisco, Oakland |
| 9 | 77xxxx | TX (Houston) | 323,637 | Houston, Sugar Land |
| 10 | 95xxxx | CA (Silicon Valley) | 299,979 | San Jose, Santa Clara |
Summary Statistics
| Metric | Value |
|---|---|
| Total US Records | 12,633,040 |
| Female Dominance | 87.6% |
| Complete Email Coverage | 100% |
| Complete Zip Coverage | 100% |
| DOB Availability | 3.02% |
| Primary Age Group (of valid DOBs) | 35-44 (36.4%) |
| Top Region | California (22.9%) |
| Top Metro | Los Angeles (6.4%) |
Email domain distribution
| Domain | Count | Domain | Count |
|---|---|---|---|
| @gmail.com | 5,745,732 | @neo.rr.com | 2,672 |
| @yahoo.com | 5,127,223 | @san.rr.com | 2,627 |
| @icloud.com | 55,321 | @ca.rr.com | 2,601 |
| @charter.net | 51,530 | @att.com | 2,589 |
| @outlook.com | 34,068 | @yaoo.com | 2,542 |
| @nordstrom.com | 4,809 | @knology.net | 2,491 |
| @comcast.com | 4,462 | @email.arizona.edu | 2,472 |
| @umich.edu | 3,958 | @udel.edu | 2,437 |
| @msu.edu | 3,852 | @usc.edu | 2,404 |
| @kent.edu | 3,620 | @temple.edu | 2,403 |
| @indiana.edu | 3,464 | @uga.edu | 2,401 |
| @umn.edu | 3,450 | @ufl.edu | 2,390 |
| @crimson.ua.edu | 3,348 | @yahoo.cn | 2,356 |
| @vt.edu | 3,270 | @auburn.edu | 2,314 |
| @cs.com | 3,183 | @wisc.edu | 2,289 |
| @osu.edu | 3,103 | @cornell.edu | 2,285 |
| @columbus.rr.com | 3,100 | @yhoo.com | 2,279 |
| @satx.rr.com | 3,080 | @hotmai.com | 2,216 |
| @asu.edu | 2,994 | @socal.rr.com | 2,203 |
| @nyu.edu | 2,887 | @yahoo.com.mx | 2,169 |
| @yahoo.co | 2,876 | @colorado.edu | 2,133 |
| @psu.edu | 2,861 | @mail.usf.edu | 2,132 |
| @wildblue.net | 2,856 | @ec.rr.com | 2,130 |
| @mailinator.com | 2,774 | @yhaoo.com | 2,103 |
| @virginia.edu | 2,763 | @live.cn | 2,065 |
| @myway.com | 2,726 | @google.com | 2,064 |
Domain Distribution Summary
| Category | Count | Percentage |
|---|---|---|
| Gmail + Yahoo | 10,872,955 | 86.1% |
| Apple (iCloud) | 55,321 | 0.4% |
| Microsoft (Outlook) | 34,068 | 0.3% |
| ISPs (RR, Charter, etc.) | ~250,000 | 2.0% |
| Educational (.edu) | ~75,000 | 0.6% |
| Typos (yaoo, yhoo, etc.) | ~15,000 | 0.1% |
| Other | ~1.4M | 11.5% |
Intended Use Cases
This dataset is suitable for:
Market and audience research
Geographic and demographic analysis
Retail expansion and site selection studies
Audience overlap and suppression modeling
Lookalike and seed audience development
Historical consumer behavior analysis
Important Limitations & Disclosure
This dataset is sold strictly as-is
Data was compiled via multi-source matching and aggregation and manually cleaned for basic validity
No claim of first-party origin, direct retailer sourcing, or customer relationship is made
No guarantees are provided regarding deliverability, accuracy, or recency
Date of birth data is largely incomplete and should not be relied upon for age targeting
Buyer is solely responsible for legal, regulatory, and compliance obligations related to any use of the data
This product is intended for research, modeling, and analytical use. It is not marketed as a compliance-ready marketing list.
Sample data:
email first_name last_name zip gender dob [email protected] Brett Markinson 90046 M [email protected] Victoria Tatar 90015 F [email protected] LISA BERNSTEIN 90265 F [email protected] Taka Stephens 91505 F [email protected] Zulema Tapia 90034 F [email protected] Tracy Ma'ae 90808 F [email protected] GLADYS FLORES 92677 F 1980-10-23 00:00:00 [email protected] Ashley Walters 27405 F [email protected] Kelly =) 90650 F [email protected] dina braun 91302 F [email protected] Andria Venturina 90036 F [email protected] Michalle Thompson 92708 F [email protected] Staci Buckley 90405 F 1966-08-25 00:00:00 [email protected] Robbyn Coffey 90303 F [email protected] catherine thompson 90265 F [email protected] Alison Volk 90266 F [email protected] Danielle Vandermade 91355 F 1987-03-25 00:00:00 [email protected] Annie Barrueta 91203 F [email protected] lily huante 90505 F [email protected] Jennifer Weigel 93041 F 1961-05-30 00:00:00 [email protected] Barbara Johnson 92325 F [email protected] Vera Cuno 92683 F [email protected] Claudine Guerrero 90005 F [email protected] casasha bergman 90802 F [email protected] J. J. CHENG 90029 F [email protected] Patrice Richardson 90631 F [email protected] Julie Lee 91755 F [email protected] Jazmyne Bacani 90720 F 1990-10-11 00:00:00 [email protected] Heidi Vandermade 90048 F 2007-06-04 00:00:00
Additional information
| Product tier (see table below) | Metro Core Markets Pack, California Fashion Audience, East Coast Fashion Corridor, Female-Only Fashion Audience, Top 20 ZIP Prefixes Pack, Email-Domain Segments (Gmail / Yahoo), Full US Audience (Complete Dataset) |
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