A data profile of the people who buy, follow and care about Michelle Yeoh in United States — modelled from more than twelve digital signal sources. Michelle Yeoh has an estimated audience of 2,803,517 people in United States.
The average Michelle Yeoh fan in United States is 43.0 years old, more female, and lives primarily in California.
The audience is concentrated in California, Texas, New York.
Top brand affinities include Taobao, Mitú, Baidu, with strongest over-indexing on Taobao (25.24× the country average).
Demographically, the Michelle Yeoh audience skews more female with an average age of 43.0, and over-indexes on personality traits such as Creativity, Quality Awareness.
Compared to the country baseline, this audience shows distinctive patterns across 20 brand affinities and 50 regions tracked by Rascasse.
The typical Michelle Yeoh fan in United States is more female, around 43.0 years old, with strong Creativity tendencies and a notable affinity for Taobao.
The key figures that characterise the Michelle Yeoh profile in United States.
77.8% are female, 22.2% are male, average age 43.0.
| Metric | Value |
|---|---|
| Female | 77.8% |
| Male | 22.2% |
| Average age | 43.0 |
| Estimated audience size | 2,803,517 |
| Age bracket | Share | % |
|---|---|---|
| 16-19 | 13% | |
| 20-29 | 16% | |
| 30-39 | 19% | |
| 40-49 | 22% | |
| 50+ | 30% |
of the worldwide Michelle Yeoh audience comes from United States.
| Country | Share |
|---|---|
| United States | 35.5% |
| United Kingdom | 6.4% |
| Germany | 5.3% |
Where the Michelle Yeoh audience in United States is strongest.
| # | Region | Reach | Affinity | × |
|---|---|---|---|---|
| 01 | Hawaii | ~20K | 1.75× | |
| 02 | Washington | ~80K | 1.33× | |
| 03 | Utah | ~30K | 1.33× | |
| 04 | California | ~400K | 1.32× | |
| 05 | Oregon | ~40K | 1.22× | |
| 06 | New York | ~200K | 1.15× | |
| 07 | Massachusetts | ~60K | 1.14× | |
| 08 | Nevada | ~30K | 1.11× | |
| 09 | Washington, District of Columbia | ~10K | 1.11× | |
| 10 | Arizona | ~60K | 1.10× | |
| 11 | Maryland | ~50K | 1.10× | |
| 12 | Alaska | ~7K | 1.09× | |
| 13 | Virginia | ~70K | 1.08× | |
| 14 | Connecticut | ~30K | 1.08× | |
| 15 | Colorado | ~50K | 1.07× | |
| 16 | Illinois | ~100K | 1.05× | |
| 17 | New Jersey | ~80K | 1.05× | |
| 18 | Minnesota | ~40K | 1.05× | |
| 19 | Idaho | ~10K | 1.04× | |
| 20 | New Mexico | ~10K | 1.03× | |
| 21 | New Hampshire | ~10K | 1.02× | |
| 22 | Vermont | ~5K | 1.02× | |
| 23 | Texas | ~200K | 1.00× | |
| 24 | Pennsylvania | ~100K | 0.99× | |
| 25 | Missouri | ~40K | 0.98× | |
| 26 | Wisconsin | ~40K | 0.98× | |
| 27 | Rhode Island | ~9K | 0.98× | |
| 28 | Oklahoma | ~30K | 0.96× | |
| 29 | Maine | ~10K | 0.96× | |
| 30 | Georgia | ~80K | 0.95× | |
| 31 | Michigan | ~70K | 0.95× | |
| 32 | Kansas | ~20K | 0.95× | |
| 33 | North Carolina | ~80K | 0.94× | |
| 34 | Delaware | ~7K | 0.94× | |
| 35 | Florida | ~200K | 0.93× | |
| 36 | Ohio | ~80K | 0.93× | |
| 37 | Indiana | ~50K | 0.93× | |
| 38 | Iowa | ~20K | 0.90× | |
| 39 | Montana | ~7K | 0.90× | |
| 40 | Nebraska | ~10K | 0.89× | |
| 41 | Tennessee | ~50K | 0.87× | |
| 42 | South Carolina | ~40K | 0.85× | |
| 43 | Kentucky | ~30K | 0.85× | |
| 44 | Arkansas | ~20K | 0.85× | |
| 45 | Louisiana | ~30K | 0.80× | |
| 46 | North Dakota | ~5K | 0.79× | |
| 47 | South Dakota | ~5K | 0.78× | |
| 48 | Alabama | ~30K | 0.76× | |
| 49 | Mississippi | ~20K | 0.75× | |
| 50 | West Virginia | ~10K | 0.73× |
The strongest cross-interests of the Michelle Yeoh audience — brands, topics and people combined.
| # | · | Interest | Category | Affinity | × |
|---|---|---|---|---|---|
| 01 | Zhang Ziyi | Movies & TV | 33.33× | ||
| 02 | Now TV | Movies & TV | 33.24× | ||
| 03 | Chinese astrology | Politics & Society | 32.20× | ||
| 04 | Baidu | Internet & Social Media | 30.29× | ||
| 05 | Jet Li | Movies & TV | 26.98× | ||
| 06 | Taobao | Shopping | 25.24× | ||
| 07 | Music of India | Music & Radio | 24.34× | ||
| 08 | Fresh Off the Boat | Movies & TV | 23.92× | ||
| 09 | Chinese television drama | Movies & TV | 23.87× | ||
| 10 | Mooncake | Food & Beverages | 23.13× | ||
| 11 | Padma Lakshmi | Fashion & Accessoires | 22.22× | ||
| 12 | Taiwanese drama | Movies & TV | 21.43× | ||
| 13 | IU (singer) | Music & Radio | 20.39× | ||
| 14 | Daniel Dae Kim | Movies & TV | 19.55× | ||
| 15 | Priyanka Chopra | Movies & TV | 19.14× | ||
| 16 | Jackie Chan | Movies & TV | 15.39× | ||
| 17 | Mitú | Travel & Leisure | 14.69× | ||
| 18 | Bruce Lee | Movies & TV | 14.30× | ||
| 19 | Nate Bargatze | Music & Radio | 10.72× | ||
| 20 | Korean drama | Movies & TV | 10.03× |
Values above 1.00× are above the country average, values below 1.00× below it.
| Trait | Cluster | Deviation | Score |
|---|---|---|---|
| Creativity | OPEN | 1.44× | |
| Quality Awareness | PREMIUM | 1.40× | |
| Extroversion | THRILL | 1.35× |
| Trait | Cluster | Deviation | Score |
|---|---|---|---|
| Patriotism | CONSERVATISM | 0.99× | |
| Career Orientation | POWER | 1.02× | |
| Risk Appetite | THRILL | 1.03× |
Michelle Yeoh has an estimated audience of 2,803,517 people in United States, concentrated in California and Texas.
77.8% of Michelle Yeoh fans are female, 22.2% are male, with an average age of 43.0 years.
Michelle Yeoh fans show strongest brand affinity for Taobao (25.24×), Mitú (14.69×), and Baidu (30.29×) over the country average.
Michelle Yeoh fans in United States are most concentrated in California (reach ~400K), Texas (reach ~200K), and New York (reach ~200K). These three regions account for the largest share of the active audience.
Beyond Michelle Yeoh itself, the audience over-indexes on Mitú (14.69×), Baidu (30.29×), Priyanka Chopra (19.14×), and Nate Bargatze (10.72×) compared to the United States average.
Related profiles, rankings and the same audience in other markets.
Audience size is the estimated number of people in United States who actively search for Michelle Yeoh. Affinity is an over-index ratio: 2.0× means the audience is twice as likely to engage with that brand or trait as the country average. Reach is the estimated number of audience members in a region. Regional and brand-affinity tables are sorted from strongest signal to weakest.
This audience profile is generated by Rascasse from anonymized search-behavior signals across United States. For methodology see methodology. Affinity values are over-index ratios vs. the country average (1.0 = baseline). Audience sizes are estimated, not measured.
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