Common First Names by Companies

How to use?

  1. The list of first names is compiled by analyzing more than 50 million social networking profiles.
  2. To use this service, please FIRST click on one of the four buttons below; NEXT, select one company from the menu, and after you click 'submit', you will see the results.
  3. Statistics are reported in % form. For example, 1.43483 means 1.43483%.
  4. Rank is calculated within each company. For example, the most common female name may have a rank = 5, which means it is the 5th common name in that company.
  5. "Gender" column reports how many % of people who use that first name is female.
  6. "Proportion" column reports "the number of profiles with that first name" out of total number of profile in that company.
  7. Due to the nature of some social networking sites, this list has more male name counts.
  8. We only display companies with more than 20000 profiles analyzed and first names appear at least 25 profiles.
  9. Unisex names are defined as first names that are within 20%-80% range (i.e., less than 80% from one gender).

Results are sorted by p-ratio, which means how many times more likely to happen than an average case. For example, Satya has a p-ratio=4.335, implying Satya (in the USA) is 4.335 times more likely to work at IBM than an average person.

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List of First Names at IBM based on 324 Thousand profiles

  Rank     First Name       P-Ratio Proportion(%) Gender(%) Caucasian(%)   African(%)   Asian(%)   Hispanic(%)
732 Satya 4.34 0.01 71.91 24.16 7.50 66.82 2.42
537 Naveen 3.84 0.01 21.92 15.60 1.38 79.95 2.19
348 Kiran 3.02 0.02 46.64 28.52 11.75 56.73 1.13
551 Ming 2.78 0.01 31.06 3.83 0.38 92.91 1.02
794 Raja 2.58 0.01 31.76 26.72 4.77 67.77 3.07
729 Chen 2.47 0.01 26.67 7.48 0.52 92.40 0.68
381 Wei 2.33 0.02 22.10 5.34 2.19 90.01 0.89
602 Yi 2.25 0.01 41.60 5.21 0.79 91.18 1.15
480 Krishna 1.97 0.02 49.97 15.24 2.43 80.26 1.27
832 Yu 1.90 0.01 50.71 1.98 0.42 94.95 0.99
538 Li 1.78 0.01 69.92 6.50 2.78 88.40 1.08
449 Michel 1.66 0.02 25.28 83.89 13.16 0.76 2.15
731 Ying 1.56 0.01 65.00 2.48 0.30 94.86 0.62
793 Jin 1.43 0.01 26.87 8.06 2.98 87.17 0.72
609 Sunny 1.13 0.01 72.14 12.81 5.18 76.16 1.65
750 Lin 1.09 0.01 53.72 13.33 1.81 83.79 0.72
332 Gerry 0.84 0.02 31.70 84.80 6.51 6.34 3.16
103 Lee 0.72 0.08 21.27 80.21 14.03 3.43 2.65
730 Blair 0.70 0.01 46.75 84.98 9.28 3.45 1.63
344 Ali 0.59 0.02 29.23 40.63 12.89 40.38 2.65
470 Rene 0.55 0.02 30.93 44.22 6.98 5.34 42.30
243 Kris 0.54 0.03 49.35 78.10 8.74 7.73 5.25
300 Kerry 0.50 0.03 49.51 79.04 11.80 3.65 5.50
665 Noel 0.49 0.01 23.32 74.53 13.39 3.88 11.63
666 Willie 0.48 0.01 24.61 64.11 31.87 1.59 5.02
113 Pat 0.48 0.07 60.02 82.41 11.12 2.91 3.45
183 Jan 0.48 0.05 71.61 83.95 11.39 0.58 3.94
162 Lynn 0.40 0.05 77.67 80.56 11.99 5.58 2.59
472 Jody 0.36 0.02 64.18 84.57 9.97 2.34 2.86
471 Jaime 0.35 0.02 42.96 43.71 3.74 3.69 50.21
331 Jordan 0.32 0.02 26.47 77.93 12.64 6.86 2.94
177 Jamie 0.31 0.05 75.90 77.81 14.89 5.18 2.23
260 Dana 0.30 0.03 78.25 81.78 13.72 2.01 1.71
274 Jackie 0.27 0.03 53.62 66.03 15.62 9.86 9.25
248 Leslie 0.27 0.03 70.12 81.22 14.58 1.01 3.37
552 Casey 0.23 0.01 40.87 69.45 17.01 15.96 1.64
629 Taylor 0.20 0.01 74.05 81.55 13.90 0.56 1.59
All Female Male Unisex Back to Start Page

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