Presentation at the RapidMiner Wisdom Conference in New York 2016.

1. Elian CARSENAT, NamSor2016-01-28
1“Using Sociolinguistics to
Enhance Customer
Segmentation, Geomarketing
& Diversity Analytics”

2. Founder Bio
2
Elian CARSENAT, a computer scientist trained at ENSIIE/INRIA, started
his career at JP Morgan in Paris in 1997. He later worked as
consultant and managed business & IT projects in London, Paris,
Moscow and Shanghai.
In 2012, Elian created NamSor, a piece of sociolinguistics software to
mine the 'Big Data' and better understand international flows of
money, ideas and people. NamSor helps answer the perennial
question all countries ask about their diasporas – who are they,
where are they and what are they doing.
NamSor has been used to attract Foreign Direct Investments (FDI), to
build-up international collaboration within scientific communities, to
attract and facilitate Diaspora investment in Start-ups...
as well as other use cases.
http://fr.linkedin.com/in/eliancarsenat/en

3. NamSor sorts Names
3
 Names are meaningful : we use sociolinguistics to extract their
semantics and deliver actionable intelligence.
 Names reflect cultural Identity
 NamSor data mining software
recognizes the linguistic or cultural
origin of names in any alphabet /
language, with fine grain and high
accuracy.

4. 4
Gender Gap
in
Financing

5. 5
Gender Gap
in
Science

6. Diasporas in Science
(in collaboration with French INSERM)
6
Thomson Reuters WebOfScience (6 countries, 250k scientists, 50k papers)
“Analysts uncovered amazing patterns in the way scientists’ names correlate with whom they publish, and who
they cite in their papers - not just in case of a particular country, but globally. Tania Vichnevskaia of the French
National Institute for Health (INSERM) presented the paper ‘Applying onomastics to scientometrics‘ at IREG
International symposium 2015 organised by University of Maribor and Shanghai Jiao Tong University. The
paper was prepared jointly with NamSor, a private start-up company specialized in mapping international
Diasporas.”
Source: WoS; Data Mining: INSERM with NamSor

7. Scholar names in some Canadian Universities
Chinese, Indian, Iranian, Moroccan, Italian names
7
Canadian Science Policy Conference - CSPC2015

8. 8
 USE CASE – BOSTON CITY GEODEMOGRAPHICS

9. US Census vs NamSor geo-demographics
9
 In July 2015, the US Government announced new
rules that will require all cities and towns receiving
federal housing funds to assess patterns of
segregation.
 The NY Times has published interactive maps of
Boston geo-demographics, which we can compare
with the information inferred by NamSor

10. US Census Race Map of Boston
10
http://www.nytimes.com/interactive/2015/07/08/us/census-race-map.html

11. Using Voters List
 US Census:
1pixel = 40 inhabitants
 Voters List:
1 pixel = 1 voter
11
Source: Boston Voters List
Visualization : ESRI
Data Mining: NamSor+RapidMiner

12. Breaking down ‘White’ and ‘Asian’ into
Portuguese, Spanish, Italian, India, Pakistan, China, ...
12
Source: Boston Voters List
Visualization : ESRI
Data Mining: NamSor+RapidMiner

13. Who LIVES in New York ?
13

14. Who OWNS in Brooklyn, NY?
Inferring origin in NYC ACRIS (Real Estate OpenData)
14
> Brooklyn zip codes
>NamSororigins

15. Who OWNS in Brooklyn, NY?
Inferring origin in NYC ACRIS (Real Estate OpenData)
15
Interesting ‘Little’ spots
 ZIP 11209 : Irish
 ZIP 11219 : Jewish
 ZIP 11233 : African American
 ZIP 11228 : Italian
 ZIP 11208 : Hispanic
 ZIP 11214 : Chinese
 ZIP 11235 : Ukrainian/Russian
 ZIP 11416 : Indian
 ZIP 11222 : Polish

16. 16
 USE CASE – ELECTIONS

17. A Decision Tree from FLORIDA Voters List
(open data)
17
 //TODO : based on FLORIDA

18. Segmenting ‘Asian’ voters would improve the model
Using NamSor Origin to infer : Indian, Vietnamese, Korean, Chinese, ...
18
Tree
ethno = (Chin: DEM {DEM=3311, REP=2636, IDP=48, INT=199, LPF=9, GRE=5, CPF=2, REF=2, AIP=0, PSL=0}
ethno = (Indi: DEM {DEM=12509, REP=4565, IDP=95, INT=432, LPF=32, GRE=10, CPF=0, REF=1, AIP=3, PSL=1}
ethno = (Indo: DEM {DEM=984, REP=718, IDP=9, INT=43, LPF=4, GRE=1, CPF=1, REF=0, AIP=0, PSL=0}
ethno = (Japa: DEM {DEM=488, REP=403, IDP=9, INT=34, LPF=2, GRE=1, CPF=1, REF=0, AIP=0, PSL=0}
ethno = (Kore: REP {DEM=1148, REP=1174, IDP=11, INT=75, LPF=3, GRE=0, CPF=0, REF=0, AIP=0, PSL=0}
ethno = (Mong: DEM {DEM=24, REP=22, IDP=0, INT=0, LPF=0, GRE=1, CPF=0, REF=0, AIP=0, PSL=0}
ethno = (Paki: DEM {DEM=4411, REP=843, IDP=25, INT=110, LPF=9, GRE=6, CPF=0, REF=0, AIP=0, PSL=0}
ethno = (Viet: REP {DEM=3798, REP=5780, IDP=65, INT=272, LPF=10, GRE=5, CPF=3, REF=3, AIP=2, PSL=0}
Pakistanis, Vietnamese didn’t vote the same.

19. 19
 USE CASE – TRAVEL INTELLIGENCE

20. “Incredible India” – 1.2 BN People
Indian onomastics by State/Union Territory
20
Names in LATIN, BENGALI, DEVANAGARI, GUJARATI, GURMUKHI, KANNADA, MALAYALAM,
ORIYA, TAMIL, TELUGU, ARABIC

21. ASSAM: Karbi Anglong, within district
Inter-caste marriages ?
21
output Input Input
clusterId clusterParentId Firstname LastName parent is FirstParentLastParent
L25354:253L64958:2797 A¡à[¹ ¹}[ššã husband ¤àl¡ü[W¡³ [W¡}>à¹
L47490:1593L64958:2797 ¤àK[¹ [W¡}>๠father ¤àl¡ü[W¡³ [W¡}>à¹
L28582:1209L47490:1593 [³>à Òü}[t¡šã husband ¤àK[¹ [W¡}>à¹
L23643:669L35593:510 ™åKƒ}à [W¡}>๚ã father ¤ài¡[W¡³ [W¡}>à¹
L23643:669L35593:510 ³à>àÒü [W¡}>๚ã father ¤ài¡[W¡³ [W¡}>à¹
L47490:1593L35593:510 W¡àì=¢ [W¡}>๠father Wå¡ì¤ [W¡}>à¹
L23643:669L35593:510 A¡àì¹ t¡àì¹ïšã husband Wå¡ì¤ [W¡}>à¹
L35593:510L47490:1593 [ƒ[ºš [W¡}>๠father W¡àì¤ [W¡}>à¹
L23643:669L47490:1593 [¹>à [W¡}>๚ã father W¡àì¤ [W¡}>à¹
parent is husband
Count of serial Column Labels
Row Labels L47490:1593 L116370:3612 L54332:2031 L184096:2297 L35593:510 L168871:1819 L135664:4438 L51271:837
L23643:669 6931 84 5099 15 2069 28 791 1924
L151415:3559 18 212 11 6446 19 1217 55 6
L28582:1209 5132 68 3565 10 1494 17 592 1323
L116370:3612 66 10283 38 72 40 321 137 29
L9839:442 2491 60 1851 9 774 11 321 660
L168871:1819 7 263 6 361 8 2730 24 4
L23642:141 1198 8 822 2 375 4 156 332
L25354:253 1181 12 932 375 7 100 323
L135664:4438 20 154 5 22 19 44 2212 3
L87032:1210 11 315 13 51 14 141 37 9
L90333:3644 3 204 2 31 190 5
L184096:2297 13 1735 3 84 11 1
L87031:697 4 136 4 12 3 137 4 5
L14495:131 614 10 432 167 4 68 163
L63724:1422 17 83 10 34 34 28 96 6
L98994:891 31 161 46 21 19 59 21 5
ASSAM: Karbi Anlong district
names clustered L116370:3612
L23643:669
L151415:3559
L47490:1593
L28582:1209
L54332:2031
L184096:2297
L168871:1819
L9839:442
L135664:4438
L87032:1210
L90333:3644
L35593:510
L51271:837
L63724:1422
L154797:1168
L64959:1796
L23642:141
L87031:697
L6536:295
L98994:891
L25354:253
L64958:2797
L30570:2614
L90334:1189
L95839:287
L100510:366
L121390:783
Other
Source: Voters List; Data Mining: NamSor

22. Applications to an Airline’s customer intelligence
22
A global airline :
‘For 93% of our customers, when
NamSor recognizes an Indian
name, the client has travelled to
India in the past.’
Finer grain segmentation using
names brings insights about
diasporas travel pattern
visiting family and friends in
their home country, as well as
their specific needs.

23. Using NamSor API
23
(1) Get an API Key
(2) Get NamSor
RapidMiner Extension

24. Thank you!
Elian CARSENAT,
elian.carsenat@namsor.com
Phone : +33 6 52 77 99 07
http://www.namsor.com/
24
Juillet 2013, Ambassade de Lituanie à Paris