Page 276 - The Digital Financial Services (DFS) Ecosystem

ITU-T Focus Group Digital Financial Services
Ecosystem

Appendix I

I.1 Social network data collection

Social networks have much more knowledge about consumers than traditional marketplaces and even
merchants. This deep knowledge enables robust targeting for new customer acquisition. For example, an
advertiser can target "men, ages 18 – 24, currently traveling within 13 miles of Atlanta, who donate to animal
welfare charities, live in an apartment, like jazz music, cosmetics, tattoos, and are interested in buying an
economy car in the next 365 days", but only target them while they are "visiting Instagram via a WiFi-connected
Samsung Tablet 2 running the Android 4.0 operating system." This example might appear to be non-sensical,
but highlights the precision advertisers can employ. For example, Facebook advertisers can use these attributes:

Table 3 – Social network data collection

Demographics Interests Behaviour Placement
• Basics (age, gender, lan- • Business/industry (agricul- • Automotive (used motor- • Ad display location (mobile
guage, location) ture, banking, etc.) cycle owners, in market for news feed, Instagram, 3rd
• Education (level, school, • Entertainment (board new BMW, etc.) party sites, etc.)
fields of study, etc.) games, animated movies, • B2B (employer size, indus- • Mobile device type (iPad,
• Ethnic affinity group (Asian, etc.) try, etc.) Android smartphone, fea-
etc.) • Family and relationships • Charitable donations ture phone)
• Financial status (income (dating, fatherhood, etc.) (animal welfare, arts, etc.) • Mobile device model (Sam-
level, net worth) • Fitness and wellness (medi- • Digital activities (recent sung Galaxy 4, etc.)
• Residence (ownership, tation, dieting, etc.) gamer, event creator, pri- • OS version (4.4 KitKat, etc.)
type, household composi- • Food and drink (French mary browser type, etc.) • Connection method (WiFi,
tion, etc.) food, recipes, etc.) • Expats (Argentinians living any)
• Life events (marriage, • Hobbies and activities abroad, etc.)
engagement, birth, etc.) (pets, travel, etc.) • Financial (credit union
• Parent status (new parents, • Shopping and fashion (cos- members, real estate inves-
stay-at-home moms, etc.) metics, toys, etc.) tors, etc.)
• Political views (conserva- • Sports and outdoors • Job role (corporate execu-
tive, etc.) (camping, baseball, etc.) tive, farmer, etc.)
• Relationships (status, • Technology (servers, cam- • Media (TV reality show
sexual orientation) corders, etc.) watchers, etc.)
• Work (employer, industry, • Mobile device (Samsung
job title, etc.) Galaxy owners, 2G internet
connections, etc.)
• Purchase behaviour
(coupon users, beer buyers,
etc.)
• Residential profiles (new
homeowners, etc.)
• Seasonal and events
(Summer Olympics watch-
ers, etc.)
• Travel (business travellers,
cruise takers, etc.)

This data comes from several sources:

• User-provided: Users often provide demographic data such as birthdate, home address, school name,
and relationship status. Each action further enriches the user’s profile. For example, liking The House of
Nanking restaurant’s Facebook page may indicate an interest in "Chinese cuisine." Other data sources
include reading articles on certain topics, checking-in at merchants, commenting on or liking a friend’s
post, or joining a Facebook group.

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