You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 retail_complains
【Schema】
# Table: reviews
[
(Date:DATE, Primary Key, Examples: [2013-02-04]),
(Stars:INTEGER, Examples: [5, 1, 2]),
(Reviews:TEXT),
(Product:TEXT, Examples: [Eagle National Mortgage]),
(district_id:INTEGER, Examples: [65, 66, 23])
]
# Table: callcenterlogs
[
(Date received:DATE, Examples: [2017-03-27]),
(Complaint ID:TEXT, Primary Key, Examples: [CR0000072, CR0000084, CR0000175]),
(rand client:TEXT, Examples: [C00004587, C00003328, C00001685]),
(phonefinal:TEXT, Examples: [977-806-9726, 322-598-7152, 508-311-5237]),
(vru+line:TEXT, Examples: [AA0103, AA0205, AA0110]),
(call_id:INTEGER, Examples: [34536, 34537, 34538]),
(priority:INTEGER, Examples: [0, 2, 1]),
(type:TEXT, Examples: [NW, PS, NE]),
(outcome:TEXT, Examples: [AGENT, HANG, PHANTOM]),
(server:TEXT, Examples: [MICHAL, TOVA, YIFAT]),
(ser_start:TEXT, Examples: [13:34:11, 10:58:22, 13:00:54]),
(ser_exit:TEXT, Examples: [13:40:23, 11:16:10, 13:13:31]),
(ser_time:TEXT, Examples: [00:06:12, 00:17:48, 00:12:37])
]
# Table: client
[
(client_id:TEXT, Primary Key, Examples: [C00000001, C00000002, C00000003]),
(sex:TEXT, Examples: [Female, Male]),
(day:INTEGER, Examples: [13, 4, 9]),
(month:INTEGER, Examples: [12, 2, 10]),
(year:INTEGER, Examples: [1990, 1965, 1960]),
(age:INTEGER, Examples: [29, 54, 59]),
(social:TEXT, Examples: [926-93-2157, 806-94-5725, 614-70-9100]),
(first:TEXT, Examples: [Emma, Noah, Olivia]),
(middle:TEXT, Examples: [Avaya, Everest, Brooklynne]),
(last:TEXT, Examples: [Smith, Thompson, Johnson]),
(phone:TEXT, Examples: [367-171-6840, 212-423-7734, 212-425-6932]),
(email:TEXT),
(address_1:TEXT, Examples: [387 Wellington Ave., 75 W. Berkshire St., 36 Second St.]),
(address_2:TEXT, Examples: [Unit 1, Unit 6, Unit 80]),
(city:TEXT, Examples: [Albuquerque, New York City, Indianapolis]),
(state:TEXT, Examples: [NM, NY, IN]),
(zipcode:INTEGER, Examples: [47246, 10040, 10162]),
(district_id:INTEGER, Examples: [18, 1, 5])
]
# Table: state
[
(StateCode:TEXT, Primary Key, Examples: [AL, AR, AZ]),
(State:TEXT, Examples: [Alabama, Arkansas, Arizona]),
(Region:TEXT, Examples: [South, West, Northeast])
]
# Table: events
[
(Date received:DATE, Examples: [2014-07-03]),
(Product:TEXT, Examples: [Bank account or service]),
(Sub-product:TEXT, Examples: [Checking account]),
(Issue:TEXT, Examples: [Deposits and withdrawals]),
(Sub-issue:TEXT),
(Consumer complaint narrative:TEXT),
(Tags:TEXT, Examples: [Older American]),
(Consumer consent provided?:TEXT, Examples: [N/A, Consent not provided, Consent provided]),
(Submitted via:TEXT, Examples: [Email, Fax, Postal mail]),
(Date sent to company:TEXT, Examples: [2014-07-09, 2012-04-13, 2012-04-03]),
(Company response to consumer:TEXT, Examples: [Closed with explanation]),
(Timely response?:TEXT, Examples: [Yes, No]),
(Consumer disputed?:TEXT, Examples: [No, Yes]),
(Complaint ID:TEXT, Primary Key, Examples: [CR0000072, CR0000084, CR0000140]),
(Client_ID:TEXT, Primary Key, Examples: [C00003714, C00001366, C00002330])
]
# Table: district
[
(district_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(city:TEXT, Examples: [New York City, Jacksonville, Columbus]),
(state_abbrev:TEXT, Examples: [NY, FL, OH]),
(division:TEXT, Examples: [Middle Atlantic, South Atlantic, East North Central])
]
【Foreign keys】
reviews.district_id=district.district_id
callcenterlogs.rand client=client.client_id
client.district_id=district.district_id
events.Complaint ID=callcenterlogs.Complaint ID
events.Client_ID=client.client_id
district.state_abbrev=state.StateCode

    【Question】
    Among the teenager clients who use Google account and Microsoft account, which group of client is more than the other? teenager refers to 13 < age < = 19; Google account refers to email like '%@gmail.com'; Microsoft account refers to email like '%@outlook.com'

    【Evidence】
    teenager refers to 13 < age < = 19; Google account refers to email like '%@gmail.com'; Microsoft account refers to email like '%@outlook.com'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CASE WHEN SUM(CASE WHEN email LIKE '%@gmail.com' THEN 1 ELSE 0 END) > SUM(CASE WHEN email LIKE '%@outlook.com' THEN 1 ELSE 0 END) THEN 'Google account' ELSE 'Microsoft account' END FROM client WHERE age BETWEEN 13 AND 19