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

    【Schema】
    【DB_ID】 bike_share_1
【Schema】
# Table: station
[
(id:INTEGER, Primary Key, Examples: [2, 3, 4]),
(name:TEXT, Examples: [San Jose Diridon Caltrain Station]),
(lat:REAL, Examples: [37.329732, 37.330698, 37.333988]),
(long:REAL, Examples: [-121.90178200000001, -121.888979, -121.894902]),
(dock_count:INTEGER, Examples: [27, 15, 11]),
(city:TEXT, Examples: [San Jose, Redwood City, Mountain View]),
(installation_date:TEXT, Examples: [8/6/2013, 8/5/2013, 8/7/2013])
]
# Table: status
[
(station_id:INTEGER, Examples: [2, 3, 4]),
(bikes_available:INTEGER, Examples: [2, 3, 4]),
(docks_available:INTEGER, Examples: [25, 24, 23]),
(time:TEXT, Examples: [2013/08/29 12:06:01, 2013/08/29 12:07:01, 2013/08/29 12:08:01])
]
# Table: trip
[
(id:INTEGER, Primary Key, Examples: [4069, 4073, 4074]),
(duration:INTEGER, Examples: [174, 1067, 1131]),
(start_date:TEXT, Examples: [8/29/2013 9:08, 8/29/2013 9:24, 8/29/2013 9:25]),
(start_station_name:TEXT, Examples: [2nd at South Park]),
(start_station_id:INTEGER, Examples: [64, 66, 22]),
(end_date:TEXT, Examples: [8/29/2013 9:11, 8/29/2013 9:42, 8/29/2013 9:43]),
(end_station_name:TEXT, Examples: [2nd at South Park]),
(end_station_id:INTEGER, Examples: [64, 69, 22]),
(bike_id:INTEGER, Examples: [288, 321, 317]),
(subscription_type:TEXT, Examples: [Subscriber, Customer]),
(zip_code:INTEGER, Examples: [94114, 94703, 94115])
]
# Table: weather
[
(date:TEXT, Examples: [8/29/2013, 8/30/2013, 8/31/2013]),
(max_temperature_f:INTEGER, Examples: [74, 78, 71]),
(mean_temperature_f:INTEGER, Examples: [68, 69, 64]),
(min_temperature_f:INTEGER, Examples: [61, 60, 57]),
(max_dew_point_f:INTEGER, Examples: [61, 57, 60]),
(mean_dew_point_f:INTEGER, Examples: [58, 56, 60]),
(min_dew_point_f:INTEGER, Examples: [56, 54, 53]),
(max_humidity:INTEGER, Examples: [93, 90, 87]),
(mean_humidity:INTEGER, Examples: [75, 70, 68]),
(min_humidity:INTEGER, Examples: [57, 50, 49]),
(max_sea_level_pressure_inches:REAL, Examples: [30.07, 30.05, 30.0]),
(mean_sea_level_pressure_inches:REAL, Examples: [30.02, 30.0, 29.96]),
(min_sea_level_pressure_inches:REAL, Examples: [29.97, 29.93, 29.92]),
(max_visibility_miles:INTEGER, Examples: [10, 9, 8]),
(mean_visibility_miles:INTEGER, Examples: [10, 9, 8]),
(min_visibility_miles:INTEGER, Examples: [10, 7, 6]),
(max_wind_Speed_mph:INTEGER, Examples: [23, 29, 26]),
(mean_wind_speed_mph:INTEGER, Examples: [11, 13, 15]),
(max_gust_speed_mph:INTEGER, Examples: [28, 35, 31]),
(precipitation_inches:TEXT, Examples: [0, 0.23, T]),
(cloud_cover:INTEGER, Examples: [4, 2, 6]),
(events:TEXT, Examples: [Fog, Rain, Fog-Rain]),
(wind_dir_degrees:INTEGER, Examples: [286, 291, 284]),
(zip_code:TEXT, Examples: [94107, 94063, 94301])
]

    【Question】
    What is the name of the station that is less used by customers who borrow bikes from? Indicate when was the station installed. less used station where bikes are borrowed from refers to start_station_name which has the least number of customers; subscription_type = 'Customer'; when installed refers to installation_date;

    【Evidence】
    less used station where bikes are borrowed from refers to start_station_name which has the least number of customers; subscription_type = 'Customer'; when installed refers to installation_date;

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

SQL: SELECT T1.start_station_name, T2.installation_date FROM trip AS T1 INNER JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T1.subscription_type = 'Customer' GROUP BY T1.start_station_name ORDER BY COUNT(T1.subscription_type) LIMIT 1