こんにちは、私の名前はマーシャです。オゾンでマーケティングアナリストとして働いています。私たちのチーム「pythonite」と「escuelite」は、会社のマーケティング全体の利益のためにすべての手と足で。私の責任の1つは、オゾンディスプレイ広告チームの分析をサポートすることです。
オゾンディスプレイ広告は、Facebook、Google、MyTarget、TikTokなどのさまざまなプラットフォームで表示されます。広告キャンペーンを効果的に機能させるには、リアルタイムの分析が必要です。この記事では、仲介者や不必要なトラブルなしにTikTokプラットフォームから広告データを収集した私の経験に焦点を当てます。
統計を収集するタスク：入門
Ozonディスプレイ広告チームは、そのサイトのすべての広告を管理するTikTokビジネスアカウントを持っています。彼らは長い間耐え、広告所からデータを収集しましたが、それでも耐えられなくなった時が来ました。そこで、TikTokからの統計の収集を自動化するタスクを取得しました。
データベースにはTikTokからのキャンペーンの注文に関するデータがすでにあり、効果的な分析を行うための十分なコストデータがありませんでした。
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Callback Address
https://www.ozon.ru.
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https://www.ozon.ru/?auth_code=XXXXXXXXXXX
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auth_code
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app_id
TikTok long-term Access Token.
curl -H "Content-Type:application/json" -X POST \
-d '{
"secret": "SECRET",
"app_id": "APP_ID",
"auth_code": "AUTH_CODE"
}' \
https://ads.tiktok.com/open_api/v1.2/oauth2/access_token
:
{
"message": "OK",
"code": 0,
"data": {
"access_token": "XXXXXXXXXXXXXXXXXXXX",
"scope": [4],
"advertiser_ids": [
1111111111111111111,
2222222222222222222]
},
"request_id": "XXXXXXXXXXXXXXX"
}
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METRICS = [
"campaign_name", #
"adgroup_name", #
"ad_name", #
"spend", # ( )
"impressions", #
"clicks", #
"reach", # ,
"video_views_p25", # 25%
"video_views_p50", # 50%
"video_views_p75", # 75%
"video_views_p100", # 100%
"frequency" #
]
TikTok API Java, Python, PHP curl-. Python .
TikTok :
pip install requests pip install six
requests
get-. six
url- .
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pip install pandas pip install sqlalchemy
SQL- , pandas
DataFrame sqlalchemy
DataFrame .
TikTok url .
# url args
def build_url(args: dict) -> str:
query_string = urlencode({k: v if isinstance(v, string_types) else json.dumps(v) for k, v in args.items()})
scheme = "https"
netloc = "ads.tiktok.com"
path = "/open_api/v1.1/reports/integrated/get/"
return urlunparse((scheme, netloc, path, "", query_string, ""))
# TikTok Marketing API,
# json
def get(args: dict, access_token: str) -> dict:
url = build_url(args)
headers = {
"Access-Token": access_token,
}
rsp = requests.get(url, headers=headers)
return rsp.json()
get
access token. :
args = {
"metrics": METRICS, # ,
"data_level": "AUCTION_AD", #
"start_date": 'YYYY-MM-DD', #
"end_date": 'YYYY-MM-DD', #
"page_size": 1000, # - ,
"page": 1, # ( , )
"advertiser_id": advertiser_id, # ID advertiser_ids, access token
"report_type": "BASIC", #
"dimensions": ["ad_id", "stat_time_day"] # ,
}
page_size
: . TikTok – 1000. , . .
get
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{
#
"message": "OK",
"code": 0,
"data": {
#
"page_info": {
#
"total_number": 3000,
#
"page": 1,
#
"page_size": 1000,
#
"total_page": 3
},
#
"list": [
#
{
#
"metrics": {
"video_views_p25": "0",
"video_views_p100": "0",
"adgroup_name": "adgroup_name",
"reach": "0",
"spend": "0.0",
"frequency": "0.0",
"video_views_p75": "0",
"video_views_p50": "0",
"ad_name": "ad_name",
"campaign_name": "campaign_name",
"impressions": "0",
"clicks": "0"
},
# ( )
"dimensions": {
"stat_time_day": "YYYY-MM-DD HH: mm: ss",
"ad_id": 111111111111111
}
},
...
]
},
# id
"request_id": "11111111111111111111111"
}
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page = 1 #
result_dict = {} # ,
result = get(args, access_token) #
result_dict[advertiser_id] = result['data']['list'] #
# page
# result
while page < result['data']['page_info']['total_page']:
# 1
page += 1
#
args['page'] = page
# page
result = get(args, access_token)
#
result_dict[advertiser_id] += result['data']['list']
advertiser_ids
.
. pandas.DataFrame
.
# DataFrame,
data_df = pd.DataFrame()
#
for adv_id in advertiser_ids:
#
adv_input_list = result_dict[adv_id]
#
adv_result_list = []
#
for adv_input_row in adv_input_list:
#
metrics = adv_input_row['metrics']
#
metrics.update(adv_input_row['dimensions'])
#
adv_result_list.append(metrics)
# DataFrame
result_df = pd.DataFrame(adv_result_list)
# id
result_df['account'] = adv_id
# DataFrame
data_df = data_df.append(
result_df,
ignore_index=True
)
#
#
#
#
# DataFrame
data_df.to_sql(
schema=schema,
name=table,
con=connection,
if_exists = 'append',
index = False
)
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#
import json
from datetime import datetime
from datetime import timedelta
import requests
from six import string_types
from six.moves.urllib.parse import urlencode
from six.moves.urllib.parse import urlunparse
import pandas as pd
import sqlalchemy
# url args
def build_url(args: dict) -> str:
query_string = urlencode({k: v if isinstance(v, string_types) else json.dumps(v) for k, v in args.items()})
scheme = "https"
netloc = "ads.tiktok.com"
path = "/open_api/v1.1/reports/integrated/get/"
return urlunparse((scheme, netloc, path, "", query_string, ""))
# TikTok Marketing API,
# json
def get(args: dict, access_token: str) -> dict:
url = build_url(args)
headers = {
"Access-Token": access_token,
}
rsp = requests.get(url, headers=headers)
return rsp.json()
#
# (, start_date end_date, [start_date, end_date])
def update_tiktik_data(
# API TikTok
tiktok_conn: dict,
#
db_conn: dict,
# id
advertiser_ids: list,
# :
start_date:datetime=None,
# :
end_date:datetime=None
):
access_token = tiktok_conn['password']
start_date = datetime.now() - timedelta(7) if start_date is None else start_date
end_date = datetime.now() - timedelta(1) if end_date is None else end_date
START_DATE = datetime.strftime(start_date, '%Y-%m-%d')
END_DATE = datetime.strftime(end_date, '%Y-%m-%d')
SCHEMA = "schema"
TABLE = "table"
PAGE_SIZE = 1000
METRICS = [
"campaign_name", #
"adgroup_name", #
"ad_name", #
"spend", # ( )
"impressions", #
"clicks", #
"reach", # ,
"video_views_p25", # 25%
"video_views_p50", # 50%
"video_views_p75", # 75%
"video_views_p100", # 100%
"frequency" #
]
result_dict = {} # ,
for advertiser_id in advertiser_ids:
page = 1 #
args = {
"metrics": METRICS, # ,
"data_level": "AUCTION_AD", #
"start_date": START_DATE, #
"end_date": END_DATE, #
"page_size": PAGE_SIZE, # - ,
"page": 1, # ( , )
"advertiser_id": advertiser_id, # ID advertiser_ids, access token
"report_type": "BASIC", #
"dimensions": ["ad_id", "stat_time_day"] # ,
}
result = get(args, access_token) #
result_dict[advertiser_id] = result['data']['list'] #
# page ,
# result
while page < result['data']['page_info']['total_page']:
# 1
page += 1
#
args['page'] = page
# page
result = get(args, access_token)
#
result_dict[advertiser_id] += result['data']['list']
# DataFrame,
data_df = pd.DataFrame()
#
for adv_id in advertiser_ids:
#
adv_input_list = result_dict[adv_id]
#
adv_result_list = []
#
for adv_input_row in adv_input_list:
#
metrics = adv_input_row['metrics']
#
metrics.update(adv_input_row['dimensions'])
#
adv_result_list.append(metrics)
# DataFrame
result_df = pd.DataFrame(adv_result_list)
# id
result_df['account'] = adv_id
# DataFrame
data_df = data_df.append(
result_df,
ignore_index=True
)
#
#
#
#
#
connection = sqlalchemy.create_engine(
'{db_type}://{user}:{pswd}@{host}:{port}/{path}'.format(
db_type=db_conn['db_type'],
user=db_conn['user'],
pswd=db_conn['password'],
host=db_conn['host'],
port=db_conn['port'],
path=db_conn['path']
)
)
#
with connection.connect() as conn:
conn.execute(f"""delete from {SCHEMA}.{TABLE}
where date >= '{START_DATE}' and date <= '{END_DATE}'""")
# DataFrame
data_df.to_sql(
schema=SCHEMA,
name=TABLE,
con=connection,
if_exists = 'append',
index = False
)
!
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