analytics-mayhem-adobe 0.0.10

Manage Adobe Analytics Reports API v2 requests to build reports programmatically.

Adobe Analytics Python package

Download Reports data utilising the Adobe.io version 2.0 API.

For more Digital Analytics related reading, check https://analyticsmayhem.com

Authentication methods supported by the package:

1. JWT
2. OAuth (tested only through Jupyter Notebook!)

JWT Requirements & Adobe.io access

In order to run the package, first you need to gain access to a service account from Adobe.io. The method used is JWT authentication. More instructions on how to create the integration at: https://www.adobe.io/authentication/auth-methods.html#!AdobeDocs/adobeio-auth/master/JWT/JWT.md. After you have completed the integration, you will need to have available the following information:

• Organization ID (issuer): It is in the format of < organisation id >@AdobeOrg
• Technical Account ID: < tech account id >@techacct.adobe.com
• Client ID: Information is available on the completion of the Service Account integration
• Client Secret: Information is available on the completion of the Service Account integration
• Account ID: Instructions on how to obtain it at https://youtu.be/lrg1MuVi0Fo?t=96
• Report suite: Report suite ID from which you want to download the data.

Make sure that the integration is associated with an Adobe Analytics product profile that is granted access to the necessary metrics and dimensions.

OAuth Requirements

To perform an OAuth authentication you need to create an integration at the Adobe I/O Console as described in the guide by Adobe at https://github.com/AdobeDocs/analytics-2.0-apis/blob/master/create-oauth-client.md. The result of the integration provides the following information:

• Client ID (API Key)
• Client Secret

Package installation

pip install analytics-mayhem-adobe

Samples

Initial setup - JWT

After you have configured the integration and downloaded the package, the following setup is needed:

from analytics.mayhem.adobe import analytics_client
import os

ADOBE_ORG_ID = os.environ['ADOBE_ORG_ID']
SUBJECT_ACCOUNT = os.environ['SUBJECT_ACCOUNT']
CLIENT_ID = os.environ['CLIENT_ID']
CLIENT_SECRET = os.environ['CLIENT_SECRET']
PRIVATE_KEY_LOCATION = os.environ['PRIVATE_KEY_LOCATION']
GLOBAL_COMPANY_ID = os.environ['GLOBAL_COMPANY_ID']
REPORT_SUITE_ID = os.environ['REPORT_SUITE_ID']

Next initialise the Adobe client:

aa = analytics_client(
        adobe_org_id = ADOBE_ORG_ID, 
        subject_account = SUBJECT_ACCOUNT, 
        client_id = CLIENT_ID, 
        client_secret = CLIENT_SECRET,
        account_id = GLOBAL_COMPANY_ID, 
        private_key_location = PRIVATE_KEY_LOCATION
)

aa.set_report_suite(report_suite_id = REPORT_SUITE_ID)

Initial setup - OAuth

Import the package and initiate the required parameters

from analytics.mayhem.adobe import analytics_client

client_id = '<client id>'
client_secret = '<client secret>'
global_company_id = '<global company id>'

Initialise the Adobe client:

aa = analytics_client(
        auth_client_id = client_id, 
        client_secret = client_secret,
        account_id = global_company_id
)

Perform the authentication

aa._authenticate()

This will open a new window and will request you to login to Adobe. After you complete the login process, you will be redirect to the URL you configured as redirect URI during the Adobe Integration creation process. If everything is done correctly, final URL will have a URL query string parameter in the format of www.adobe.com/?code=eyJ..... Copy the full URL and paste it in the input text. For a demo notebook, please refer to the Jupyter Notebook - OAuth example

Report Configurations

Set the date range of the report (format: YYYY-MM-DD)

aa.set_date_range(date_start = '2019-12-01', date_end= '2019-12-31')

To configure specific hours for the start and end date:

aa.set_date_range(date_start='2020-12-01', date_end='2020-12-01', hour_start= 4, hour_end= 5 )

If hour_end is set, then only up to that hour in the last day data will be retrieved instead of the full day.

Request with 3 metrics and 1 dimension

aa.add_metric(metric_name= 'metrics/visits')
aa.add_metric(metric_name= 'metrics/orders')
aa.add_metric(metric_name= 'metrics/event1')
aa.add_dimension(dimension_name = 'variables/mobiledevicetype')
data = aa.get_report()

Output:

itemId_lvl_1	value_lvl_1	metrics/visits	metrics/orders	metrics/event1
0	Other	5000	3	100
1728229488	Tablet	200	45	30
2163986270	Mobile Phone	49	23	31
...	...	...	...	...

Request with 3 metrics and 2 dimensions:

aa.add_metric(metric_name= 'metrics/visits')
aa.add_metric(metric_name= 'metrics/orders')
aa.add_metric(metric_name= 'metrics/event1')
aa.add_dimension(dimension_name = 'variables/mobiledevicetype')
aa.add_dimension(dimension_name = 'variables/lasttouchchannel')
data = aa.get_report_multiple_breakdowns()

Output: Each item in level 1 (i.e. Tablet) is broken down by the dimension in level 2 (i.e. Last Touch Channel). The package downloads all possible combinations. In a similar fashion more dimensions can be added.

itemId_lvl_1	value_lvl_1	itemId_lvl_2	value_lvl_2	metrics/visits	metrics/orders	metrics/event1
0	Other	1	Paid Search	233	39	10
0	Other	2	Natural Search	424	12	412
0	Other	3	Display	840	41	31
...	...	...	...	...	...	...
1728229488	Tablet	1	Paid Search	80	12	41
1728229488	Tablet	2	Natural Search	50	41	21
...	...	...	...	...	...	...

Global segments

To add a segment, you need the segment ID (currently only this option is supported). To obtain the ID, you need to activate the Adobe Analytics Workspace debugger (https://github.com/AdobeDocs/analytics-2.0-apis/blob/master/reporting-tricks.md). Then inspect the JSON request window and locate the segment ID under the 'globalFilters' object.

To apply the segment:

aa.add_global_segment(segment_id = "s1689_5ea0ca222b1c1747636dc970")

Issues, Bugs and Suggestions:

https://github.com/konosp/adobe-analytics-reports-api-v2.0/issues

Known missing features:

• No support for filtering
• No support for custom sorting