|ITP 2.1||Capped client-side cookies that are placed on the browser using the document.cookie API to a seven-day expiry. Released February 21, 2019.|
|ITP 2.2||Drastically reduced the seven-day expiry cap to one day. Released April 24, 2019.|
To mitigate the impact of ITP 2.1, ITP 2.2, and future ITP releases, complete the following tasks:
- Deploy the Experience Cloud ID (ECID) library to your pages.
The ECID library enables the people identification framework for Experience Cloud Core solutions. The ECID library allows you to identify same site visitors and their data in different Experience Cloud solutions by assigning persistent and unique identifiers. The ECID library will be updated frequently to help you mitigate any ITP-related changes that impact your implementation.
For ITP 2.1 and ITP 2.2, ECID library 4.3.0+ must be utilized for mitigation.
2. Use Adobe’s CNAME and Enroll in Adobe Analytics’ Managed Certificate Program.
After installing the ECID library 4.3.0+, you can leverage Adobe Analytics’ CNAME and Managed Certificate Program. This program lets you implement a first-party certificate for first-party cookies at no charge. Leveraging CNAME will help customers mitigate the impact of ITP 2.1 and ITP 2.2.
If you are not leveraging CNAME, you can start the process by talking with your account representative and enrolling in the Adobe Managed Certificate Program .
I captured page load time in prop as mentioned in this article:
This will assign the page load time, in tenths of a second, to prop1. For example, if my page took 3.75 seconds to load, I would get a raw value of 38 in the Page Load Time (prop1) report.
So I did it simple by capturing the page load time in seconds.
Following is the report which I start getting after this:
This data is not much helpful for the marketer to understand data so it needs to be classified into range.I decided to create a classification for prop1 as shown below:
There are 2 option to classify the data.
- Classification file upload
- Classification rule builder
In classification rule builder we have to use Regx.
Less than 1 Sec : \b(^0|0.[1-9])\b
1-3 Seconds: \b(^1|1.[0-9]|^2|2.[0-9])\b
3-5 Seconds: \b(^3|3.[0-9]|^4|4.[0-9])\b
5-10 Seconds: \b(^5|5.[0-9]|^6|6.[0-9]|^7|7.[0-9]|^8|8.[0-9]|^9|9.[0-9])\b
More than 10 Seconds: [1-9][0-9]
After processing the data in report will look like as shown below:
Well explained on Guide to Using UTM Parameters in Adobe Analytics
I used following code to capture Tracking code:
Then I set up the calssification in Analytics
Below is the screen shot:
Then under classification rule builder I did following setup using regx ^(.+)\:(.+)\:(.+)\:(.+)\:(.+)$
The above regex will not accept value if any one of the field is missing like search:google:christmas::gifts or :google:christmas::gifts
To fix this we have to use : ^(.*)\:(.*)\:(.*)\:(.*)\:(.*)$
For reference check Adobe Doc
For testing, you can use https://rubular.com/
1.Pull following column through data feed:
2.Apply filter on column exclude_hit=0
3.Apply filter on column va_new_engagement=1
4.Apply filter on column va_closer_id=channel ID
Notice: The count should match to reports
- Difference b/w prop and evar
- What is the pathing report
- Explain segment comparison
- Explain the container in segments
- What is the use of calculated metric
- Analysis Workspace
- Visitor and visit concept
- Mid , experience cloud ID services
- Merchandizing evar
- Image request parsing
- Data collection query parameters
- What is DTM?
- In how many ways you can implement analytics
- Difference b/w Google Analytics and Adobe Analytics
- Export and import tools
- Data sources
- Processing rule
- Processing order
- Marketing channel
- Internal URL filter
- Reason of “other” in pages report
- S.t and s.tl implementation and syntax
- Difference b/w s.t and s.tl
- What is data range
- Virtual report suite
- Menu customization
- Experience cloud
- Report suite and rollup
- Multi suite tagging
- Difference b/w rollup and multisuite tagging
- What is the primary and secondary server call
- Key metrics
- First part cookie
- Tracking code
- Product string
- Visitor id identification method
- Data feed understanding
- Scenario-Based time spent on page.
- Scenario based segment configuration questions.
- Prop can correlate with (only prop or traffic sources)
- The difference in multi tagging and rollup report suite.
- Options available when on first screen of Report builder wizard when creating a request.
- who can create and share calculated metrics
- Total appears in report represents?
- Maximum number of line items that can appear on screen while running a report (answer:200)
- Recommended format to download or schedule file for 501 line items (csv)
- Data extract limitation (available only in csv format)
- when an alert is set on percentage change how it works
- Publishing list benefit
- How a non-admin user can share a report
- Definition of Report Acceleration
- variable character limit
- How total would be affected if we apply an advance filter on the reports
- what changes can be applied to a dashboard in one go which will affect all the reportlets
- If notes are added in any report who can view those notes
- definition of copy me, on menu, options available for dashboard
- When running page views or any other metrics report which are the default column that appears in report (4 week prior and 52 weeks prior).
Is Ad Cloud able to pass display media exposure back to AA, and then have this forwarded via Server
Advertising Cloud is able to pass data in Analytics via Adobe server to server integration for Adobe to understand if the ad impacted the consumer to visit the website. For example, Adobe can capture view-through data and understand if someone saw the display ad (delivered via Advertising Cloud) and then went to the advertisers’ web page a few days later (via the Analytics pixel on the web-page), we can capture that as a view-through conversion and say the ad contributed to the person visiting the website. From there, Analytics can understand other ways users are interacting with the web page (bounce rate, page views etc.) to help create audiences to re-target, or build look-a-likes (via pushing Analytics segments into Audience Manager). Additionally, we can create audiences via Analytics data and push those audience segments into Audience Manager through
You have the option to push audiences straight from Analytics into Advertising Cloud to target, or can push the audiences into Audience Manager for organization and then push from AAM to Ad Cloud to target.
The benefits of using all three products together are:
– Capture audience and website behavior and use it to build segments of in-market intenders
– Create a single view of the customer form your unified data, enriched and added data
– Deliver a personalized ad to an audience at the right time whenever they are in an authentic and relevant experience
– Continue to personalize through analysis, reporting & optimization
I am sure most of you must be aware of the “Mobile carrier” reports and it’s behavior in site catalyst.
Still, I would like to share a few inputs which I came across, and thought some people might not be aware of the same.
It’s a Visitor’s profile report that comes under Technology i.e Visitor’s profile à Technology à Mobile carrier
It helps identifying the traffic ( mobile visitors) by carrier i.e the mobile operators and shows the wireless service provider.
The third party : NetAcquity is the one which sources the data for us.
It is calculated as taking the unique key calculated by taking the combination of ISP + Domain. Hope this little information may help in understanding.
It is possible that at 12:00 pm a person was located in a city and at 3:00 pm he was identified in another city (geo.city in target). He was connected by 4G.
It is possible that their IP can change. Adobe takes their IP and
Mobile device geotargeting on 4G, LTE, etc. is less reliable than standard internet (Cable, Fiber, etc.)
See from Digital Element’s accuracy FAQ here: https://www.digitalelement.com/resources/faq/
Yes. Generally, Internet traffic can be broken down by connectivity type into:
1) Wired PC-based traffic
2) WiFi-based mobile-device and PC traffic and
3) Cell tower-based mobile device traffic.
IP geolocation data will enable you to accurately target the first two connectivity types – fixed and Wi-Fi. In terms of traffic from mobile devices specifically, Wi-Fi connections represent well over 80 percent of mobile Internet device traffic in terms of how users are connecting, meaning most mobile device traffic can be accurately targeted using IP geolocation.