How To mitigate the impact of ITP 2.1, ITP 2.2, and future ITP releases-Adobe Analytics

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:

  1. 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.

Check the link too: https://docs.adobe.com/content/help/en/id-service/using/reference/ecid-library-methods.html

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 .

How To Classify Page Load Time In Adobe Analytics

I captured page load time in prop as mentioned in this article:

https://theblog.adobe.com/measuring-site-speed-in-adobe-analytics/

s.prop1=s_getLoadTime();

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.

s.prop1=(s_getLoadTime()/10);

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:

Classify Tracking Code using Rule Builder

Well explained on Guide to Using UTM Parameters in Adobe Analytics

I used following code to capture Tracking code:

s.usePlugins=true;
s.doPlugins=function(s) {

if(s.Util.getQueryParam(‘utm_medium’)){
s.campaign=s.Util.getQueryParam(‘utm_medium’)+”:”+s.Util.getQueryParam(‘utm_source’)+”:”+s.Util.getQueryParam(‘utm_campaign’)+”:”+s.Util.getQueryParam(‘utm_content’)+”:”+s.Util.getQueryParam(‘utm_term’);
}
s.campaign=s.getValOnce(s.campaign,’s_campaign’,0);

}

Then I set up the calssification in Analytics

Below is the screen shot:

Then under classification rule builder I did following setup using regx ^(.+)\:(.+)\:(.+)\:(.+)\:(.+)$

Sample value:

search:google:christmas:article:gifts

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/

How To Cross Verify Marketing Channel through Data Feed

1.Pull following column through data feed:

exclude_hit        

va_closer_detail              

va_closer_id      

va_detail            

va_finder_detail              

va_finder_id      

va_instance_event          

va_master_id    

va_new_engagement

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

Data Feed column reference

Adobe Analytics Interview Questions

  1. Difference b/w prop and evar
  2. What is the pathing report
  3. Explain segment comparison
  4. Explain the container in segments
  5. What is the use of calculated metric
  6. Analysis Workspace
  7. Visitor and visit concept
  8. Mid , experience cloud ID services
  9. Merchandizing evar
  10. Image request parsing
  11. Getqueryparam
  12. Data collection query parameters
  13. What is DTM?
  14. In how many ways you can implement analytics
  15. Difference b/w Google Analytics and Adobe Analytics
  16. Export and import tools
  17. Data sources
  18. Classification
  19. Processing rule
  20. Processing order
  21. Marketing channel
  22. Internal URL filter
  23. Reason of “other” in pages report
  24. S.t and s.tl implementation and syntax
  25. Difference b/w s.t and s.tl
  26. What is data range
  27. Virtual report suite
  28. Menu customization
  29. Experience cloud
  30. Report suite and rollup
  31. Multi suite tagging
  32. Difference b/w rollup and multisuite tagging
  33. What is the primary and secondary server call
  34. Key metrics
  35. First part cookie
  36. Tracking code
  37. Product string
  38. Plugin
  39. Visitor id identification method
  40. Data feed understanding
  41. Scenario-Based time spent on page.
  42. Scenario based segment configuration questions.
  43. Prop can correlate with (only prop or traffic sources)
  44. The difference in multi tagging and rollup report suite.
  45. Options available when on first screen of Report builder wizard when creating a request.
  46. who can create and share calculated metrics
  47. Total appears in report represents?
  48. Maximum number of line items that can appear on screen while running a report (answer:200)
  49. Recommended format to download or schedule file for 501 line items (csv)
  50. Data extract limitation (available only in csv format)
  51. when an alert is set on percentage change how it works
  52. Publishing list benefit
  53. How a non-admin user can share a report
  54. Definition of Report Acceleration
  55. variable character limit
  56. How total would be affected if we apply an advance filter on the reports
  57. what changes can be applied to a dashboard in one go which will affect all the reportlets
  58. If notes are added in any report who can view those notes
  59. definition of copy me, on menu, options available for dashboard
  60. 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).

Impression data to Audience Manager (AAM)

Is Ad Cloud able to pass display media exposure back to AA, and then have this forwarded via Server side ?

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 server to server integration to house all segments in one place for organization.

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

Mobile carrier reports and it’s behavior in Adobe Analytics

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.

Different geo for the same person in Adobe Analytics

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 map it to a geo that is provided by geo-targeting vendor, Digital Element. The user’s ISP changed their IP address at some point.

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.