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
Amazon S3 or Amazon Simple Storage Service is a “simple storage service” offered by Amazon Web Services that provides object storage through a web service interface. Amazon S3 uses the same scalable storage infrastructure that Amazon.com uses to run its global e-commerce network.
The 2-letter codes shown below are supplied by the ISO ( International Organization for Standardization). It bases its list of country names and abbreviations on the list of names published by the United Nations. The UN also uses 3-letter codes, and numerical codes to identify nations, and those are shown below.
Adobe follow A3 (UN) notation for geo_country column.If visit has to be matched to Analytics reports for a specific country then the query is as follows:
select count(distinct(concat(post_visid_high,post_visid_low,visit_num))), pagename, page_event, post_event_list from test12345 where exclude_hit = ‘0’ and geo_country = ‘bra’ and hit_source = ‘1’ and bot_id = ‘0’;
For Visit following is the query:
select count(distinct(concat(post_visid_high,post_visid_low,visit_num))), pagename, page_event, post_event_list from test12345 where exclude_hit = ‘0’ and hit_source = ‘1’ and bot_id = ‘0’;
Data Connector is formerly known as Genesis. Adobe Data Connectors provide a complete development ecosystem to help Data Connectors partners integrate their products and services into the Adobe Experience Cloud.
Segment created in Adobe Analytics can be reused in Data Connector as Remarketing Segment.
Sometimes the segment is not available in Data Connector. There can be multiple reasons for it like
1. Data Connector owner does not have access to segment
2.The segment is not shared to owner
The major point often get missed, is the compatibility.If the segment is not compatible to Data Warehouse then it will not be available under Data Connector for re-marketing segment.
No one wants to miss the experience of Virtual Reality. During Adobe’s recent summit event held in London, the company revealed a new virtual reality (VR) application that it has in the works by the name of Project New View.
Adobe’s Project New View will help the marketing Professionals in the future to make decisions by analyzing through VR mode. Check its glimpse below and comment you opinion.
Decision trees, and data mining are useful techniques these days.A decision tree is a hierarchical relationship diagram that is used to determine the answer to an overall question. It does this by asking a sequence of sub-questions related to that question. Each branch of the diagram represents a possible choice or answer to a specific sub-question. And each sub-question iteratively reduces the number of remaining choices, or answers, until only the correct one for the overall question, in that particular situation, remains.
Let’s look at an example. In the diagram above, the overall question is, ‘Is the weather good enough to go outside?’ This isn’t a simple question to answer. There are a number of factors to consider. Each bubble in the diagram represents a factor, or sub-question, and each line represents a choice or answer to the sub-question above.
So the first sub-question we ask is, ‘Is it windy?’ If it is, we go down the left of the diagram, if not, we go down the right. Let’s say it is windy. That takes us to the ‘What is the outlook?’ sub-question. If the answer is sunny, we go down the left, if overcast down the center, and if rainy, down the right. Let’s say that it is sunny, so we go down the left. Then the next sub-question is ‘What is the humidity?’. If the humidity is less than 80 percent, the answer to the overall question is ‘Yes’. And if the humidity is greater than 80 percent, the answer is ‘No.’