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What is the relationship between "Media Buy Key" and "Creative Key?
In Marketing Cloud Intelligence, the 'Media Buy Key' is typically associated with the purchase details of a media campaign, such as the platform, audience, and budget. The 'Creative Key' relates to the specific creative asset used within a campaign, like an image, video, or text. A single media buy can have multiple creative variations to test performance or to target different audiences, leading to a one-to-many relationship.
A client provides the following three files:
File A:
File B:
File C:
File A was uploaded using the Ads data stream type.
The client would like to create this view (data from Files B & C) in Datorama:
Which proposed solution would cause a false connection between the two files?
With File A uploaded using the Ads data stream type, the client wishes to create a view incorporating data from Files B & C.
A false connection would occur if VLOOKUP in Data Stream B is used incorrectly to return ''Day'' and ''Installs''. In this scenario, VLOOKUP might inaccurately link data based on MB Name between File B and File A or File C, which do not have a 'Day' field to correctly join on. Moreover, 'Installs' data in File B doesn't exist, so VLOOKUP cannot correctly return this information. The correct method would be to use the 'Media Buy New Name' to link File B and File C since they both have this field, ensuring accurate connection and avoiding data mismatches or false connections.
A client Ingested the following We into Marketing Cloud Intelligence:
The mapping of the above file can be seen below:
Date --- Day
Media Buy Key --- Media Buy Key
Campaign Name --- Campaign Name
Campaign Group -. Campaign Custom Attribute 01
Clicks ---> Clicks
Media Cost ---> Media Cost
Campaign Planned Clicks ---> Delivery Custom Metric 01
The client would like to have a "Campaign Planned Clicks" measurement.
This measurement should return the "Campaign Planned Clicks" value per Campaign, for example:
For Campaign Name 'Campaign AAA", the "Campaign Planned Clicks" should be 2000, rather than 6000 (the total sum by the number of Media Buy keys).
In order to create this measurement, the client considered multiple approaches. Please review the different approaches and answer the following question:
Which two options will yield a false result:
The goal is to obtain a 'Campaign Planned Clicks' value per Campaign, not accumulated by Media Buy keys. Option 1 (SUM aggregation function) would sum all the 'Campaign Planned Clicks' across Media Buy keys which would not yield the unique value per Campaign. Similarly, Option 5 (AVG aggregation function at Campaign Key level) would incorrectly average the values. Both options do not provide a way to return a singular 'Campaign Planned Clicks' value for each Campaign.
A client's data consists of three data streams as follows:
Data Stream A:
* The data streams should be linked together through a parent-child relationship.
* Out of the three data streams, Data Stream C is considered the source of truth for both the dimensions and measurements.
Assuming the data was ingested properly and the Parent Child was created correctly according to the client's requirements, what is the total
Impressions value for Campaign Key 'CK_3'?
Assuming that Data Stream A is set correctly with parent-child relationships:
To find the total impressions for Campaign Key 'CK_3', you would look in Data Stream A, since it contains the 'Impressions' metric.
As per the provided data, Campaign Key 'CK_3' has 100 impressions.
An implementation engineer is requested to extract the second position
of the Campaign Name values.
The Campaign values consist of multiple delimiter types, as can be
seen in the following example:
Campaign Name: Ad15X2w&Delux_wal90
Desired value: Delux
Which three harmonization methods will achieve the desired outcome?
To extract specific elements from a string in Marketing Cloud Intelligence, such as the second position of a Campaign Name with multiple delimiters, several harmonization methods can be employed:
Calculated Dimensions: These allow for the creation of custom dimensions using expressions or formulas that manipulate existing data. A calculated dimension can be designed to parse and extract segments of a string based on delimiters.
Patterns: This method involves defining a pattern or regex (regular expression) that matches and isolates the desired portion of the string. Patterns are highly effective for strings with complex structures and varying delimiter types.
Mapping Formula: Similar to calculated dimensions, mapping formulas provide a way to apply a transformation or extraction rule to data fields directly within data streams, enabling targeted data extraction like the desired 'Delux' from the Campaign Name.
These methods enable the implementation engineer to accurately segment and extract the needed data from complex string fields efficiently.
