Use Adobe Analytics with Recommendations
Using Adobe Analytics as the behavioral data source lets clients use the view-based and/or purchase-based behavioral data from Analytics in Adobe Target recommendations activities. This feature is especially helpful in situations where the Target Recommendations setup is new and Analytics has a lot of historical data to leverage.
Using Analytics as the behavioral data source can act as a rich source of information about user behavior. This might include data from a third-party source or feed that is shared only with Analytics.
While creating criteria in Recommendations, there are two radio buttons that let you choose which data source is to be used: mboxes or Analytics.
If these two buttons do not display in your account, reach out to Customer Care .
Use Cases for Analytics data in Target
Using Analytics as the behavioral data source for recommendations also provides the ability to deploy specific use cases without the requirement of tagging entity pages with all the Target entity parameters. Although that requires certain pre-requisites to be in place, availability of "Product Variables" is the most important thing for that functionality to work seamlessly. Regular eVars and Props are not sufficient for this handshake to happen automatically between Analytics and Target.
You can use Analytics as the behavioral data source to:
- Display recommendations on a retail site to users on a PDP page, based on what other users purchased from the same category in the last month, using Analytics data.
- Display content on the home screen of a media site for the most popular content in a particular category that is currently trending, based on Analytics data.
Implementation in Analytics
The following sections will help you implement this feature on the Analytics side.
Prerequisites: set up product variables in Analytics
You must implement product variables in Analytics with the necessary attributes that are required for Target Recommendations.
A Target Recommendations sample feed format will act as guide on which all attributes need to be defined in the product variables. Later those values must be "mapped" within the Target UI for the respective Target entity values.
If it is a content site, the respective content pieces must be treated as "products" and associated attributes about that content (example: author name, publish date, content title, month of release, etc.) must be passed as attributes. Granularity of category level, or category types, should be decided by the business based on use-case requirements.
For more details on how to set up product variables, see products in the Analytics Implementation Guide . Some of the notes in that documentation need discretion of the team who is deploying it (example : Category). It is always advised to consult with Adobe before doing this activity.
Analytics data is sent via a daily feed. Behavioral results will take up to 24 hours to be reflected within recommendations results on your site. As with all Recommendations criteria settings, this data source can and should be tested.
For quick decision making on which data source is to be used, if there is a lot of organic data generated every day by users, and not much dependency required on historic data, then using a Target mbox as the behavioral data source can be a good fit. In cases of less availability of organic data generated recently, if you want to bank upon Analytics data, then the using Analytics as the behavioral data source is a good fit.
Steps to deploy
Assuming all the pre-requisites are in place, the following tasks must be performed by the Adobe Target Recommendations team:
The steps below are for illustrative purposes only. A member of the Recommendations team must currently perform these steps. Contact Customer Care for more information.
- In Target, click Administration > Implementation to acquire your Target client code.
- Acquire your Analytics report suite.Use your Analytics production site report suite. This is the report suite that tracks the site in which you have Recommendations deployed.
- In Analytics, click Admin > Data Feeds .
- Click Add to create a new feed.
- Fill in feed information:
- Name : Recs Prod Feed
- Report Suite : Your pre-determined report suite
- Email : Specify any appropriate address for an Admin user
- Feed interval : Select the desired interval
- Delay Processing : No delay.
- Start & End Dates : Continuous feed
- Fill in the details in the Destination section:Consult with the Adobe Analytics team before doing this step.
Screenshot is for reference purposes only. Your deployment will have different credentials. Consult with the Adobe Analytics team or Customer Care while doing this step.
- Type : FTP
- Host : xxx.yyy.com
- Path : Your Target client code
- Username : Specify your username
- Password : Specify your password
- Fill in the Data Column definitions:
- Compression Format : Gzip
- Packaging Type : Single File
- Manifest: Finish File
- Included Columns :The columns must be added in the same order documented here. Select the columns in the following order and click Add for each column.
- Click Save .
With this, the set up on Analytics side is complete. Now it is time to map these variables on Target side for continuous supply of behavioral data.
Implement in Target
- In Target, click Recommendations , then click the Feeds tab.
- Click Create Feed .
- Select Analytics Classifications , then specify the report suite.
- Click Mapping , then map the field column headers to the appropriate Recommendations field names.
- Click Save .
Frequently Asked Questions
Consider the following FAQs as you use Analytics with Target:
Are the entity.id and entity.categoryId values required to be passed within the Target mbox call?
Yes, those two values are still required. The rest of the attributes can be passed via an Analytics feed, as discussed in this document.
Can I use dynamic inclusion rules, such as entity parameter matches profile attributes using the Analytics feed approach?
Yes, you can. The method is similar when using Target stand-alone. In this case,however, you must be mindful about the timing factor. The entity variables that are supposed to match with the profile variables are dependent on the data layer that might appear much later on the page.