Web Personalization (RTP) - Analyzing Content Recommendation Engine results in Google Analytics

 

 

Included in this Article:

 

 

 

Overview

If your account is liked to a Google Analytics (GA) account, you can use GA as a powerful tool to analyze the performance of the content being used in the Content Recommendation Engine (CRE). Below we show you how to navigate your GA account to get to this information. If you would like more information on analyzing CRE results within Marketo, see Define a Smart List for Web Personalization Activities - Marketo Docs - Product Docs

 

Create RTP Recommendation Engine Goal

 

1. Login to your Google Analytics account and go to 'Admin' > 'Goals' (under the VIEW column)

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2. Click the '+New Goal' button and set the following:

  1. Goal Setup: 'Custom'
  2. Goal Description
    1. Name: RTP Recommendations
    2. Type: Destination

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c. Goal Details

  1. Destination: (Regular Expression) to equal "rcmd"

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d. Click 'Create Goal'

 

This goal will allow you to easily see how many visitors clicked the recommendation engine.

Going to Reporting > Conversions > Goals > Overview  you will see stats about this goal completions:

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Comparing Recommended Content with Generic Content Consumption

 

Before running this comparison please import the following GA predefined segment from the GA gallery: RTP Recommendation Engine (Segment)

Make sure you add it to your view by clicking the '+Add Segment' button and searching the "RCMD True" segment.

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  1. Go to 'Reporting' > 'Behavior' > 'Site Content' > 'All Pages'
     
  2. Search for all pages that have 'rcmd' in their URL

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3. Add a secondary dimension: 'Page Title and export all the results as a CSV file (see easier workaround below)

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4. Go to the 'Page Title' Tab in the report

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5. Within the CSV file copy all the page titles (unique names) and paste them in the report's search box, separated by a | (i.e  Webinar for Financial sector|Contact Us|New York Summit)

 

6. The report will include now only recommendable pages and the top metrics will show comparison between the total and the recommended content consumption.

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