Help Center

Both publishers and content marketers alike are challenged with devising strategies to engage readers and generate sought-after advertising revenue through increased page views. You can use BlueConic personalized content recommendations to engage and retain your customers and visitors, creating rich content experiences that deliver the right content at the right time. Increased engagement with readers online can lead to longer session durations, increased ad revenue, and reader loyalty.

Value-Based Outcome

All BlueConic use cases are associated with one or more Value-Based Outcomes, which are defined areas of focus that help you measure the value of a CDP to your overall business. This use case can be closely related to the Value-Based Outcome of Smarter Customer Engagement, which focuses on communicating with customers in smarter, more actionable ways that are creative and dynamic.

Case Studies

• Endeavor Automates Online Content Journeys and Productizes Audience Data

Before You Begin

Before you begin implementing smarter content recommendations, make sure you are familiar with what a CDP use case is and all of the components that will make yours more successful. Once this is done, you can start defining your “Before You Begin” requirements, which includes identifying key stakeholders and target audiences, gathering data sources and credentials, and defining what success looks like.

For this use case, example “Before You Begin” requirements may look something like this:

• Key stakeholders: Editorial teams
• Target audience: All visitors to your content channels
• Data types: Content information
• Platforms: Content management systems
• Measurement and ROI: Clicks and clicks to views ratios, time on site, average page views per visits

Basic Overview

To execute this use case, you will follow these basic steps:

1. Collect content data with a Content Collector connection.
2. Activate data by creating a content recommendations dialogue that incorporates algorithms and filters.
3. Test and launch your content recommendations dialogue.
4. Refine your content recommendations over time to become smarter and deliver higher impact.

Use Case Configuration Steps

Step one: Collect data

A Content Collector connection is used as a content store within BlueConic to gather information that can be pulled for recommendations. 

• To create a Content Collector connection, select Connections from the main BlueConic menu and click the Add connection button. Search for 'content collector' and create the connection.
   
• To set up the connection, select "Collect data from your channels" in the left-hand panel. Then select the channel(s) to collect content data from. You can optionally define URL rules to specify which areas of the sites to collect content data from.
  
• Next, manage which data is collected by pasting an article URL into the Test URL field, and clicking 'Test' to review the metadata that would be collected for your content. Once the test metadata has been generated, you can make edits and updates to the default settings. For more information on the metadata BlueConic collects, refer to this article on Configuring the Content Collector to collect items for recommendations. A few things to keep in mind during this step:
  • Use the smallest resolution image possible, such as a thumbnail. This will help decrease storage costs and improve load times for your recommendations.
  • Implement a unique article ID for your content. This will decrease the chance of duplicated recommendations with content that has been updated or revised.
  • Add any custom metadata fields that will help with categorization or tagging later on.
    
• Once everything looks correct in the test, set the algorithm time frame. Some recommendation algorithms are based on a look-back time frame (for example, Viral articles or recent high CTR). For details on how each recommendation algorithm operates, see BlueConic recommendation algorithms.
  
• Finally, you can optionally set request headers to collect content recommendations from web pages that are under development or behind a login or paywall. Click "Add request header," choose a channel, and add the custom header name and value used to access the content.
  

Step two: Activate data

Note: Before creating a content recommendations dialogue, make sure you have the Content Recommendations toolbar plugin added to your tenant by going to Plugins in the main Settings menu. If it is not already listed, click the Add plugin button, search for “Content Recommendations,” and click the green plus button.

• Now, go to Dialogues in the main menu and click the Add dialogue button.
• Choose the Content dialogue from the pop-up window and when the new page opens up, name your dialogue and click Save. 
  • Note: Content recommendations can also be served to your website with other dialogue types, like a lightbox or toaster.
    
• To set up the dialogue, configure the following settings:
  • Who: For this use case, leave this setting as the “All visitors” segment for the widest impact. However, you may want to adjust this later on depending on the recommendations algorithm.
    
  • When: Again, leave this setting as the default option. However, if you wanted to adjust when or how often this dialogue is shown to visitors, you would do so here.
    
  • Where: In this setting, define the channel(s) you want the dialogue to appear on. For content recommendations, this will be whatever channel houses your digital content.
    
  • What: This is where you will define what your dialogue will look like on your website with the Content Recommendations toolbar plugin. 
    • Inside your dialogue, select the content editor to insert a content placement based on a recommendation algorithm of your choosing at the place of the cursor. 
    • To add a recommendations placement, edit an interaction and select Insert object > Content Recommendations from the content editor toolbar.
      
    • When you select "Content Recommendations" BlueConic adds a placeholder placement to the content editor. Hovering over the placeholder makes it light up in blue. Click and edit or double-click the placeholder to open the Content recommendations pop-up where you can configure the content placement. In this pop-up, you can configure:
      • Collector: Set this as the Content Collector connection you set up earlier.
      • Layout: Click the dropdown menu to choose a template to display your recommendations -- for example, in a list of links, or a list with images, etc. You can also choose to edit your own template for recommendations.
      • Frequency cap: You can choose to exclude articles after the user has seen the article a set number of times and not clicked on them.
      • Recommendation sets: Choose the number of articles or posts you'd like to include in the recommendations that are displayed. By default, there is one recommendation set showing four items with one algorithm and one filter. Add new sets of recommendations with different algorithms and filters to select the recommended content.
      • Filters: Filters can be used to refine your content recommendations even further. For example, set a filter to weed out content older than a specific amount of time or set a filter to only show recommendations that have a thumbnail image.
      • Enable fallback: You can set a fallback algorithm so if the recommendation set(s) you've defined don't deliver enough results to fill a set, you can fill the empty space with other items. If you choose to enable fallback recommendations, content items that don't already appear in the recommendation set and also match the configured algorithms and filtering options are used to fill any empty spots in the recommendations area.
        

• • Why: You can leave this setting as the default option or you can choose to configure a unique conversion moment for your dialogue here.
• Once your dialogue is configured correctly, switch the toggle at the top of the page to “On” and remember to Save.

Step three: Test and launch

Create test segments

For testing purposes of this use case, you will want to create two new segments that track how many visitors view and click on your new dialogue within a BlueConic insights dashboard.

Do this by going to Segments and clicking the Add segment button. Name your new segment “Views of [Content Recommendations Dialogue Name]” and hit Save. After creating your segment, you will define the appropriate conditions by going to the Drill down condition section and opening the Select condition dropdown list. For the dropdown list, select profile property to open a pop-up window.

Once in the pop-up window, use the Search function and type “variants” into the field. Of the results, select Variants viewed and choose “Variant A” of your Content Recommendations dialogue. Click Save to finalize your segment settings.

To create the second segment named “Clicks of [Content Recommendations Dialogue Name]”, repeat the above steps. For the profile property, use Variants Clicked to isolate visitors who not only saw your content recommendations, but also clicked on them.

A/B testing

After you have created your Content Recommendations Dialogue and it is live on your channels, you may want to set up A/B testing to optimize its performance.

You can do this by opening up the original dialogue you created and creating new variants of the same dialogue. To create new variants, select Optimization in the top right corner, click Add variant under the existing variants in the Optimization tab of the Dialogues menu.

You can create as many variants as you would like to test out different types of content recommendations and algorithms. Once your variants are created, in the Optimization tab, you can choose the desired distribution for each variant, but we recommend an even split.

Now you can allow your dialogue to run, automatically rotating through the variants, and collecting feedback on which one has the best clicks to views ratio.

Content Recommendations Insight Dashboard

Create a new insights BlueConic dashboard for this use case by going to More > Insights in the main navigation menu and clicking the Add dashboard button. Enter a name for your dashboard that correlates with your use case and click Save. After the dashboard is created, you can click the Add insight button to choose which insights you want displayed that will measure the success of your content recommendations. A few suggested insights include:

• Dialogues Table
  • Using the Search box, search for the Dialogues Table. To configure this insight, choose the position that represents your content recommendations placement. Next, click the Table columns option, and select the metrics you’d like to view. Check off the options for Views, Clicks, and Clicks/views ratio. Click OK.
• Profile Property
  • Using the Search box, search for the Profile Property Insight and add two of them to your dashboard. To configure the first Profile Property Insight, select the profile property for Page views (All Visits) and select the radio button next to Use segment. Search for the name of the segment you created that tracks clicks of the content recommendations dialogue. Click OK. Repeat the same steps for the second Profile Property Insight on your dashboard. Except for this insight, search for the name of the segment you created that tracks views of the content recommendations dialogue.
• Top recommended items
  • Using the Search box, search for Recommendations: Top Items Insight. To configure the insight, select the Content Collector connection you created and whether you want to see results displayed in views or clicks. Click OK. The insight then shows a scrollable list of the top 50 personalized content recommendations for your Content Collector connection with the number of views or clicks recorded.
    

Google Analytics 4 testing

In addition to using BlueConic insights to report on the click-through rate of your content recommendations dialogues, you can also use Google Analytics 4.

To do this, you will want to instrument the hyperlink <a></a> tags of your content recommendations templates with Google Analytics 4 event tracking similar to the following:

 <a href="{{URL}}" title="{{name}}" onclick="ga('send', 'event', 'bc_dialogue',
  'bc_recs', 'bc_name_of_dialogue');">{{name}}</a>

In this code, bc_dialogue will refer to the fact that the click was generated from a BlueConic dialogue, bc_recs will refer to the fact that the click was for a content recommendation, and bc_name_of_dialogue will refer specifically to the name of the dialogue in question.

With this level of click detail in Google Analytics 4, you will not only be able to attribute clicks from BlueConic dialogues (in general) but also from content recommendations down to the dialogue that generated the click.

Next steps: Refine and personalize your use case

• Set up Open-Time Email Recommendations
• Create your own personalized content recommendations template
• Play around with different BlueConic recommendations algorithms

Vertical Use Case Ideas

• Top CDP Use Cases for Media and Publishing