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Create personalized content recommendations in BlueConic

BlueConic offers a range of tools to customize personalized content recommendations. The Content Recommendations toolbar plugin adds a capability to the toolbar of the content editor that lets you insert content placements, dependent on having set up a Content Collector Connection. Click on the icon to insert a content placement based on an algorithm of your choosing at the place of the cursor.

You can vary recommendations so they are not the same on every page:

  • Use contextual filters and algorithms based on the current page (look-alike algorithm, metadata filters based on current page, etc.).
  • Create multiple variants with different settings and make them rotate.
  • Use the option in the filters area to exclude content if it hasn't been clicked on a after a certain number of views, for example.
  • Use a fallback set of recommendations to fill the recommended items area if your algorithms and filters don't produce enough new content to recommend.

To add a recommendations placement, edit an interaction and click "Insert object" from the content editor toolbar:

How to add personalizationn with content recommendations to Dialogues in BlueConic to create individualized marketing and personalization

Selecting "Content Recommendations" adds a placeholder placement to the content editor. Hovering over the placeholder will light it 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.

Configuring content recommendations

How to do personalization and personalized content recommendations in the BlueConic customer data platform (CDP)

Collector: Select a Collector, which serves as a source from which recommended items will be chosen. You define this by configuring a Content Collector Connection.

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.

You can have multiple sets of recommendations. Double-click the recommendation set to edit or adjust its algorithms and filters. Click copy How to choose personalization recommendation algorithms and content filters for personalized content recommendations in BlueConic to duplicate a recommendation set. If you have more than one, you can reorder the recommendation sets by dragging and dropping the recommendation bar up or down. Click delete How to select personalization content recommendation algorithms powered by AI and Machine Learning in the BlueConic CDP to remove a recommendation set.

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.

How to enable fallback items for BlueConic personalizatin and content recommendations sets; content store


To refine the algorithms that choose recommended items, click the algorithm box in a recommendation set.

How to create individualized marketing and personalization with content recommendations in the BlueConic customer data platform

The algorithms tab opens, where you can adjust the algorithms that select content for each individual user. To return to the main content recommendations window, click the arrow in the upper left corner.

How to use machine learning algorithms for BlueConic personalization and content recommendations using the content store

As you make changes, you should be able to see them reflected in the content placement in your editor window:

How to create content recommendations for personalization with BlueConic via the BlueConic content store

There are a number of algorithms you can introduce, to boost the prevalence of articles with certain characteristics as described below. For each algorithm added, you can control whether it is used at all, and to what degree it will be incorporated into the overall content selection.

What are the BlueConic personalization content algorithms that create individualized marketing recommendations using machine learning and AI?

Content recommendation algorithms

BlueConic offers recommendation algorithms that are profile-based, aggregate usage stat-based, and content-based. For details on how each algorithm operates, see BlueConic recommendation algorithms.

Aggregate stats-based algorithms:

  • Viral news: Content that has been popular as an entry point to the site over the last several hours.
  • Recent high CTRs: Content that has been clicked within content recommendations placements served by BlueConic.
  • Breaking news: Content that has been read the most across the site over the last several hours.

Content-based algorithms:

  • Same category: Content that is in the same category as the current article being read.
  • Look-alike articles: Content that has a similar textual makeup (title, description, and text) to the current article being read.
  • Recency: Content that has a recent published date.
  • Seen articles: Boost articles the viewer recently viewed.

Profile-based algorithms:

  • Collaborative filtering: Content read by other users similar to the current user.
  • Same interest: Content in the same categories as those the user has shown the most interest in.

Tip: Not sure which algorithm to choose? Choose a few different algorithms to try. You can test them with A/B testing and dialogue optimization.

The time frame defined for content algorithms is defined at the bottom of the Content Collector connection.

Setting the ramp up speed of profile-based algorithms

Profile-based algorithms also take a "ramp up speed" setting, which enables BlueConic to react more quickly to any information that is being populated in the user profile:

How do I set the ramp-up speed of profile-based personalization algorithms in BlueConic and the content store?


How do I use metadata to include or exclude content in BlueConic personalization content recommendations and the content store?

The filters area allows you to include or exclude content based on its metadata or other options.

Seen articles: Either exclude articles or only show articles the user recently viewed.

Metadata filters

You can specify how metadata filters should be applied to recommendation sets. When you click the Add filter button, you can construct a new metadata filter tailored to the data your content collector gathers. For example, if your content consists of articles tagged by the categories Sports or Entertainment, you can filter for articles that exactly match those categories.

How do I apply personalization using metadata filters to BlueConic recommendation sets via the content store?

See your top content recommendations

To see which content items are recommended most often, use the Top recommended items insight.

FAQ: Content recommendations

Q: What is the BlueConic Content Store?

The content store is a pool of content items to be recommended, gathered using the BlueConic Content Collector Connection. This connection collects data about your content and stores it in a BlueConic content store, which feeds personalization in BlueConic. See the CDP use case for delivering 1:1 content recommendations.

Q: How do I add content recommendations to emails?

A: Using the Open-Time Email Recommendations feature, you can deliver dynamic, individualized content or product recommendations via email based on up-to-the-minute customer data. These recommendations are updated the moment your customers open their email.

Q: I removed an article from my page. When will it be removed from the recommended items?

A: A content item will be added to a queue to be deleted when the required fields can no longer be scraped in the browser of the visitor, even if a visitor views the item -- for example, if the article has been deleted or if the publication date is no longer available.

Q: I cannot see item X in my personalization recommendations. What could cause this?

A: Items or articles are added to the recommendations queue when a customer or visitor views the item. Articles that have no views from customers or visitors won’t be added to the queue.

Items in the recommendations queue are evaluated and if the required fields become valid, those articles are added to the content store for personalization.

Q: How does Indexing work with personalized content recommendations?

A: Indexing items may take some time while when the collector is still collecting items Depending on how much traffic your channel has, there might be a short delay indexing content items when the collector is still actively collecting items. 

Q: When will my item be added to the queue to be evaluated?

A: Items are added to the queue for the first time when a visitor views an article. Also, when an item or content article has been clicked on twice, but no view follows the click, the system checks for all required fields and re-evaluates whether to include or delete the item.


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