To create personalized content recommendations in BlueConic dialogues, you start by collecting a content store, which is a pool of content to be recommended, 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.
Make smarter content recommendations
You can use the content items collected through the connection in BlueConic dialogues to make smarter personalized content recommendations. BlueConic recommendations are powered by algorithms and filters you set in the content editor toolbar.
Configure the Content Collector
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.
In the metadata section at the top of the page, you can choose whether to get email notifications when the connection runs or fails to run.
To set up the connection, select "Collect data from your channels" in the lefthand panel.
1. Select the channel(s) to collect content data from.
Optionally, define URL rules to specify which areas of the sites to collect content data from.
2. Manage which data is collected.
Next, paste an article URL into the Test URL field, and click 'Test' to review the metadata that would be collected for your content:
BlueConic requires a number of metadata fields out of the box. Select the checkbox for other metadata fields that are also required.
If required metadata fields are not populated, the webpage may not surface content information in a supported format. The default BlueConic selector will automatically detect:
- Schema.org Microdata or JSON-LD
- Open Graph meta tags
- Regular meta tags
Providing metadata in one of these standard ways is not only good for the content collector, it is good for Google, Facebook, Twitter, and countless other platforms, because it allows these platforms to understand your content.
Click Add data field to add custom metadata fields. For example, if your content has a sponsorship association, or is tagged based on overarching stories or topics, influencers, or sources, you can use this data in recommendation placement filtering.
3. Set the algorithm time frame.
Some recommendation algorithms are based on a look-back time frame (for example, Viral articles or recent high CTR). You can configure that time frame here, based on hours or days:
For details on how each recommendation algorithm operates, see BlueConic recommendation algorithms.
4. Set request headers (optional).
To enable content recommendations to be collected from webpages that are under development or behind a login or paywall, you can add HTTP request headers here. Click "Add request header," choose a channel, and add the custom header name and value used to access the content.
Content personalization tips:
- Make sure images are being scraped properly.
- Review article names to be sure appendages do not exist, for example " | Site.com" may be appended to every title depending where it is being scraped from.