Traqli Personalization helps publishers deliver personalized recommendations to their audience via email and on websites. It recommends stories based on behavior and content preferences of each visitor.
In order to identify individual users interests Traqli performs semantic analysis for each content piece that have been published, so in other words we can identify the meaning of your content in an automated way.
Here is a list of languages you can start working with right away: arabic, armenian, basque, brazilian, bulgarian, catalan, cjk, czech, danish, dutch, english, finnish, french, galician, german, greek, hindi, hungarian, indonesian, irish, italian, latvian, lithuanian, norwegian, persian, portuguese, romanian, russian, sorani, spanish, swedish, turkish, thai, bulgarian, estonian, macedonian, polish, slovak, slovene, serbian, ukrainian, tamil, bengali.
Traqli currently supports over 40 languages and we are able to add support for many specific languages on-demand, so please send us a request if you didn't find your language in the list.
Traqli captures cookies of every visitor, so they get new stories accordingly to their behavior and content preferences. To make content recommendations accurate and relevant Traqli analyzes:
what kind of content visitors read;
how much time they spend on each article;
the meaning of the content visitors read (using semantic analysis engine);
groups behavior patterns;
the meaning of new content gathered through RSS feeds.
When we have all these information we can find the right conformity for every user and provide relevant content for her.
Leveraging Traqli’s personalization and content selection approaches you can combine any type of content (e.g. articles, products, advertising) and any type of recommendation approaches (personally recommended, trending or just latest) in your emails or onsite recommendations in a completely automated way.
Recommended content is the most relevant for each user and this type of content selection helps to increase audience engagement better comparing to other approaches. But at the same time the combination of most popular and personally recommended content has the best balance, since personally recommended stories provide the most interesting content related to recent user interests while top trending content has a great discovery function, so users can find something they never paid attention to before.
It’s important to mention that we send only unique and new content (not read by the user). There is rare case when the user can get recommended content that she already read, it can happen in case if user reads the content on a different device from the one she uses to read email (which means there is no connection on a cookie level between browsing history and email address). Also, in case of emails, Traqli finds new content for every subscriber from the moment of last sent email. When there is no user behavior data (when you upload existing data base) Traqli starts with most read content selection for unknown users, since this type of content provides higher engagement comparing to just latest stories selection, but once the user make a first click next email will contain personalized content selection.