Taboola Network Insights

My Role
User Experience Design, Information Design, User Interface Design

Taboola platform allows online content publishers to market digital content through a recommendation engine which is served via site widgets and feeds on publishers and websites across the web.

Network Insights allows publishers to compare articles to overall traffic in the Taboola Network and identifies topics that are interesting to readers. Network Insights spots emerging and waning interest in topics that informs the publisher which topics to cover.


Publishers have a suite of tools that help inform editors, writers, and social media managers to make decisions about their articles on a daily basis. Most of these suites do not provide a way to leverage a large dataset to identify topic trends that are emerging or waning in interest. Network Insights leverages the Taboola network to compare the reader's topic interest to traffic as a whole and identify the publisher's coverage and topic trends. Network Insights allows publishers to make decisions on short-term and long-term coverage.

Our goal for this project was open-ended since we were building something that was quite experimental but if presented correctly could prove to be useful. We started with a modest goal of giving the publisher something to look at in an easy to digest format and iterating as needed.

My role was to take Taboola's rich dataset and design a dashboard with appropriate data visualizations that made sense to the publisher.


Network Insights started as an internal idea, so it had to be tested and validated by publishers to determine if it was worth building. Through interviews with publishers and internal teams, we collected data through research to prioritize what publishers wanted out of this feature.

Publishers wanted:

  • To see what repeat visitors (renamed loyal readers) were interested in on their website and in general.
  • To see trends of loyal readers on the publisher's website and on aggregate.
  • To see a breakdown of categories and topics were interesting to loyal readers.
  • To see topics related to trending topics.
  • Actionable alerts based on Network Insights data.

With this data, I started thinking about the data visualizations that would best visualize the raw data. We also needed to consider power users — users who wanted to see the granular data and make their own decisions. The design required a hybrid of easy-to-digest visualizations and more tabular/excel data that could be sorted by columns and exported into its raw data form.

With all of this in mind, I designed the layout with a bird's eye view of the data at the top and the more detailed data at the bottom of the page in a sortable table. Loyal Readers is defined as a person who has visited the website twice within two weeks.

We alpha tested with a few publishers to validate our design and gather feedback. It went through several iterations to clear up confusion with the data visualizations.

Network insights v1
The first version of Network Insights we launched. Loyal Readers (top bar graphs) are defined as a repeat visitor if they visited the publisher's site more than once in a span of two weeks. Top categories compared the publisher's loyal readers to categories read across the Taboola Network. The table at the bottom is top topics that could be sorted by the publisher's coverage against a topic, which would be considered a coverage opportunity.
Network insights live
Network Insights Live - topic data visualization. In the second iteration, we added a topic view that was a visual representation of the topic table seen in the first iteration. The user can click on individual topics and get the high-level data of a topic (see panel at bottom left).
Network insights trend
Same topic view with topics trending up or down.
Network insights tv
Fullscreen/TV display - Some publishers have a large dataset. I designed this view with no website chrome (i.e., removed the layout, unnecessary UI elements.) also works for displaying on a large screen like a TV.