Competitively actionable analytics at The Tab

Competitively actionable analytics was a core element of the technology and product strategy at The Tab. The below details how this approach has translated into product and how that went on to affect user behaviour. The Tab had built a set of micro services to data mine Facebook, WordPress and Google Analytics which created a rich dataset in their data warehouse. This was of little value until it was in front of contributors to drive their learning and retention from competitively actionable analytics data.

The Tab Team

The Tab’s Team analytics platform was created with a main goal of providing actionable analytics data in a competitive framework for individuals and teams. The product aimed to get authors excited about how individual efforts had contributed to their teams overall success. What you see in red is an authors impact on the team performance. Authors are given a clear overview of how each of their recent stories has performed. At the highest level an overview of the top performers is presented as a leaderboard broken down by country and then team, author and story allowing multiple layers of competition and learning across our network.

Author Impact in Team Dashboard

Author Impact in Team Dashboard

Team, Reporter and Story Leaderboards

Team, Reporter and Story Leaderboards

Personal

Personal Dashboard

Previous to this project data access was restricted to a small set of users with a Google Analytics login and a high level understanding of custom filters. WordPress also had some stats but only at a team level and they were reasonably hidden within the dashboard.

Take the data to the people

The initial launch was a web based front end, the next step was an upgrade of the notification system to be able to send detailed data summaries via email. Distributing data via email increased users to Team by 200% with close to 100% of our active authors logging in monthly. These emails have been able to maintain a 65% open rate and over 10% click through rate after being sent thousands of times.

Weekly Stats Email

Weekly Stats Email

Monthly Stats Email

Monthly Stats Email

Usage growth after email prompt (1st Feb)

Usage growth after email

Team Retention

Team Retention

Data everywhere

Analytics are a natural form of gamification and authors were constantly posting screenshots in Facebook groups or Snapchats. To further enable view ability and use stats were embedded on every page of the site for logged in users. The goal was to show as much data as possible within the visual interface that was being used daily. A completely open approach to numbers allowed authors to learn from each others success, there are no restrictions in who can see what data once someone is logged in.

Homepage Stats (Logged In)

Homepage Stats (Logged In)

Story Stats (Logged In)

Story Stats (Logged In)

Author Stats (Public)

Author Stats (Public)

Real time notifications

Team has been running successfully since November 2015 delivering a clear path to re-engagement and a core part of The Tab’s retention strategy. Since then notifications have been extended to be able to deliver realtime readers stats as well as historical page views. These notifications are delivered by an iOS app The Tab + as well as Facebook Messenger. Push notifications enabled The Tab + to have a very similar level of use and retention to Team. The number of interactions with users has increased as the data is broken down into multiple smaller messages and sent as soon as thresholds are reached. This helped authors to stay excited about their existing story for longer and the more they enjoy process the more likely they are to repeat it.

The Tab +

The Tab +

Messenger Push Notification

Messenger Push Notification

Tabitha Messenger Interface

Tabitha Messenger Interface

The Tab + Consistent Engagement

The Tab + Consistent Engagement

The Tab + Retention

The Tab + Retention

Actionable analytics for editorial decision making

In a bid to take the data to where editors gather a Slack bot was built to enable access to detailed Google and Facebook data just by pasting in a url. Slack channels were created where real time publish and stats updates allowed editors to make decisions about what to promote across our network. Previously editors had to log into multiple systems to gather this information and spend a lot of time filtering. Decisions are now able to be made and discussed in near realtime.

Tabitha Slack Story Stats

Tabitha Slack Story Stats

Tabitha Slack Channel to Promote Post

Tabitha Slack Channel to Promote Post

Big Screen Dashboards

With authors being served with data in a competitively actionable analytics framework editors within the office were the next to be targetted. Utilising Geckoboard, custom API’s and Google Sheets The Tab designed big screen dashboards to drive competition and growth centrally.

UK Editorial Dashboard

UK Editorial Dashboard

US Editorial Dashboard

US Editorial Dashboard

Global Sharing Dashboard

Global Sharing Dashboard

It was a lot of effort to get the data in the right place and then expose it in creative ways to drive learning and performance but now competitively actionable analytics has permeated into The Tab’s culture there is no going back.

The geeky bit

Millions of rows of data a day are processed using Node.js, Amazon Simple Queue Service, Elastic Beanstalk Workers and MySQL running on RDS. The data is cleaned and then transformed into day, month and year increments as well as aggregated for users and teams. This preprocessing allows very fast recall of any data set. Amazon API gateway using a Node.js Lambda based server less API layer is used for data retrieval. This handles a lot of the standard API paradigms like security and caching which kept the focus on data and user experience. The front end was built in React.js utilising Chart.js for graphing.

The team

Big up to Richard Coombes for helping with the backend magic, Matteo Gildone for helping with the frontend magic, Serge Bondarevsky for design and Charlie Gardner-Hill for the dashboards. My focus was on the product management, data collection, data transformation and building the API layer.

Leave a Reply

%d bloggers like this: