Play Sports Network delivers personalised content to its users

Play Sports Network is the world’s largest cycling media company and community, reaching and engaging with more than 40 million cycling fans and riders around the world.

"To build a community around our brands, we need to understand our users and what they’re interested in, and then engage them through relevant content. Ancoris helped us to accelerate our work to build personalised feeds. In three months, we’ve been able to save a year of work and develop the infrastructure needed to support our plans for the next two years. What Ancoris has delivered is a total game changer for us!"

Javier Arias González

CTO, Play Sports Network

About the project

Play Sports Network (PSN) wanted to increase user engagement of their Global Cycling Network (GCN) social media app, allowing like-minded cycling enthusiasts to upload, share, comment on and consume content.

Ancoris was approached by PSN with a complex machine-learning problem, to develop a collaborative based recommendation engine to deliver personalised content feeds to users of the GCN app. Working closely with PSN’s data science team, Ancoris data and analytics practice delivered a leading-edge matrix factorisation model, trained using BigQuery ML. Ancoris also delivered reusable, high-value data assets, including a Single Customer View (SCV) and an engagement scoring algorithm that PSN will continue to leverage in future optimization.

Then

Users of the GCN app were all being shown the same content, with only limited personalisation based on user characteristics such as the user’s language.
Personalisation was further complicated because users didn’t have a unique user ID across PSN channels or even within the GCN app. Their activity may be recorded under different user IDs, according to whether they’re logged in or out of their account on the app, or if they switch device or network.
PSN’s in-house data science team had begun building a content-based recommendation engine that would filter content based on selections made by each user. They quickly discovered the high-complexity nature of the problem* and turned to external support to help accelerate their progress and fully leverage the capabilities of the Google Cloud Platform.

Now

PSN has a leading-edge recommendation engine to serve highly engaging content. The solution includes:
- The very latest machine learning technology via BigQuery ML
- Hyper-scalable storage via BigQuery; anticipated to scale to over a trillion rows in the matrix output table alone
- dbt (data build tool) for easy productionisation of the ML pipeline using SQL
- A blueprint for a modern data analytics platform that they can take forward for future use-cases
A Single Customer View (SCV) that consolidates different user IDs belonging to the same individual across all channels. The recommendations generated by both the content-based and the collaborative engines are more accurate. Reporting at marketing department and board level has also improved in accuracy.
An engagement scoring algorithm that uses a stream of activity data from users’ phones to provide an accurate assessment of how engaging users found each piece of content they interacted with.
Up to one year of work saved over the course of the three-month project. The combination of the technical infrastructure and the coaching and mentoring delivered throughout the project has set the PSN team up to now carry the work forward independently for the next two years.

Read the blog post by David Taylor, Technical Lead at Play Sports Network

"We chose Google’s BigQuery, but we found ourselves running into performance issues and our model was taking too long to run. We realised we needed help in figuring out how to make effective use of BigQuery, as well as other features in the Google Cloud Platform, to productionise the solution."

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