Personalised reading lists for maximum engagement
JAMES uses the power of machine learning to create personalised reading lists for your readers and increase overall engagement. Countless research and use cases have proven the positive correlation between engagement and retention and with JAMES you can leverage it to create the perfect onboarding experience and to turn your readers into loyal subscribers. Discover the benefits of the Launch Partner Programme and signup to join the next round of JAMES rollout.
Activate your readers for increased conversion rates
JAMES predicts which content is more likely to trigger readers’ interaction. Like a butler, JAMES learns from your readers’ behaviour and serves them with the right content to maximise engagement levels and turn your readers into loyal subscribers.
Measure and improve engagement
JAMES uses self-learning algorithms to increase engagement and reduce churn. JAMES measures daily its impact and iterates to obtain the best result. Your team can see the increased engagement with easy-to-understand dashboards.
Get in touch with our team and join the
Launch Partner Programme
JAMES can help your organisation serve your readers the content they love and increase your chances of retaining them. We are currently selecting 2 publishers for the third batch of our Launch Partner Programme closing in June 15th 2020. If interested in participating, fill out the form and our team will be in contact shortly.
JAMES easily integrates in your tech environment
JAMES integrates within your existent ecosystem and draws inputs from your content, analytics and email platforms. After identifying readers through cookies or subscription references, it learns what is the content that triggers them, and provides you every day with a set of recommendations per reader. This file can then be integrated in personalised newsletters, push notifications or web hooks.
JAMES has been named the best customer retention programme from the Data & Marketing Association thanks to its innovative approach to content recommendation for news products. This was achieved through a close collaboration with The Times and The Sunday Times which allowed for extreme focus on newspaper’s pain points.
JAMES – The Times Case
JAMES was born as a collaborative project between Twipe and The Times of London. After receiving Google DNI funding, a group of 22 people worked for a year in fine-tuning a product that could help accelerate digital subscriptions growth. Over 100.000 subscribers of The Times were served with individualised newsletters compiled from the content of a daily edition. Several optimisation algorithms were developed and tested to achieve the best results.
Throughout this one year project we learned that there is a high potential for the use of AI to create better customer experiences, by creating reader engagement with automatically generated emails. On selected cohorts, this has resulted in a 49% decrease in churn rate with the highest impact being observed on low to medium engaged subscribers. JAMES continues to be used and developed at The Times and The Sunday Times and is now being made available through the Twipe digital publishing SaaS platform. Download the report and get all the insights from the Times Case.
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- Twipe – JAMES, Your Digital Butler – How The Times & The Sunday Times are personalising news triggers
- Twipe – 49% reduction in churn observed with JAMES, Your Digital Butler
- Digiday – News UK used personalized newsletters to cut subscriber churn in half
- Press Gazette – Times titles halve digital subscriber churn with tailored emails from AI named ‘James’
- Journalism.co.uk – The Times employs an AI-powered ‘digital butler’ JAMES to serve personalised news
- Twipe – How The Times is achieving hyper-personalisation with “JAMES, Your Digital Butler”
- Twipe – Google DNI funds collaborative project of Twipe and The Times & The Sunday Times
- Twipe – Reuters names JAMES a development to watch for 2018
- Digiday – The Times of London turns to a ‘digital butler’ named James to increase subscriptions