Subscription numbers you can trace back
Based on a regional media house: €42M revenue, 55,000 print and 25,000 digital subscriptions, 900,000 unique users per month. Every figure is derived from the documented assumptions - the levers come from published industry benchmarks.
Each module solves a concrete subscription problem
Click a module for the full use case with visualisations, transparent model calculation and business impact.
The subscription growth simulator
Set the sliders to your scale: the simulator projects the digital subscription base over 24 months - once with your current figures, once with the levers of the six modules.
Model calculation, not a forecast: new subscriptions per month = paywall contacts × conversion; paywall contacts are set at 6 times the subscription base. The module levers match the assumptions derived in the modules: cancellation rate -20% relative (Modules 01 and 05), paywall conversion +30% (Module 02).
No theory - results in weeks
We work with the data you already have. No vendor lock-in, no cloud mandate, no hidden costs.
The following six modules use a fictitious regional media house (55,000 print and 25,000 digital subscriptions, €42M revenue) to show how data science can be applied across the subscription business - from the dynamic paywall to winback.
All euro figures are model calculations. They are derived transparently from clearly named assumptions (assumption baseline → lever → result). The levers follow published industry benchmarks (including Mather Economics, INMA, FT Strategies, Piano) and are deliberately conservative - no promises.
In a real project, knowledge of your house is the decisive factor: How is your paywall configured today? What data sits in the subscription system, CRM and web analytics? Where do you lose subscribers - at the start, in the base or on price? On that basis we build models that fit your reality.
This portfolio shows which questions data science can answer in publishing - the concrete answers emerge only with your data.