CLIENT:
US daily newspaper and website publisher
OBJECTIVE:
A US daily newspaper and website publisher sought out a robust data-driven and reliable way to select paid subscriber-only content.
Traditionally, newsrooms hand-picked content for premium status through “gut feel”. Mather’s Premium Content Engine helps alleviate stress from the newsroom by reducing decision fatigue. PCE is a complementary component to editorial decision-making by organizing content intelligently, selecting articles for premium status through NLP, and maximizing subscriber LTV modeling.
Having an effective Premium Content Strategy can:
- increase the number of sales attempts to drive acquisition growth
- convert readers who may not convert under traditional metered paywalls
- reinforce the value of the publisher’s content to paying subscribers
APPROACH:
- The publisher sends a Slack alert to Mather indicating that an article is ready for scoring.
- Mather scrapes the publisher’s CMS for article metadata (e.g., section, author, etc.).
- Mather applies a scoring algorithm to determine if the article is a fit for premium.
- Mather pushes a recommendation to the publisher via a Slack alert.
- The publisher interprets the request and makes the final editorial decision.
RESULT:
- An estimated 3-year LTV incremental lift from subscriber revenue: $2.7M
- Increased premium conversions by 30.4%
- Increased conversions per article produced by 6.3%
- Increased premium production as a share of article production by 3%
- Estimated remaining revenue opportunity based on optimal share of premium content: $1.5M
More on Mather’s Premium Content Engine
- Download our latest Premium Content case study flyer
- View Article on Premium Content Models and Monetization Strategies – February 2022
- View Article “Impact from Subscriber-Only (Premium) Content” – October 2021
