① Enter Your Content's Performance Parameters
What is Content Depreciation?
Unlike physical assets, digital content does not wear out mechanically, but its value can decline over time due to factors like:
- Competition: Newer, more relevant content emerges.
- Algorithm changes: Search engines or platform feeds shift.
- Outdated information: Statistics, methods, or references become obsolete.
- Audience fatigue: The same content stops generating clicks.
However, evergreen content—topics that remain relevant indefinitely—can maintain value if updated periodically. This tool helps you model the financial lifecycle of a piece of content, factoring in both natural decay and the positive effect of updates (refresh, repromotion).
Use this to: Compare expected returns from different content types, decide when to invest in updates, and forecast your content portfolio's total value.
Strategies to Reduce Content Depreciation
Regular Updates
Refresh statistics, add new sections, or update examples. Even small updates can signal freshness to algorithms and audiences.
Internal Linking
Link to your evergreen content from new posts. This passes authority and keeps it in front of readers.
Periodic Repromotion
Share content again on social media, newsletters, or in roundups. Each repromotion can create a spike in traffic.
Repurpose Formats
Turn a blog post into a video, podcast, or infographic. New formats tap into different audiences and extend lifespan.
Frequently Asked Questions
It varies widely. High-quality, highly competitive niches may see 3-8% monthly decay. Less competitive topics can maintain 0-3% decay for years. Use your own analytics to estimate.
Enter the update cost and a growth factor (e.g., 10% monthly bump for 3 months). Compare total earnings with and without the update. If the increase exceeds the cost, it's worthwhile.
Yes. By modeling different scenarios, you can decide which content types to prioritize and how often to update them for maximum long-term ROI.
This model assumes consistent decay; seasonal content would need a more complex model. For evergreen topics, this is a solid starting point.