Did they actually read it?
View time and scroll depth are useful — but they never quite answer the question that matters most. Consumed does.
Numbers without context mislead you
Is 100 km/h fast? It depends entirely on whether you're on a motorway or a residential street. A number in isolation rarely means anything — only context makes it useful.
The same is true in publishing analytics. Take these two articles:
Which article performed better?
Two good metrics. One shared blind spot.
Scroll depth
Shows how far readers scroll — but not how they scrolled. A reader may rapidly scan to the bottom looking for related articles. Scroll depth alone can't tell you if they read a word.
View time
A reader spending 40 seconds on a 300-word article is impressive. The same 40 seconds on a 1200-word feature is actually quite poor. View time without knowing article length is close to meaningless.
Both metrics try to answer the same question: did the reader actually read the article?
At Kilkaya, we approach that question more directly.
Introducing Consumed
Instead of juggling several ambiguous numbers, Consumed asks one simple binary question: did the reader consume this article — yes or no?
Spent enough time relative to article length and scrolled at least 60% of the article.
Didn't meet either or both criteria — even if they spent some time or scrolled a bit.
Example: How to read Consumed
60% of readers spent enough time and scrolled far enough to genuinely engage with the article.
But is 60% actually good?
A 60% Consumed rate on a 300-word article is very different from 60% on a 2000-word investigative feature. Short articles are naturally easier to consume — in some cases, the first screen view alone puts 50% of the article in sight.
Kilkaya analyzed thousands of articles to understand the relationship between word count and Consumed rate — and built a formula that calculates what you should expect for any given article length.
Expected Consumed rate by article length
Illustrative values based on typical patterns — actual thresholds are calibrated to real data.
Meet Diff Consumed — the number that actually matters
The difference between actual and expected Consumed — Diff Consumed — tells you whether an article genuinely engaged readers beyond what you'd expect. It's the fairest way to compare a 200-word news brief with a 2000-word feature.
A model that evolves with you
Right now, Expected Consumed is based on article length alone — and that already creates a far fairer baseline. But the model is designed to go further. Additional factors can be layered in to reflect how your readers actually behave.
"The goal is not to lower the bar. The goal is to create a fair comparison that reveals which stories genuinely capture readers' attention — regardless of whether they are short updates or long, in-depth features."
Ready to measure what matters?
To use Consumed, Kilkaya needs the word count of each article. Many publishers already send this — contact us to check.
Reach out on Slack or email support@kilkaya.com