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:
Same metrics — but Article A (300 words) likely performed much better than Article B (600 words).
Context changes everything. That's exactly why Consumed exists.
Two good metrics. One shared blind spot.
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.
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?
Relative to article length
At least 60% of the article
One clear number: did the reader actually read it?
Spent enough time relative to article length and scrolled at least 60% of the article.
Did not meet either or both criteria even if they spent some time or scrolled a bit.
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.
Consumed
How many readers actually consumed the article
Expected Consumed
What we would expect given the article's length
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.
More than 25% above
15–25% above expected
5–15% above expected
Within 5% of expected
5–15% below expected
More than 25% below
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.
Sports vs. politics vs. culture. Reading patterns vary by section.
Paying readers typically engage differently than anonymous visitors.
A breaking news brief has different expectations than a long feature.
Any signal your CMS provides can be incorporated into the model.
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.