1. Aggregate public engagement
reactions + comments + shares
Nonli uses the public engagement counters available for every analyzed post on the selected network during the last 30 days.
Automatic comparison for publishers
Compare the engagement of two media accounts
Enter two Facebook pages or Instagram accounts. Nonli ranks their last 30 days by total engagements, then shows monthly engagement and video-view trends over 90 days and follower trends over 18 months.
Methodology reviewed:
Compare two media accounts automatically
Transparent methodology
Total engagement decides the result
Nonli retrieves public Facebook or Instagram aggregates for one identical UTC 30-day scoring period, monthly activity trends over 90 days and follower trends over 18 months. The account with the most total engagements wins; rates and trends explain the result without changing it.
2. Rank total editorial impact
winner = highest total engagements
Total engagements alone select the winner. Average engagement per post remains visible to show how publishing volume contributes to that total.
3. Add normalized context
average engagements ÷ followers × 100
Engagement rate shows how much engagement each average post generates relative to the current follower base, but it never changes the total-engagement winner.
A percentage is not a universal performance verdict
Network, audience size, editorial topic, format, distribution, period and the actions counted as engagement all change the result. A useful benchmark states every one of those choices instead of applying a generic good-or-bad threshold.
Why this public comparison uses followers rather than private reach data: Meta's official distinction between reach and impressions
How publishers should use the result
Use the head-to-head result
The comparator locks the network and 30-day window, then shows the absolute engagement lead so total editorial impact is immediately interpretable.
Separate distribution from resonance
Read total engagements beside engagement rate, video views, clicks, sessions, reach and publication volume so volume and efficiency are not confused.
Read trends before isolated posts
Use consistent weekly or monthly cohorts to distinguish repeatable editorial performance from one exceptional publication.
Keep the evidence visible
Nonli displays posts, total and average engagements, followers, video views, engagement rate and monthly trends for both accounts instead of returning an unexplained score.
Media engagement comparison FAQ
What counts as a social media engagement?
Reactions or likes, comments and shares are the most portable definition. Clicks, saves, private messages and platform-specific actions can be included only when the same data is available for every item being compared.
How does the automatic account comparison work?
Nonli compares public engagement, publication and video-view totals for both accounts over the same 30-day UTC window. The highest total engagement wins. Monthly engagement and video-view trends cover 90 days, while follower trends cover 18 months; none can alter the result.
Can engagement rate exceed 100%?
Yes. One person can perform several actions, and a post may reach people beyond its follower base. A rate above 100% is mathematically possible and should trigger a check of the formula and data scope, not automatic rejection.
Do I need to connect the Facebook or Instagram accounts?
No. Enter a public handle or profile URL. The website calls Nonli's API from the server, and the API credential is never exposed in your browser. The comparison works when Nonli has enough public posts for both accounts during the period.
What is a good engagement rate for a media publisher?
No single threshold is valid across publishers. Compare the same network, denominator, audience scale, content format, period and engagement definition, then relate the trend to traffic and editorial objectives.
Turn total impact into an editorial decision
Nonli connects engagement volume, video views, clicks, traffic and publishing choices so newsroom teams can understand what performed and improve the next distribution cycle.
See Nonli with your data