Find Content Outliers with a Spreadsheet

A content outlier is a post that performs unusually strongly relative to a defined comparison group. One simple research method is to divide a post’s views by the median views in a comparable sample from the same creator. FeedRama can help collect and export the available post metrics; calculate this ratio in your spreadsheet. It is not a built-in FeedRama outlier score.

Collect a comparable sample before choosing winners

Start with a creator’s recent posts or a consistent time window, not only the most-viewed posts you already like. Record the account, range, collection date, and number retrieved. For Instagram views, use the Reels tab; for TikTok, use the profile’s video grid. The TikTok sorter helps review the resulting collection.

You can install FeedRama and use TikTok CSV export or Instagram CSV export when your account has export access. A views-sorted list is fine if it still contains the whole intended sample; exporting only its top five would bias the baseline.

Calculate a median-based view ratio

In a standard FeedRama CSV, Views is column E. Suppose rows 2 through 26 contain 25 comparable videos with verified numeric view counts. Put =MEDIAN(E2:E26) in K2, then put =IF(AND(ISNUMBER(E2),$K$2>0),E2/$K$2,"") in L2 and fill it down through L26.

Check every included value first. Missing metrics can appear as blanks or zero placeholders; the formula cannot tell those apart from genuine zero views. Exclude unavailable metrics explicitly and document exclusions. Do not mix photo posts without comparable view data into a video baseline.

These numbers illustrate the calculation. They are not measured campaign results. A 5× ratio means five times this sample’s median, not five times every creator’s expected performance.

What the ratio does and does not establish

The ratio helps you choose posts for closer inspection. It does not show why they performed well, whether the views were paid, or whether they produced sales. There is no universal threshold that makes a post “viral” across every account and niche.

The median is less sensitive than the mean to one very large value, but the sample still matters. A creator who recently changed topics may need separate baselines before and after the change. Newer posts also have less time to accumulate views. Document these limitations instead of treating the ratio as a precise prediction.

Compare the outlier with ordinary posts

Watch the standout and two or three posts near the median. Note the topic, opening promise, demonstration, length, and audience problem. Use hook analysis to capture how the opening works, rather than copying its wording.

Also inspect cover images if visual presentation is part of your question. A pattern shared by the standout and ordinary posts may not explain the difference. Write down an alternative explanation, such as a timely topic or collaboration, before deciding what to test.

Turn the analysis into a small experiment

A practical brief has five fields: source URL, observed pattern, proposed explanation, original concept, and success measure. Test one change at a time when feasible. Revisit your own results after a comparable observation period rather than judging a fresh upload against a month-old one.

Keep the exported sample unchanged and work in a copy. If you collect the account again later, save a second dated file; otherwise a changing baseline can make the earlier ratio impossible to reproduce.

Frequently asked questions