Social media competitor analysis worksheet
Compare supplied account observations within a common window. Build an honest peer report with visible coverage and unknowns.
See the number and its context.
Run the calculation to see the sample, formula and warnings. Missing observations remain unknown.
Choose peers for a reason
Compare accounts serving a similar audience and publishing a similar format. Keep platform, selected interactions, denominator, date window and measurement age aligned. Likes do not reveal a competitor’s private reach or demographics.
Our illustrative pottery fixture is your own-data teaching example. For a peer worksheet, add separate account values you are permitted to use. Self-reported observations are labeled as supplied data, not verified platform retrieval.
An annotated three-peer example
Illustrative Instagram peers, September 2026, likes + comments, 48-hour observations: A has 100 interactions / 1,000 followers = 10%; B has 200 / 5,000 = 4%; C has an unknown follower denominator, so its rate is unknown.
B has more raw interactions while A has a higher normalized rate. Neither observation identifies a cause. The three-peer CSV loads in this worksheet; replace the illustrative inputs with permitted observations. Missing values remain unknown.
A difference is a question to investigate
A peer’s higher observed rate can reflect topic, audience size, promotion, timing or sampling. It does not prove their format caused the difference. Rankings are withheld when metric/window definitions are incompatible.
Export the rows and report. Choose one feasible content variable, record a baseline, and revisit an equal follow-up window in the experiment journal.
Sources & review
Reviewed 2026-10-04. Examples are labeled illustrative; observed behavior and source limitations are recorded in the research ledger.
Related tools and guides
Analyze your own social data
Preview a supported CSV, review errors and mapping, then commit it to this browser’s private workspace.
Your social growth experiment journal
Save a hypothesis, one changed variable and a comparable baseline. Revisit the outcome without overstating what caused it.
Data sources & privacy
Know which observations you supplied, how they were normalized and what this release cannot see.