Blog strategy
Social media competitor analysis: read public evidence
A social media competitor analysis compares what several brands publish under the same conditions so you can decide what to test on your own account. Collecting screenshots is only the start. Keep the evidence, explain what you think it means, and separate that interpretation from a hypothesis.
Start with a decision
Write down the decision you need to make before opening any profiles. “What topics and scenes could help us explain our service more clearly?” gives you a direction. “Which brand is winning?” asks for a ranking that public data rarely supports.
Choose accounts that compete for a similar need or audience attention. A brand selling the same product may be a direct competitor. An account in another category may still be a useful editorial reference if it answers a similar question. Record which relationship applies and why you included it.
Limit the review to one social network and one question. When you mix channels, formats, and metrics, each number can describe something different. You can take on a second question in a later review.
Decide what counts as evidence
Set the platform, dates, accounts, and post-selection rule before you collect anything. For example, you could review the public Reels on each profile during the same three-week window. Record when you viewed each post, its link, format, topic, message, and call to action.
If the platform displays public counters, copy the exact label, value, and observation date. “Unavailable” describes a value you could not observe. Zero describes a measurement with a value of zero; keep those meanings separate. Include posts that do not fit your first impression so one unusual result does not decide the analysis.
| Record | Comparable evidence | Reading limit |
|---|---|---|
| Content | Topic, format, scene, promise, and visible action from posts on the same channel and dates. | It describes what the account published; it does not reveal why. |
| Cadence | Publication dates and one shared rule for counting included posts. | A short window does not represent a full year or an entire strategy. |
| Visible interactions | Public counters with their label, value, and observation date. | They do not show who saw the post or how many accounts it reached. |
| Missing information | Mark a field unavailable when you could not observe it. | A missing counter is different from a measured zero. |
| Advertising | Record a paid post only when you have direct, dated evidence. | Not finding an ad in your sample does not prove that the brand is not advertising. |
| Business results | Use your own business data and an attribution source you control. | Another brand’s public posts do not prove its sales or return. |
Swipe the table to see all columns.
Followers, views, comments, and reach describe different units. Keep each measure with its name and source. A profile count does not show how many accounts received a particular post; visible replies do not establish purchases or revenue on their own.
Separate observation, interpretation, and hypothesis
An observation states what you can point to in a post or a dated record. An interpretation suggests a pattern that might explain those observations. A hypothesis describes a test on your own account. Keeping these as separate fields lets your team see where the evidence ends.
The wording and counts in this table preview a fictional case; they do not describe real accounts.
| Layer | Question | Sample wording |
|---|---|---|
| Observation | What appears in the posts you reviewed? | In the fictional case, four of six Reels show the product in a use setting. |
| Interpretation | What pattern might that evidence suggest? | The sample spends time showing when the product could be useful. |
| Hypothesis | What decision do we want to check on our account? | A use scene may help our audience understand when to choose each product. |
| Action | What will we change, and when will we review our evidence? | Publish one original post with a use scene, then review available responses and metrics at the end of the agreed period. |
Swipe the table to see all columns.
An interpretation offers one possible reading of another brand’s decisions. A hypothesis states what you want to check on your own account; the result remains open until you measure it. Decide in advance which signal you will check on your own channels, and note other factors that could influence it, such as topic, distribution, or timing.
Fictional example: two bakeries
Patio Bread and Grain & Salt are fictional brands. This example compares six public Instagram Reels from each account, published between September 1 and September 21, 2026. It is a practice sample for recording content, not a report of real Instagram results.
In the example, five of Patio Bread’s six Reels show finished bread, while one includes a serving scene. Four of Grain & Salt’s six show an occasion for using the product; the other two focus on product presentation. These counts describe a fictional sample. The file does not simulate reach, impressions, views, or interactions.
Across six reviewed posts per account, Grain & Salt shows more use scenes. The descriptions and URLs in this case are fictional.
A post about choosing bread for breakfast could answer a common question. The team can test the idea and review its own data.
The downloadable sheet has one row per Reel and a case summary. Each record labels the scene as a fictional observation, marks metrics unavailable, and uses example.com URLs as placeholders. They do not represent real accounts or posts.
Download the observation log as a CSV. Replace the sample rows with posts you can verify, and keep the observation date beside every URL.
Turn the idea into a test for your brand
Choose one variable you can change and one signal your team can observe in its own data. That could be a relevant question, a click with a known source, saves per reach when reach is available for your account, or an inquiry arriving through a measurable channel. Set the period and decision rule before publishing.
One of your own posts with a different outcome can help you decide what to explore next. Topic, distribution, or timing may also have played a part. To examine those factors, follow the process in testing social media content. If you still need to organize your goals and channels, start with a social media strategy. A social media audit can help you review your profiles and available measurement first.
Analyze public evidence without filling in missing data
Keep the limits beside each finding
A public review can show topics, formats, and visible conversations during a defined window. It cannot show another account’s private reach, audience makeup, sales, or return. Activity missing from your sample does not prove that a campaign or post does not exist.
The definition, matrix, case, and CSV are HeyMark editorial work. The example’s names, dates, descriptions, and post counts are fictional. The CSV leaves reach, impressions, views, and interactions unavailable; it does not simulate those results. End with your own decision, the evidence behind it, and a date to review what happens on your account.
Plan your next post in HeyMark.
Keep the idea, review the draft with your team, and see how it performed in the accounts you connected.