“We should try something like this.”
Everyone in social media marketing has probably read this sentence. Someone sends a competitor’s post in your team chat, and suddenly it’s a reference for next month’s content.
It might be a good idea for short-term posting. But before adding it to the calendar, you need to breakdown the strategy and check whether this is something their audience consistently responds to, or are you looking at an exception?
And to answer those questions, you reach the stage of scrolling. Going through profiles of multiple competitors You compare engagement patterns, examine the content strategy, and work out what’s worth testing with your own audience. All these steps are part of a useful social media competitor analysis process.
In this guide, we’ll walk through that process, from choosing competitors and tracking the right metrics to turning your findings into a content plan. You’ll find worked examples and a template to organize your research.
Once you understand the process of social media competitor analysis, we’ll show how you can automate this process with Vaizle agents, so your next competitor review builds on the work you’ve already done.
Social media competitor analysis is the process of comparing your brand’s social content, publishing activity, positioning, and available performance metrics with competing accounts. It helps you establish benchmarks, understand competitors’ approaches, and identify opportunities to improve your own strategy. You may also see it called social media competitive analysis.
The finished analysis should give you three useful outputs:
For example, “Competitor A publishes more videos” is an observation.
“Competitor A repeatedly publishes short demonstrations that answer customer setup questions, and those posts outperform its general announcements” gives you something more specific to investigate.
Your job is to move from the first kind of statement to the second, without assuming you know more than the evidence shows.
Start with information that helps you make a decision. A spreadsheet with 40 metrics is not automatically more useful than one with eight well-chosen fields.
Use these categories to organize your research.
| Area | What to record | What it helps you investigate |
|---|---|---|
| Audience | Current follower count and historical follower counts when available | Which accounts are growing and how their audience sizes compare |
| Publishing activity | Post count, posting frequency, consistency, and content-format mix | How competitors distribute their publishing effort |
| Visible engagement | Available likes, reactions, comments, shares, or views, depending on the platform and source | Which posts receive a response and whether that response repeats |
| Content and creative | Topics, hooks, formats, visuals, recurring series, and calls to action | How competitors package ideas and which combinations deserve closer attention |
| Positioning | Audience addressed, problems emphasized, promises, proof, and offers | How each competitor wants people to understand its brand |
| Community | Relevant public questions, discussion themes, and brand responses | What people ask about and where content could provide better answers |
Treat this as your research framework, not a promise that every platform exposes every field. For example, Meta’s Instagram Business Discovery documentation describes access to selected public account and media information, rather than the complete analytics of another account.
Your competitor comparison and your internal performance report serve different purposes.
For Instagram, public account and post information is different from the account and content analytics available through Instagram Insights. A competitor’s public profile is not access to its private reporting.
In practice, organize your information like this:
| Information | How to handle it |
|---|---|
| Public posts, captions, visible interaction counts, and follower snapshots | Record what your chosen source actually provides |
| Historical follower growth | Calculate it only when you have comparable earlier and later snapshots |
| Private analytics, such as another account’s reach or saves | Mark them as unavailable unless the account owner has explicitly supplied them |
| Competitor clicks, conversions, revenue, or return on ad spend | Keep them out of the comparison unless you have a credible, disclosed source |
| Your own account’s richer analytics | Use them separately to assess the results of your content experiments |
This distinction matters when calculating engagement. Comparing your full interaction total with a competitor’s likes and comments would give you an uneven comparison.
For the shared benchmark, use metrics available consistently across the accounts. Then use your own social media metrics to investigate business results in more detail.
The process is straightforward: choose the decision you need to make, select relevant competitors, collect comparable information, benchmark performance, examine content patterns, review positioning, and define your next tests.
The care you put into the comparisons matters more than the number of accounts you include.
Start by completing this sentence:
We are analyzing competitors so we can decide __________.
“Improve our social media” is too broad. “Choose two content series to test next month” gives the research a clear purpose.
Other useful objectives include identifying topics for a new audience, understanding how competitors explain their products, or deciding whether your publishing mix needs to change.
Your objective determines what deserves attention. A positioning review needs close reading of messages and offers. A performance benchmark needs consistent metrics and a well-defined reporting period.
Running example: Imagine you manage Instagram for a project-management software company that serves small marketing agencies.
Your objective is to identify practical content ideas for agency owners, rather than fill another month with feature announcements. You will compare your account with three relevant software brands.
The accounts, post details, and performance figures used in this guide are illustrative. They are teaching examples, not industry benchmarks or Vaizle customer results.
Your strongest business competitor is not necessarily your most useful social media benchmark.
Build a shortlist that accounts for both commercial competition and competition for attention.
Direct competitors sell a similar solution to a similar audience. For the project-management example, these would include other products targeting small agencies.
Indirect competitors solve the same underlying problem differently. An agency might use an operations consultant or a spreadsheet-based planning system instead of dedicated software.
Content competitors publish for the same audience without necessarily selling a competing product. A creator who teaches agency owners how to manage client relationships could be highly relevant to your content research.
You can also study an aspirational brand, but keep it separate from your core performance benchmark. Its scale, audience, and resources may make it more useful for creative inspiration than numerical comparison.
Search for the topics your audience cares about.
In our example, that could include “client onboarding,” “agency project management,” “client approvals,” and “scope creep.” Review relevant social search results, public discussions, and accounts your customers mention.
Then check whether each account actually addresses your audience. A large productivity page aimed at students may have impressive engagement but little relevance to agency owners.
For a first analysis, start with three to five core competitors. Record why you included each one, the audience it appears to address, and the platform you will compare.
Repeat the selection process for another platform when necessary. You can study the same brands across channels, but their relevance and publishing activity may differ.
Choose a shared reporting period before collecting numbers.
A completed 30-day period is a practical starting point. Extend it when accounts publish too infrequently to give you enough examples, while keeping the period consistent across the comparison.
You can collect information manually from public profiles and posts, or use a tool that supports the competitor sources you need. For a focused Instagram comparison, our free Instagram profile analyzer tool provides a starting point for reviewing publishing activity and visible engagement.
Whichever method you choose, record the platform, reporting period, collection date, source, and metric definitions.
Suppose you select posts published during a 30-day period and collect their interaction counts a week later.
You have measured the visible interactions on posts published during that period, as observed on your collection date.
You have not necessarily measured interactions that happened only within those 30 days.
That distinction becomes especially important when you compare your own analytics export with public competitor counts. Check whether both datasets describe the same thing.
A post published yesterday has had less time to accumulate responses than one published several weeks ago.
For a retrospective review, record post dates and avoid judging very new posts alongside older ones without acknowledging the difference. Collecting the snapshot after the reporting period closes gives recent posts more time, but their ages will still vary.
For a more controlled ongoing comparison, capture each post’s results at the same age, such as seven days after publication. That requires a collection process that records those snapshots.
A hidden or unavailable count is not zero.
If you can collect complete interaction data for only 10 of an account’s 12 posts, record that coverage. Calculate averages using the 10 posts with complete information and label the result accordingly.
Likewise, a current follower count cannot reveal last month’s follower growth. You need an earlier observation or a source that already collected it.
These details may feel administrative, but they prevent a report from looking precise while comparing different things.
Start with three questions:
How much content did each account publish? What response did a typical post receive? How consistent were those results?
Follower count provides context, but it cannot answer those questions by itself.
For this guide’s Instagram examples, we will use visible likes plus comments as the shared interaction measure.
Use the same definition for every account, including your own.
Average interactions per post = Total measured interactions ÷ Number of posts with complete data
Consider this illustrative comparison:
| Metric | Competitor A | Competitor B |
|---|---|---|
| Posts published | 24 | 8 |
| Total likes and comments | 2,400 | 1,600 |
| Average likes and comments per post | 100 | 200 |
Competitor A generated more interactions overall. Competitor B generated twice as many interactions per post.
Both statements are correct. Neither proves that publishing less produces better results.
You still need to examine the individual posts.
The median is the middle value when you arrange your results from lowest to highest. With an even number of posts, it is the average of the two middle values.
Suppose Competitor B’s eight posts received these interaction counts:
30, 40, 45, 50, 50, 60, 75, and 1,250
The average is 200. The median is 50.
One exceptional post generated 1,250 of the account’s 1,600 interactions. The remaining seven posts averaged just 50 interactions each.
The exceptional post is still worth studying. But the median stops you from treating that result as the account’s normal performance.
Report the average and median together when you can. One shows the arithmetic average; the other helps you inspect the center of the results.
For a follower-based comparison, you can calculate:
Engagement rate by followers = Average interactions per post ÷ Reference follower count × 100
If a hypothetical account averages 100 likes and comments per post and has 10,000 followers at the chosen reference date:
100 ÷ 10,000 × 100 = 1%
Record which follower snapshot you used, such as the count on your collection date. Apply the same rule across accounts.
This is a normalized interaction measure, not the percentage of followers who engaged. The numerator counts actions rather than unique people, and it does not identify whether each interaction came from a follower.
Also keep follower-based rates separate from rates calculated using reach, impressions, or views. Changing the denominator changes the meaning of the result.
When you have comparable follower snapshots:
Follower growth rate = (Ending followers − Starting followers) ÷ Starting followers × 100
Use growth as a reason to investigate an account, not as proof of the cause.
A follower increase and a new content series may happen together. That alone cannot establish that the series caused the increase.
Your benchmark should tell you where to look next, rather than declare a winner from one metric.
Once you understand account-level performance, move to the posts themselves.
Start with stronger posts, but include ordinary and weaker posts too. Otherwise, you have no way to check whether the characteristics of a successful post also appear in content that received little response.
Create a small set of consistent labels.
| Element | What to record | Illustrative example |
|---|---|---|
| Format | How the post is presented | Carousel |
| Topic | The main subject | Client onboarding |
| Hook | The opening idea that earns attention | A common approval mistake |
| Visual approach | How the information appears | Annotated workflow screenshots |
| Proof | What supports the message | A worked project example |
| Call to action | What the post asks people to do | Save the checklist |
| Special context | Anything that may affect interpretation | Collaboration, giveaway, launch, or promotion |
Keep the labels specific enough to be useful. “Educational” is a broad category. “A step-by-step client onboarding checklist” tells you much more.
Choose one primary topic for each post and add secondary tags when necessary. This makes your comparisons easier to repeat.
Suppose a competitor’s educational carousels receive more engagement than its promotional images.
There are several possible explanations. The topic may be more relevant, the format may explain it better, or the hook may be stronger.
You have identified a promising combination, but you have not isolated the reason it worked.
Look for comparisons that help narrow the explanation. How do educational images perform? How do promotional carousels perform? Does the same topic receive a response in more than one format?
Small samples will not answer everything. They can still give you a better starting point for an experiment.
A useful finding should describe more than a format.
For example:
Four of the account’s six stronger posts explain a specific client-onboarding problem, show the process visually, and finish with a practical checklist.
That suggests a content approach you could adapt.
By contrast, “Their carousels perform well” leaves your team to guess the subject, opening, structure, and intended audience.
For video research, watch the actual video when assessing its opening, pacing, demonstration, or payoff. A caption and view count are not enough to describe those elements.
The goal is to understand the content well enough to create your own relevant interpretation, rather than reproduce a competitor’s post.
Performance tells you where the response happened. Messaging analysis helps you understand what the brand was trying to communicate.
Read the account description, pinned content, recurring posts, and relevant landing pages together.
Look for the main audience, the problem the brand emphasizes, the outcome it promises, and the proof it offers.
For our agency-software example, one competitor might focus on saving time. Another might emphasize protecting project profitability. A third might center its message on smoother client communication.
Those are different positioning choices, even when the products have overlapping features.
Review relevant public comments for questions that could inform your content.
Suppose people repeatedly ask how a demonstrated workflow handles external clients. That gives you a possible topic to investigate.
Record the posts and comments supporting the observation. Also describe your sample, such as the available comments on five relevant posts, rather than presenting a small selection as the opinion of the entire audience.
Look at the nature of the response as well as the volume. Questions about implementation tell you something different from giveaway entries, complaints, or short expressions of approval.
Public advertising can help you compare the offers and messages competitors emphasize beyond their regular posts. Meta’s Ad Library provides a way to search active ads across its products.
Review the visible promise, creative, offer, call to action, and destination page.
For example, a competitor might teach client-management techniques in its social posts while promoting a downloadable onboarding resource in its ads. That creates a possible connection between its educational content and its acquisition offer.
Keep that as an observation about messaging. The existence or longevity of an ad does not, by itself, establish profitability.
A topic missing from competitors’ content might be overlooked. It might also have little relevance to the audience.
Check the gap against your own customer questions, sales conversations, search information, and product expertise.
The stronger opportunity is where an audience need, limited useful coverage, and your ability to help overlap.
Your analysis should end with decisions.
Use this sequence:
Evidence → Possible explanation → Test → Success measure
Here is an illustrative example.
| Evidence | Possible explanation | Test | Success measure |
|---|---|---|---|
| Several competitor posts explaining client onboarding outperform that account’s general product announcements | Agency owners may respond to specific implementation help | Publish four original onboarding tutorials over four weeks | Compare saves relative to reach and relevant audience questions with your own comparable educational posts |
The evidence supports trying the idea. It does not guarantee the result.
Choose experiments based on audience relevance, strength of evidence, and production effort. Two well-supported tests are easier to learn from than ten unrelated changes.
For educational content, you might assess saves relative to reach and the quality of follow-up questions, using your own account analytics.
For content intended to generate enquiries, look at attributable enquiries and their relevance. A larger interaction count alone cannot establish that the content attracted better prospects.
Instagram Insights provides account and content performance information for your own analysis; use the fields available to you rather than assuming the same information is available for competitors.
Assign an owner, record the intended publishing schedule, and choose the review date before launching.
Measure posts at comparable ages. If your rule is to review seven-day results, the final review needs to happen after the last test post has had seven days to accumulate them.
Treat four posts as an initial test, not a final verdict. Record whether the result was encouraging, inconclusive, or weaker than your baseline, then decide whether to repeat, refine, or stop.
This is where competitor research becomes part of your social media strategy, rather than a report that sits beside it.
Let’s bring the process together for the fictional project-management software company.
The goal is to choose useful content for agency owners. We compare the brand and three relevant competitors on Instagram over the same 30-day publication period.
All figures and content observations in this example are illustrative. Interaction counts represent likes plus comments observed at the shared collection time, with complete data for every included post.
| Metric | Your brand | Competitor A | Competitor B | Competitor C |
|---|---|---|---|---|
| Followers at reference date | 10,000 | 20,000 | 8,000 | 12,000 |
| Posts published | 12 | 24 | 8 | 12 |
| Total likes and comments | 1,200 | 2,400 | 1,600 | 1,800 |
| Average interactions per post | 100 | 100 | 200 | 150 |
| Median interactions per post | 96 | 92 | 50 | 146 |
| Follower-based engagement rate | 1.00% | 0.50% | 2.50% | 1.25% |
Competitor A generated twice your total interactions, but it also published twice as often. Its average and median provide little evidence that a typical post receives a stronger response than yours.
Competitor B has the highest average and follower-based rate. As the earlier example showed, one exceptional post accounts for most of its interactions.
Competitor C deserves closer examination. It published the same number of posts as your brand and recorded stronger average and median results.
That is a reason to inspect its content, not yet a reason to copy its approach.
In this illustrative dataset, Competitor C published:
| Content group | Number of posts | Median likes and comments |
|---|---|---|
| Educational carousels | 6 | 185 |
| Promotional images | 4 | 115 |
| Founder-led posts | 2 | 136 |
The educational carousels explained practical agency workflows. Four addressed client onboarding, using examples and checklists.
This gives you a more useful lead than the account’s overall engagement rate.
However, the comparison combines differences in topic, format, and presentation. It supports testing that combination, rather than claiming that carousels caused the stronger response.
Keep your existing publishing frequency for the first test. Replace some generic feature announcements with four onboarding tutorials and two practical workflow demonstrations.
For the tutorials, show the process clearly enough that an agency owner could use it. For the demonstrations, connect the product feature to a recognizable client-management problem.
Review the tutorials against your own educational-content baseline. Assess the demonstrations using relevant product questions and attributable enquiries, rather than assuming every interaction has the same value.
The final recommendation is specific:
Keep the publishing schedule stable and test more practical onboarding content. Review the results before increasing production.
That decision is more defensible than “Competitor A posts 24 times, so we should too.”
The manual framework explains what good research requires. Vaizle helps you turn that framework into a reusable marketing workflow.
Vaizle is an all-in-one marketing workspace with connected data, competitor research, creative analysis, reporting, and live agentic templates. Those templates can run a sequence of analysis steps rather than requiring you to initiate each task separately.
For social competitor research, the practical starting point is your own account context alongside supported public competitor sources.
Connect the relevant accounts you manage, then add the competitor sources for the review.
Vaizle supports Facebook and Instagram competitor-page sources, alongside selected competitor ad sources and websites or webpages. These are public or selected sources, not access to competitors’ private accounts.
Keep the scope focused. For the agency-software example, start with your Instagram account and the three selected Instagram competitors. Add relevant competitor webpages or ad sources when you need to investigate positioning and offers.
Set the analysis objective and guidance, including the reporting period, comparison metrics, and preferred output.
A useful instruction would be to separate observed facts from possible explanations and flag missing information rather than estimating it.
Use an appropriate agent template or configure a workflow around the research steps you need.
For the example in this guide, you could structure the work like this:
| Workflow stage | Output to request |
|---|---|
| Review publishing activity and available metrics | An account comparison using consistent definitions |
| Examine stronger and weaker posts | A shortlist of content patterns with supporting examples |
| Review static creatives and messaging | Observations about hooks, layouts, offers, and positioning |
| Compare findings with your brand context | Opportunities relevant to your audience and product |
| Prepare the research brief | Prioritized tests, evidence, limitations, and next actions |
Vaizle’s agentic templates can combine connected data, analysis, checks, summaries, and reporting outputs into a repeatable process. Its creative capabilities also support visual review of static ad creatives and social media images.
This means the research can include how a post communicates, not only how many interactions it received.
For example, your review could examine whether stronger static posts use a problem-led opening, show the product in context, or explain a process visually. Review those observations alongside the available performance data.
For video posts, Vaizle currently analyzes available metadata and performance information rather than interpreting the full video or its frames. Watch the videos separately when the research depends on pacing, spoken hooks, or demonstrations.
Specify that the report should include supporting post references where available, the period analyzed, comparison definitions, and missing-data notes.
Then review the recommendations against what you know about your customers.
A strong report might identify a repeated educational theme, explain where it appears, and suggest a test. Your team still needs to decide whether that idea fits your product, audience, and production capacity.
For an agency, the same research structure can be reused for another client while keeping that client’s objectives, competitors, and brand context separate.
Vaizle supports recurring email reporting and checks, including daily or weekly schedules. You can use that capability to make research part of your regular review process rather than rebuild the same task each time.
For example, schedule a weekly competitor summary before your content planning meeting. Keep the output focused on new activity, recurring themes, notable changes, and findings worth reviewing.
Follow the Vaizle email scheduling guide to configure delivery and review the resulting schedule.
You can organize the research in three spreadsheet tabs: Account benchmark, Content evidence, and Action plan.
Record the platform, reporting period, collection date, data source, and interaction formula at the top. Keep one benchmark table per platform so different metric definitions do not become mixed together.
The sample rows below continue the illustrative example.
Use one row per account.
| Account | Followers at reference date | Posts analyzed | Average interactions per post | Median interactions per post |
|---|---|---|---|---|
| Your brand | 10,000 | 12 | 100 | 96 |
| Competitor C | 12,000 | 12 | 150 | 146 |
Add fields for the profile URL, total published posts, data coverage, total measured interactions, and calculated engagement rate.
Include starting and ending follower counts only when you have valid snapshots for both dates.
Use one row per post.
| Post reference | Format and topic | Hook and call to action | Visible interactions | Research note |
|---|---|---|---|---|
| Illustrative post C-08 | Carousel about client onboarding | Opens with an approval mistake; asks readers to save the checklist | 165 | Similar practical explanations appear in other stronger posts |
In your working version, replace the illustrative reference with the original post URL. Add the publication date, collection date, visual approach, and any relevant campaign or collaboration context.
Keep the observation separate from your interpretation. “The post contains a checklist” is observable. “The checklist caused the engagement” needs evidence you may not have.
Use one row per proposed experiment.
| Finding and evidence | Next test | Success measure | Owner | Review point |
|---|---|---|---|---|
| Practical onboarding posts repeatedly attract stronger responses in the selected competitor sample | Publish four original onboarding tutorials | Saves relative to reach and relevant questions, compared with your own baseline | Social media lead | After every test post has seven days of results |
Add a priority, current status, result, and next decision.
This last tab closes the loop. Over time, it shows which competitor-inspired ideas worked for your audience and which were not worth continuing.
Use different levels of review rather than repeating the full analysis every week.
The following is a practical starting cadence, not a rule every team needs to follow.
| Frequency | What to review | What you should leave with |
|---|---|---|
| Weekly | Notable posts, campaigns, offers, and relevant audience questions | A short list of changes worth watching |
| Monthly | Comparable performance, repeated content patterns, and results from your own experiments | Adjustments to the next content plan |
| Quarterly | Competitor selection, positioning, platform priorities, and broader audience needs | Changes to your research scope or strategy |
Review sooner when a competitor launches something directly relevant to your audience or when your own results change enough to warrant investigation.
Keep previous snapshots and decisions. Without them, each review becomes another isolated report instead of a record of what changed and what you learned.
Your competitor analysis is useful when it changes what you investigate, create, or test.
Start with a small set of relevant accounts and one decision you need to make. Compare the available information fairly, examine the content behind the numbers, and choose a manageable set of experiments.
The aim is not to make your brand look more like everyone else. It is to understand the category well enough to make better choices for your own audience.
Yes. You can start with public profiles, visible post information, and a spreadsheet.
Vaizle also offers focused Instagram competitor analysis tools for comparing supported account activity and engagement. Check the tool’s current scope and access requirements before starting.
The main trade-off with a manual process is the work involved in collecting consistent observations and repeating them. Historical information is available only when you or your chosen source have collected it.
Yes, you can analyze the public content and information available through supported sources. You do not need access to a competitor’s private account to study its posts, visible engagement, or messaging.
That does not give you the competitor’s complete analytics. For Instagram, Meta distinguishes public Business Discovery information from the insights available for an account’s own content.
An analytics tool may also require authorization from your own account to retrieve the permitted data.
A social media audit evaluates your own presence: account setup, content, performance, goals, and what needs improvement.
Competitor analysis adds an external comparison. It examines relevant competing accounts to help you understand your relative performance, content choices, and positioning.
They work well together. The audit shows what is happening on your accounts; competitor research provides additional context for deciding what to investigate or change.
Choose a tool based on the sources and work you need to automate: data collection, benchmarking, content analysis, creative review, or recurring reporting.
Vaizle supports competitor research using selected public social, ad, and webpage sources, alongside agentic templates and recurring reporting workflows. Its supported social competitor sources include Facebook and Instagram.
Check platform coverage, available metrics, historical depth, and output quality. A long integration list alone does not establish that every platform supports the same competitor research.
Connect your marketing channels, ask questions, compare performance, and get reports without jumping between dashboards.
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