> ## Documentation Index
> Fetch the complete documentation index at: https://anchors.in/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# AI Analysis Report
> A complete guide to the AI Analysis Report on anchors — what it measures, how to read it, and how marketers can use it to make better campaign decisions.
The AI Analysis Report is the most detailed view of your campaign on anchors. It goes well beyond impressions and engagement rate. It tells you what people felt when they read the post, what questions they asked, whether they wanted to buy, and which creator actually delivered business value — not just reach.
For marketers, this report is where influencer marketing stops being a gut-feel channel and starts being a data-backed one.
The AI Analysis Report is available after posts go live and comments start coming in. The more comments a campaign generates, the richer the analysis. Reports update as new data syncs from LinkedIn.
***
## How to access the report
From your live campaign dashboard, click **View All Analysis** or **Check Post-Analysis Report** on any creator row. You can also access it from the left-side campaign progress panel under **AI Analysis Report**.
***
## Overall vs creator-level report
At the top of the report, you can switch between two views:
| View | What it covers |
| --------------------------- | ------------------------------------------------- |
| Overall Campaign Analysis | Combined data across all creators in the campaign |
| Individual Creator Analysis | Deep dive into one specific creator's performance |
Start with the overall view to understand how the campaign performed as a whole. Then go creator by creator to understand who drove which kind of value.
***
## The three report tabs
Both the overall view and the individual creator view are split into three tabs. Each tab answers a different question.
| Tab | The question it answers |
| ------------------- | ------------------------------------------------- |
| AI Comment Analysis | What did people say and how did they feel? |
| Engaged Audience | Who actually interacted with the content? |
| Reached Audience | Who saw the content, even if they did not engage? |
***
## Tab 1: AI Comment Analysis
This is the most valuable tab for marketers. Comments on LinkedIn are not noise — they are direct signals from your target audience about how they responded to the campaign.
### Campaign metrics summary
At the top of this tab, a quick summary of the campaign's overall performance:
| Metric | What it shows |
| ----------------------- | -------------------------------------------- |
| Total reach | Unique people who saw the campaign posts |
| Total likes | Combined likes across all posts |
| Total comments | Combined comments across all posts |
| Total reposts | Combined reshares across all posts |
| Average engagement rate | Engagement rate averaged across all creators |
### Top audience categories
Shows the strongest audience signals from the campaign across four dimensions. You can filter the view by reach, engaged likes, or engaged comments.
| Dimension | What it tells you |
| --------- | ------------------------------------------------------------- |
| Role | Which job titles showed up most in the campaign audience |
| Location | Which cities or countries the audience came from |
| Seniority | Whether you reached entry level, managers, directors, or CXOs |
| Industry | Which industries the audience works in |
This is useful for comparing against the audience you targeted. If you set filters for founders and HR leaders but the top role in your report is student, something went wrong in creator selection and you can fix it in the next campaign.
***
### Sentiment Analysis
The AI reads every comment across all creator posts and sorts them into three buckets.
| Sentiment | What it means |
| --------- | ----------------------------------------------------------------------- |
| Positive | Comments that show approval, interest, praise, or enthusiasm |
| Negative | Comments that show criticism, disappointment, or concern |
| Neutral | Comments that are informational or observational without strong feeling |
Under each sentiment bucket, the report shows example comments so you can read actual responses rather than just percentages.
**Why this matters for marketers**
Sentiment tells you things a like count never will. A post can have 500 likes and a comment section full of scepticism. A post can have 80 likes and 40 comments from people saying they are going to try the product this week. The numbers look very different but the second post is the better campaign outcome.
Tracking sentiment across campaigns also tells you whether certain creators, topics, or content formats consistently generate more positive responses. That feeds directly into brief writing and creator selection for future campaigns.
### Emotional signals
Beyond positive, negative, and neutral, the report also identifies the specific emotions present in comments:
| Emotion | What it looks like in comments |
| -------------- | -------------------------------------------------------------- |
| Excitement | "This is exactly what I have been looking for" |
| Curiosity | Questions about how the product works or where to get it |
| Trust | Mentions of the brand or creator being credible |
| Support | Encouragement and sharing the post with others |
| Disappointment | Expectations that were not met |
| Frustration | Complaints about a feature, experience, or claim |
| Skepticism | Doubt about the product's claims or the creator's authenticity |
Scepticism and frustration in comments are early warning signals. If they appear consistently across multiple creators in a campaign, it usually means either the product claim in the brief was too strong or the creator's audience did not trust the collaboration.
### Comment depth analysis
The report splits comments into two categories:
| Category | What it means |
| ----------- | -------------------------------------------------------------------------------- |
| Superficial | Short, low-effort comments like "Great post" or single word reactions |
| Detailed | Longer comments that engage with the content, ask questions, or share an opinion |
Detailed comments indicate real audience involvement. They are also more likely to be from the professional, decision-maker audience you were trying to reach. A high ratio of detailed to superficial comments is a strong signal that the content resonated at a deeper level.
***
### Customer Query Analysis
This section is where the report shifts from brand perception into business intelligence. The AI identifies comments that contain actual buyer signals and sorts them into three categories.
Comments where people asked questions about the product — how it works, where to get it, pricing, availability, features, or anything else they wanted to know before deciding whether to try it.
The report shows:
* Volume of product queries
* Top query categories (delivery, pricing, availability, features, etc.)
* Example queries
* AI-generated insight summarising what the audience most wanted to know
**Why this matters**
Product queries are your market research. If ten people asked about pricing in comments, that is a signal your brief did not address cost expectations clearly enough — or that pricing transparency could convert more buyers. If people keep asking where to buy, your brief needs a clearer call to action. These insights directly improve your next campaign brief.
Comments that signal a real intention to try, order, or buy. These are not just interest — they are the closest a comment can get to a conversion signal.
The report shows:
* Total purchase intent count
* Intent level distribution (low, medium, high)
* Intent keywords that triggered the classification
* High-intent example comments
* AI-generated purchase intent insight
**Why this matters**
Purchase intent comments are the clearest ROI signal in an influencer campaign outside of actual tracked conversions. If a creator generated 40 high-intent comments on a campaign where you paid them a fraction of what a lead generation ad would cost for the same signal, that creator is worth working with again. This data lets you calculate influencer value in business terms, not just marketing terms.
Comments that flag a problem, worry, or friction point. These include concerns about delivery, quality, refunds, customer service, pricing fairness, or anything else that made someone hesitate.
The report shows:
* Concern categories
* Concern volume per category
* Specific concern examples
* AI-generated recommendations for how to address them
* Common themes: delivery, quality, pricing, refunds, customer service
**Why this matters**
Concerns in comments are objections that are costing you conversions. Most brands never collect this data because it requires reading hundreds of comments manually. anchors surfaces it automatically. If the same concern appears across multiple creators' posts, it is not a one-off — it is a pattern in how your audience perceives the product. That is product feedback, not just campaign feedback.
***
### Best performing influencers
This section highlights creators by the type of value they delivered — not just who got the most reach.
| Category | What it identifies |
| ------------------------------- | --------------------------------------------------------------- |
| Best performing influencer | Highest overall campaign performance across metrics |
| Highest positive sentiment | Creator whose comments had the most positive audience response |
| Most customer queries generated | Creator who drove the most audience curiosity about the product |
| Most purchase intent | Creator whose audience showed the strongest buying signals |
This matters because the creator who generated the most impressions is not always the one who delivered the most business value. A mid-sized creator with a highly engaged niche audience often drives more purchase intent and more qualified queries than a larger creator with a passive following.
***
### Influencer performance comparison table
A side-by-side breakdown of every creator in the campaign across the key intelligence metrics.
| Column | What it shows |
| ----------------- | ------------------------------------------ |
| Influencer | Creator name |
| Total comments | Volume of comments on their post |
| Positive comments | Count of positive sentiment comments |
| Negative comments | Count of negative sentiment comments |
| Neutral comments | Count of neutral sentiment comments |
| Product queries | Number of product questions in comments |
| Purchase intent | Number of comments showing buying interest |
| Customer concerns | Number of comments flagging concerns |
This table is exportable as a CSV. Use the **Export CSV** button at the top right of the table to download the full creator comparison. This is useful for presenting campaign results to stakeholders, comparing campaigns over time, or building your own analysis on top of the data.
***
## Tab 2: Engaged Audience
This tab focuses on who actually interacted with the campaign — people who liked or commented, not just people who scrolled past.
### Top metrics
| Metric | What it shows |
| --------------- | --------------------------------------- |
| Engagement rate | Average rate across all posts |
| Total likes | Combined likes from engaged audience |
| Total comments | Combined comments from engaged audience |
### Audience snapshot
A quick summary of the strongest engaged audience signals:
| Signal | What it tells you |
| ------------- | -------------------------------------------------- |
| Top role | The most common job title among people who engaged |
| Top location | Where most of the engaged audience is based |
| Top industry | The industry most represented in engagement |
| Top seniority | The seniority level most present in engagement |
### Audience distribution
A full breakdown of the engaged audience across four dimensions. Toggle between **Likes** and **Comments** to see whether different audience segments engage differently.
| Dimension | What to look for |
| ---------------- | ------------------------------------------------------------------- |
| Job roles | Are the roles engaging with the content the same ones you targeted? |
| Locations | Is engagement coming from the right geographies? |
| Industries | Are the industries represented what you expected? |
| Seniority levels | Are decision-makers engaging or mostly junior profiles? |
**Why this matters for marketers**
Engagement from the wrong audience is a common problem in influencer marketing that most brands never catch because they do not have this data. If you targeted CTOs and your engaged audience is mostly entry-level employees, the creator's audience did not match what their profile suggested. This tab gives you proof of that mismatch so you can make a better creator decision next time.
***
## Tab 3: Reached Audience
This tab shows who saw the campaign posts, even if they did not like or comment. It is about visibility and audience spread.
### Top reach metrics
| Metric | What it shows |
| ----------------------- | ------------------------------------------------- |
| Total reach | Unique people who saw the posts |
| Engagement rate | How much of the reached audience actively engaged |
| Likes | Total likes from reached audience |
| Comments | Total comments from reached audience |
| Likes to comments ratio | Balance between passive and active response |
### Reach summary
Quick snapshot of the strongest reached audience signals — top location, top role, top seniority.
### Audience distribution
Full breakdown of the reached audience across job roles, locations, industries, and seniority. This is particularly useful for awareness campaigns where the goal was visibility into a specific professional segment rather than direct engagement.
**Reached vs engaged — why both matter**
| | Reached audience | Engaged audience |
| ----------------- | --------------------------------------------- | -------------------------------------------- |
| What it measures | Who saw the content | Who responded to it |
| Best for | Awareness campaigns | Performance campaigns |
| What it tells you | Whether the campaign reached the right people | Whether the content moved the right people |
| Risk to watch | High reach, wrong audience | High engagement, low seniority or wrong role |
***
## Individual creator report
Switch to any creator using the selector at the top of the report page. Everything described above — all three tabs — is available at the individual creator level too.
Use the individual report to:
* Compare two creators who had similar reach but different comment quality
* Identify which creator drove the most purchase intent relative to their audience size
* Decide which creators to work with again based on audience fit, not just performance numbers
* Build an internal shortlist of high-value creators for future campaigns
***
## Rate the influencer
After reviewing the AI report, you will see a **Rate the Influencer** panel on the side. This is private and only visible to your team.
| Field | What to fill in |
| ------------------------------- | ------------------------------------------------------------------------------------- |
| Overall experience rating | Star rating covering content quality, revision experience, and campaign effectiveness |
| Future collaboration likelihood | Score from 1 to 10 on whether you would work with them again |
| Additional feedback | Free text for specific notes about the collaboration |
> Submit a rating for each creator. This data feeds into anchors' creator ranking system and improves future recommendations for your account and others on the platform.
***
## What marketers should do with this report
Most marketers look at a campaign report once and file it. Here is how to actually use it.
If negative sentiment or scepticism appeared consistently, read the example comments. They will tell you exactly which claim, tone, or product angle did not land. Rewrite that part of your brief for the next campaign.
Every unanswered question in a comment is a conversion that did not happen. If people kept asking about pricing, add a pricing reference to the next brief. If they asked about delivery, make sure creators mention it. The comments are telling you what your content is missing.
Sort the influencer comparison table by purchase intent. The creator at the top of that column is your highest business-value creator from this campaign. Work with them again. The one at the bottom with high reach and zero purchase intent is not worth the same budget next time.
Compare the top role and top seniority in your engaged audience against what you set in the influencer criteria. If there is a mismatch, your creator selection or audience filters need adjusting. The data tells you exactly where the gap is.
Download the influencer performance table as a CSV. Use it to build a campaign summary for your team or leadership. The data covers sentiment, queries, intent, and concerns — enough to present a full picture of what the campaign delivered without relying on vanity metrics.
If the same concern appeared in comments across multiple creators, take it to your product or customer success team. Comments on an influencer post are unsolicited feedback from real potential buyers. That is valuable data that goes beyond campaign performance.
***
## Next steps
Save top-performing creators from this campaign to your private influencer list, you can access from the dashboard
See how other brands are running LinkedIn influencer campaigns for inspiration and benchmarking.