Per-comment sentiment
Every comment is read one by one. Not just positive / neutral / negative, but sub-classified as criticism, praise, question, purchase intent and more.
About us
We measure the real story behind every campaign.
We are an analytics platform first. We track your influencer campaigns from publish moment to final report, and turn comments, engagement patterns and industry data into a concrete answer to 'what did this campaign actually do'.
Our story
Why we focused on deep analytics.
Most influencer campaigns in Türkiye are reported with a screenshot and a total like count. Brands spend the budget, the content goes live, and they're left without a real answer to 'did this work'. Surface engagement metrics drive the decision — even though a campaign's true impact lives in the conversation underneath the content.
That's why we built an analytics platform. We read every comment one by one, surfacing sentiment, purchase intent, the themes people raise and the shifts in brand perception. We then benchmark that against the industry and the campaign's objective (awareness / conversion / reputation) to produce an objective performance score.
Our creator discovery and content approval flow exist to make that analysis happen consistently. The actual product is the report you receive at the end: 'this size got a lot of complaints', 'this message doubled purchase intent', 'you're in the top band of the industry norm'. Every new campaign sharpens our report methodology.
app.vibemetri.com/reports/example
Campaign report
Örnekv5.7.13 · brand_awareness
What we measure
The layers we see in a campaign.
Our analysis pipeline runs automatically after every publish. Brands don't have to open separate reports — everything lands in one campaign report.
Purchase intent
Signals like 'where's the link', 'just ordered', 'do you have a size chart' are detected and turned into an intent score.
Theme clustering
Topics raised in the comments are grouped (fabric quality, shipping time, perceived value). You see immediately which themes are positive and which are creating friction.
Objective-based performance score
Scoring weights vary by campaign objective (awareness, sales, reputation). The same piece of content can be good for awareness but middling for sales — we separate the two.
TR-calibrated indicative norms
Sentiment and themes are compared against TR-calibrated, publicly-sourced indicative industry norms by category and market. Engagement and reach references are computed by follower segment (nano → mega). 'Better or worse than the industry' gets a TR-calibrated answer, not a global average.
Action-plan recommendations
The report doesn't stop at 'what happened'; it suggests 'what should change next campaign'. Concrete list of messages to repeat and themes to address.
Methodology
How we operate.
Transparent methodology
Scoring formula, sentiment categories, benchmark norm and its source — all shown openly in the report. No 'black-box score'.
Data-first
We don't make decisions on 'a feeling'. Adding a new metric goes through validation, verification and industry cross-check.
Local calibration
Comment dynamics in Türkiye (sarcasm, emoji use, slang) are calibrated specifically for Turkish. We correct the global model with local data instead of copy-pasting it.
Contact
Get in touch.
Demo, sample report, or a question about our methodology — drop us a line, we'll get back.