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Marketing

Influencer placement analytics

Most influencer budgets are judged by feel: a video looked strong, so the creator gets booked again. You paste a link to a placement, and the platform watches the video, reads the comments, matches promo-code sales from your own data and compares the result with the placements already in your base.

How it works

You giveA link to the placement once it goes live, or a creator channel for the platform to monitor, plus access to the system that records promo-code sales.
The system doesThe watching and the maths. AI goes through the video and the comments, pulls views, likes and engagement, joins promo-code sales from your BI and sets the result against similar placements in your base.
You stay in controlOver what counts as a good placement. You set the scoring rules and their weights per niche, add questions for a specific campaign, and make the final call on every creator.
You getA verdict on each placement: whether it worked, why, what to change next time, and whether to book that creator again and how.

Inside the system

06 components
01

Placement import

Paste a link to a reel and the placement is parsed, tagged to its creator and tracked: views, likes, comments, engagement rate, length, language and platform. Metrics refresh after three days, after two weeks, then weekly.

02

Sales matching

The promo code is read from the caption automatically and joined to your internal sales data, so every placement carries the revenue it actually brought in.

03

Video and comment analysis

AI watches the video and checks it in two passes. First the basics: format, editing pace, setting, product on screen, type of call to action, niche. Then the meaning: where the integration sits, how often the brand is named, the tone of the pitch, competitors in frame, and buying intent in the comments.

04

Scoring with an explanation

Each placement gets a score built on the rules and weights from your brief, a breakdown of pros and cons, and a comparison with the average for the creator's niche. It is checked against the creator's overall record to flag anomalies, and custom questions for a campaign are answered alongside.

05

Creator scouting

A creator's profile is read before you book: niche, market and language, average engagement over the last ten videos, view trends, posting rhythm, the share of sponsored videos, repeat brands, typical formats and calls to action, and the mood and buying intent in the comments. The output is one signal against the niche average: go now, watch, pause or book again.

06

Team workspace

Cards for every video and creator with the factors behind them, niche averages for engagement and top formats, weekly and custom-period digests, filters by niche, creator list and date, and export for reports. Roles have separate access levels, notes and statuses are shared, and the history shows who changed what.

Who it's for

Good fit
Brands placing ads with creators every month
Marketing teams whose influencer budget is spent on gut feel
Teams working with creators across several niches or markets
Not a fit
Brands running a handful of placements a year
Campaigns aimed at awareness only, with no sales to track
Teams with no way to tie a sale back to a creator

What you can configure

01
What makes a placement goodThe scoring rules and their weights, set per niche and revised as the data grows.
02
Which niches you compare againstA set of niches to start, from beauty and tech to travel and gaming, with new ones added from the interface.
03
What each campaign asksCustom questions for a specific campaign, answered for every placement alongside the standard score.
04
How placements get inBy hand from a link to a video, or automatically from connected creator channels the platform monitors.

What we need from you

Your sales dataAccess to the internal system that records orders and revenue by promo code, so each placement is matched to what it sold.
Your scoring briefThe rules and weights that define a good placement for you.
Instagram accessThe accounts the platform needs to read placements and monitor creator channels, where required.

Example use cases

Deciding whether to book a creator a second time

A marketing team rebooks creators because the last video felt strong, with no sales figures behind the decision. An AI agent now matches each placement's promo code to actual orders and says whether a repeat is worth it and what to change in the brief.

Often deployed with

LiveLive — in production today, ready to deploy in your business.

Influencer outreach pipeline

Book creators end to end, from target profile to signed.

ReadsA target creator profile and public web data.
ReturnsA live pipeline of enriched, contacted, booked creators.
×3.3placements per month
See the solution →
LiveLive — in production today, ready to deploy in your business.

Creative performance scoring

Know which creative wins before you spend.

ReadsYour past creatives and their performance data.
ReturnsA predicted score per creative, before any spend.
−15%monthly testing budget
See the solution →

Put sales figures behind every booking decision.

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