PROPOSED PROJECT
Influencer Metrics Tracker
Give sports brands and talent managers one dashboard for athlete and creator campaigns across connected social accounts and supplied performance reports. Compare available reach, views, engagement, and watch time; use AI to tag topics and opening messages, find performance patterns, and draft sponsor reports with clear metric definitions.
Court Sessions
Every number accounted for.
TikTok
2 postsBars compare views within TikTok.
YouTube
2 postsBars compare views within YouTube.
A sponsor report
you can explain.
A fictional sportswear campaign, Court Sessions, features two creators across TikTok and YouTube. The example compares four posts from 1–14 September 2026. Source files report 120,000 and 80,000 TikTok views, plus 42,000 and 28,000 YouTube views. Interactions mean likes, comments, and shares only. The team wants to see which opening messages deserve another test without treating views as unique people.
WHAT IS USUALLY DONE
Sponsor reports are often assembled from screenshots, post links, and spreadsheets. Someone reconciles dates and totals by hand, while missing watch-time data can look like a zero. A high-view post gets called a winner without checking the platform, format, or campaign objective.
WHAT SOVRUN CAN DO FOR YOU
Sovrun would build a campaign dashboard for a sports brand or athlete manager, starting with two social platforms and one sponsor campaign. Connect authorized accounts or import the team’s reports, match each post to its creator and campaign, and retain the reporting window and source. AI groups posts by topic and opening message. The team can compare like-for-like posts, inspect missing data, and export a sponsor report with the numbers behind every claim.
Open the number.
See the source.
A chart is only useful if the team can trace it back to the post. These example records show the exact counts behind the report, including the fields that are missing.
Start with the drill
- Views
- 120,000
- Likes
- 6,800
- Comments
- 400
- Shares
- 1,200
- Interactions / views
- 7.0%
- Watch time
- Not supplied
Source: creator-export.csv
Record TT-01 · 01–14 Sep 2026
Start with the gear
- Views
- 80,000
- Likes
- 2,800
- Comments
- 200
- Shares
- 600
- Interactions / views
- 4.5%
- Watch time
- Not supplied
Source: creator-export.csv
Record TT-02 · 01–14 Sep 2026
Break down the play
- Views
- 42,000
- Likes
- 1,800
- Comments
- 120
- Shares
- 180
- Interactions / views
- 5.0%
- Watch time
- 12,600 min
Source: channel-report.csv
Record YT-01 · 01–14 Sep 2026
Walk through the kit
- Views
- 28,000
- Likes
- 1,100
- Comments
- 100
- Shares
- 200
- Interactions / views
- 5.0%
- Watch time
- 8,400 min
Source: channel-report.csv
Record YT-02 · 01–14 Sep 2026
Lead with the drill?
Nia’s drill-led post has 7.0 interactions per 100 views; Ren’s gear-led post has 4.5, within this TikTok sample. Different creators and audiences could explain the gap. Test both openings with the same creator and comparable posts before choosing a direction.
Connector scope follows account permissions and the available reports. See the official TikTok video metrics and YouTube Analytics definitions.
The report.
And what sits behind it.
A proposed first build connects two sources, tracks one sponsor campaign, and gives the manager three useful outputs. No performance uplift is assumed.
Court Sessions
- TikTok views
- 200,000
- YouTube views
- 70,000
- Posts included
- 4
- Reporting window
- 01–14 Sep
From post to proof.
Same post. Same reporting window. Source file attached.
What happened. What to try.
- Results by platform
- Posts behind the totals
- Missing source data
- Next opening to test
How we’d make it happen.
Connect the source records
Agree on the campaign window, import a sample, and match account and post IDs. Keep a record of the file or account each number came from, including later corrections.
Compare the right things
Separate platform and format groups. Calculate agreed metrics from the source values, then review AI-suggested content labels. Keep unavailable fields visibly different from zero.
Build the sponsor report
Let the manager inspect chart rows, approve the narrative, and export the report. Re-import corrected files without counting the same post twice.
Where AI helps
Language and video models suggest content labels such as training advice, match reaction, or product demonstration. We compare their labels with a reviewer’s sample before using them at scale. Calculations run in code from source records. AI can suggest a pattern to test, but cannot reveal a platform’s ranking algorithm or prove why a post performed well.
What people approve
The campaign manager approves creator identities, post matches, metric definitions, content labels, and the final sponsor narrative. A source without permission or a missing metric remains unavailable. Reports do not combine platform reach into a claimed unique audience.
What we’d need to start
- Campaign brief, creator roster, post links, and the reporting dates
- Authorized analytics access or performance reports from each creator
- Metric definitions, sponsor reporting template, and the person who approves the report
How we’d test it
Reconcile dashboard totals with the supplied reports. Test duplicate imports, late corrections, deleted posts, disconnected accounts, missing watch time, and zero views. On a labeled sample, measure how often AI topic tags agree with the team. Compare report preparation time with the previous process before claiming time savings.
Prepared for Sports & fitness. We would agree on the work after reviewing your brief, materials, and requirements.
This is a proposed project, not completed client work. The visuals and numbers show example outputs, not measured results.
