Normalize channel changes to one baseline
A reusable skill shaped around you
Comparable-window paid media ops
LiFoli Works monitors paid-media changes on a shared baseline and prepares one experiment plus budget recommendation for approval.
Separate cause candidates
Propose one experiment
Prepare the budget change for approval
What Foli learns by watching
- Which conversions count as success
- Budget stop/increase thresholds and approvers
- How lag and seasonality are interpreted
What arrives for your review
- Channel change monitor
- Cause candidate list
- Single experiment plan
- Budget change approval pack
What LiFoli already understands
The structure this work already shares.
Paid media ops aligns goals and conversion definitions, compares cost, conversion lag, creative, and targeting changes, then runs one hypothesis at a time.
Every run leaves the evidence, the exceptions, and your corrections behind, so the next one starts closer to your way.
From observed work to the next run
The skill Foli builds for your team.
An ads ops skill that watches comparable windows and prepares one evidenced experiment at a time.
- Asks instead of guessing when this run differs from the last one
- Stops before anything is sent, submitted, or paid
- Leaves the evidence and the open exceptions attached to every result
FAQ
What teams ask before they start
Does LiFoli already understand Paid media operations work?
Paid media ops aligns goals and conversion definitions, compares cost, conversion lag, creative, and targeting changes, then runs one hypothesis at a time. An ads ops skill that watches comparable windows and prepares one evidenced experiment at a time.
How far does Foli take Paid media operations work on its own?
Foli gathers the sources, cross-checks them, drafts the output, and lists every exception it could not resolve. Budget moves, bid policy changes, and major creative launches need media approval.
How does observed work become automation?
Foli does not only record clicks. It structures inputs, steps, judgments, exceptions, approvals, and completion criteria into a reviewable skill. An ads ops skill that watches comparable windows and prepares one evidenced experiment at a time.
What if the workflow changes?
When inputs, screens, or rules diverge from the last run, Foli asks instead of guessing. Your corrections become part of the next skill.
Shape one workflow first
Automate one repeat task with approval still in your hands.
We confirm your current steps, judgment points, and the first skill Foli should prepare.