> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flokitai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Run your first campaign with FloKit

> Plan, launch, monitor, and optimize a campaign using payback-aware recommendations.

Use this guide to run a campaign with FloKit as the payback and recommendation layer. The goal is not just to launch spend. The goal is to run a controlled campaign where acquisition, conversion, revenue, and guardrails are visible from the start.

This guide assumes your subscription source, attribution source, and spend data are connected.

## Campaign goals

Start by choosing one clear campaign goal:

| Goal                         | Example target                      | FloKit focus                               |
| ---------------------------- | ----------------------------------- | ------------------------------------------ |
| Validate a new channel       | TikTok annual-plan acquisition      | CAC, trial conversion, payback             |
| Scale a proven campaign      | Increase budget on strongest cohort | Spend, ROAS, payback window                |
| Test creative quality        | Compare UGC hooks                   | Conversion, renewal quality, CAC           |
| Test offer or paywall signal | Annual plan emphasis                | Trial-to-paid, refund rate, payback        |
| Reduce waste                 | Cut weak cohorts                    | Budget shifts, creative pauses, exclusions |

Avoid launching with too many goals. One campaign should have one primary decision.

## Prerequisites

* Production workspace is active.
* Revenue source is connected.
* Attribution source is connected.
* Spend data is available.
* Campaign naming convention is documented.
* Target market, platform, channel, and offer are defined.
* Guardrails are configured.
* Review owner is assigned.

## Campaign setup

<Steps>
  <Step title="Define the campaign brief">
    Document objective, channel, audience, market, budget, offer, creative set, target payback window, and decision date.
  </Step>

  <Step title="Confirm tracking">
    Make sure campaign, ad set, creative, country, platform, and offer fields are available in the attribution source and can be joined to revenue.
  </Step>

  <Step title="Set guardrails">
    Configure campaign-level spend caps, excluded campaigns, approval rules, and rollback triggers before launch.
  </Step>

  <Step title="Launch in the ad platform">
    Launch the campaign using your normal ad platform process. FloKit reads the resulting spend and attribution data.
  </Step>

  <Step title="Monitor early data quality">
    Confirm installs, spend, attribution, trial starts, and subscription events are arriving. Do not make budget decisions from incomplete data.
  </Step>

  <Step title="Review payback signal">
    Once enough data is available, compare CAC, ROAS, trial-to-paid conversion, renewal quality, and payback against the target.
  </Step>

  <Step title="Review recommendations">
    Use the action queue to approve, reject, defer, or manually test recommended budget shifts, creative pauses, or audience changes.
  </Step>

  <Step title="Close the campaign review">
    Document the outcome, next action, and whether the campaign should scale, continue, pause, or become a new test.
  </Step>
</Steps>

## Campaign brief template

| Field             | Example                                    |
| ----------------- | ------------------------------------------ |
| Objective         | Validate annual-plan acquisition on TikTok |
| Channel           | TikTok                                     |
| Market            | US                                         |
| Audience          | Broad interest + lookalike                 |
| Offer             | Annual plan with trial                     |
| Budget            | \$2,000/day initial cap                    |
| Primary metric    | 60-day payback projection                  |
| Secondary metrics | CAC, trial conversion, refund rate         |
| Decision date     | 14 days after launch                       |
| Owner             | Growth lead                                |
| Guardrails        | Max \$2,000/day, rollback if CAC rises 20% |

## What to monitor

| Phase    | Signals                                                                   |
| -------- | ------------------------------------------------------------------------- |
| Day 0-2  | Spend delivery, attribution mapping, event arrival, obvious tracking gaps |
| Day 3-7  | CAC, trial starts, paywall conversion, creative fatigue, early ROAS       |
| Day 7-14 | Trial-to-paid conversion, cohort size, refund risk, projected payback     |
| Day 14+  | Renewal quality, payback trend, scale or pause decision                   |

## Using FloKit recommendations

FloKit may recommend:

* Moving budget toward a stronger cohort.
* Pausing creative with declining conversion or weak payback.
* Excluding a low-LTV audience.
* Testing a different offer or paywall emphasis.
* Changing bids or target CPA where payback supports scaling.

Do not approve recommendations automatically during the first campaign. Review each recommendation against the campaign brief and guardrails.

## Decision framework

| Result                                        | Decision                                               |
| --------------------------------------------- | ------------------------------------------------------ |
| Payback is strong and data quality is trusted | Increase budget within guardrails                      |
| Conversion is strong but payback is weak      | Review offer, retention, refunds, and audience quality |
| CAC is high but renewals are strong           | Extend observation window before cutting               |
| CAC is high and revenue quality is weak       | Pause, reduce budget, or change targeting              |
| Data is incomplete                            | Fix tracking before deciding                           |

## Campaign closeout

At the end of the first review window, document:

* What was launched.
* What data was trusted.
* What data was missing.
* Which recommendations were approved or rejected.
* Whether the campaign scaled, paused, or became a new test.
* What guardrails should change before the next campaign.

## Related pages

* [Get your first payback report](/guides/first-payback-report)
* [Review your first action queue recommendation](/guides/review-action-recommendation)
* [Campaign recommendations](/product/campaign-recommendations)
* [Guardrails](/product/guardrails)