> ## 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.

# Campaign recommendations

> How FloKit generates and prioritizes growth action recommendations.

FloKit surfaces recommendations when it detects a gap between your current acquisition behavior and optimal payback. Recommendations are generated daily and prioritized by projected impact on CAC payback — not by platform-reported metrics like CTR or impression share.

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## How recommendations are generated

FloKit continuously monitors five signal categories:

* **Budget allocation vs payback-adjusted ROAS** — identifies campaigns where spend is misallocated relative to actual subscription revenue returned.
* **Creative fatigue** — tracks trial conversion rate (not just CTR) on running creatives. A creative can maintain strong CTR while trial conversion declines — FloKit flags the latter.
* **Cohort LTV divergence** — detects when the same channel produces materially different LTV across cohort weeks, which indicates an audience or offer shift worth investigating.
* **Offer and paywall conversion rates vs subscription quality** — surfaces cases where a high-converting offer is producing low-renewal subscribers, or a lower-converting offer is producing higher-LTV cohorts.
* **Country-level payback differences** — identifies markets within shared campaigns that have significantly different payback windows, which can distort blended campaign metrics.

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## Types of recommendations

### Budget recommendations

"Shift \$X from Campaign A to Campaign B."

Triggered when payback-adjusted ROAS in the target campaign is more than 15% higher than the source campaign, spend capacity exists in the target (not budget-limited), and the confidence threshold is met. FloKit calculates payback-adjusted ROAS using actual subscription revenue — not platform-reported conversion value.

### Creative recommendations

"Pause creative X — trial conversion declining 35% vs 30-day average."

FloKit flags creatives where **trial conversion rate** is declining, independent of CTR. A creative that still generates clicks but is producing fewer trials is a spend efficiency problem that platform reporting won't surface. Creative recommendations always include the trailing 30-day trial conversion baseline for comparison.

### Audience recommendations

"Exclude lookalike segment Y — 60-day refund rate 3× account average."

Audience recommendations are based on cohort quality — renewal rate, refund rate, and 90-day LTV — not just acquisition volume. A lookalike segment that generates installs cheaply but churns fast is costing you more per retained subscriber than the CPI suggests.

### Offer recommendations

"Test 7-day trial on annual plan in UK — annual plan has 2× 90-day LTV vs monthly but lower trial conversion."

FloKit suggests A/B tests for offers, not unilateral changes. Offer recommendations surface cases where LTV signals suggest an untested offer configuration could improve payback, and propose a scoped test to validate. FloKit does not modify offers or pricing directly.

### Paywall recommendations

"High-performing offer X is not shown to cohorts with the strongest payback history — consider broadening exposure."

Paywall recommendations are generated from a join of `paywall_viewed`, `offer_viewed`, and `subscription_started` events. They surface mismatches between your best-converting paywall configurations and your highest-LTV user segments. Available during private beta.

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## Confidence scoring

Each recommendation includes a confidence score from 0 to 100. The score is calculated from:

* **Sample size** — number of users in the affected cohorts.
* **Statistical power** — signal strength relative to variance in the metric.
* **Cohort stability** — whether the trend is consistent across multiple cohort weeks or driven by a single outlier period.

FloKit does not surface recommendations below 70% confidence. The confidence threshold for actions entering the approval queue is configurable in [Guardrails](/product/guardrails) — the default is 80%, recommended minimum is 85%.

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## Explaining a recommendation

Every action in the queue includes a structured explanation:

* **The signal that triggered it** — the specific metric deviation FloKit detected.
* **The expected impact** — projected change in CAC, ROAS, or trial conversion, with the confidence score.
* **The guardrails checked** — which guardrails were evaluated and whether any were close to triggering.
* **The rollback condition** — the metric threshold that would trigger a rollback if the action goes live.

If an explanation feels incomplete or the signal doesn't match what you're seeing in your own reporting, use the **Feedback** button on the action card. FloKit's team reviews flagged recommendations and they feed directly into model improvement.