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.
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 ofpaywall_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.
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.
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.