Disable Audience Network to eliminate paid ad click fraud
Turn off Audience Network and Display Network expansion on paid ad campaigns to avoid massive bot traffic that inflates metrics while starving your pipeline of real leads.
Why this can grow a startup
Default ad platform settings route budget to third-party sites riddled with programmatic click fraud bots. These bots click ads and submit fake leads, making your cost-per-lead look cheap while delivering zero real pipeline. Forcing ads to show only in native in-feed placements raises the visible CPC but dramatically improves lead quality and actual ROI.
Source-backed example
Wovly analysis of 250+ founder case studies — provable bot traffic rates on third-party placements: TikTok Audience Network (79%), Meta Audience Network (67%), Google Display Network (27%).
Source-reported evidence
Source: wovly.ai · Browse wovly.ai tactics
79%)
Attributed to the cited source; GrowthDex has not independently verified it. Use this as context, not as a forecast, benchmark, or recommended target.
Evidence scope: claims and metrics are attributed to the linked source; GrowthDex has not independently replicated the reported result. Check the original context and validate fit before applying it. Read the evidence methodology.
Source discovered: March 25, 2026
When to use it
Use this when Ads is relevant to 0-100, 100-1K and you can run a bounded test with a paid budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
How to do it
- Choose one target segment and define the exact next action this tactic should prompt.
- Adapt the tactic to Ads, using evidence from wovly.ai as context—not as a promised result.
- Record a baseline for: Cost per qualified conversion. Set a guardrail that would make you pause the test.
- Run a narrow pilot and choose a test window that fits the normal decision or use cycle; do not scale before you have observed the full cycle.
- Compare the signal with its baseline. Scale only if the intended action improves without crossing the guardrail; otherwise revise one variable or stop and record what you learned.
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