Disable ad Audience Networks to avoid bot click fraud
Force paid ads to show only in native platform feeds by disabling Audience Network and Display Network expansion, eliminating massive bot traffic that inflates costs.
Why this can grow a startup
Default ad platform settings route spend to third-party Audience Networks where bot traffic is rampant. These bots click ads and submit fake leads, artificially inflating marketing KPIs while starving sales teams of real prospects. By restricting ads to native in-feed placements only, the cost per click may appear higher on paper but every dollar goes toward reaching real humans with actual buying intent, dramatically improving true ROI.
Source-backed example
B2B paid advertising deep-dive in Wovly's 250+ case study analysis — measured bot/click-fraud rates of 79% on TikTok Audience Network, 67% on Meta Audience Network, and 27% on Google Display Network, all programmed to click ads and submit fake leads.
Source-reported evidence
Source: wovly.ai · Browse wovly.ai tactics
79% on TikTok Audience Network
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 24, 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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