Why do my local service ads get inconsistent results from week to week?

Direct Answer: Inconsistency in local service ads is often due to bot traffic contamination that varies in intensity. When bots hit your campaigns, they trigger pixels and mislead the algorithm, causing performance swings. The page explains that bot traffic can cause a campaign that...

Direct Answer: A low CPL on Meta can hide real quality problems because the platform counts every form submission as a lead, even ones that are unreachable, duplicated, or never engaged. The page explains that you should compare Meta reporting with first-party records like...

Direct Answer: Real visitors produce imperfect, varied behavior like pauses, hesitation, and natural movement shaped by reading and decision-making. Automated browsers tend to have more uniform timing and movement. BotRefund uses these behavioral differences as detection signals. It treats the difference as evidence rather...

Direct Answer: Real visitors on home, mobile, or corporate networks usually have a coherent set of connection, location, language, and timing facts. Bots often use proxy rotation, location masking, or browser spoofing, which make those facts disagree. BotRefund uses the DNS Challenge Blocked check...

Direct Answer: Real visitors produce imperfect, varied behavior with pauses, hesitation, natural movement, and interactions shaped by reading and decision-making. Automated scripts can send clicks and scrolls, but they struggle to match that variation. BotRefund uses behavioral signals like Popup Blocker Bypass to capture...

Direct Answer: Modern bots use residential proxies and headless browser stealth plugins that look identical to real users at the network layer. IP-based blocklists can't catch them. BotRefund inspects the visitor's device and interaction dynamics, such as touch pressure and hardware rendering, to spot...

Direct Answer: Some tools rely on a single rule or a simple browser tell. Sophisticated bots can patch or spoof those tells. BotRefund uses 106 independent checks and AI prediction to look at the whole pattern. A languages mismatch alone is not enough, but...

Direct Answer: Privacy tools, corporate networks, and unusual devices can change browser fingerprints in ways that mimic automation. BotRefund records these anomalies as evidence but does not label them as bots without corroborating signals. This reduces false positives while still flagging suspicious activity.

Direct Answer: Virtual machines and spoofed browser profiles can claim hardware details that do not match what the browser actually does. This often creates mismatches in audio, GPU, fonts, or processor behavior. BotRefund's Audio Latency Drift check captures that mismatch. A real browsing session...

Direct Answer: A normal browser reports a matching set of fonts and operating‑system information. Automated browsers frequently spoof one element while leaving another unchanged, creating a mismatch. BotRefund treats a font‑OS mismatch as a strong indicator of non‑human traffic.

Direct Answer: A single anomaly can be caused by privacy tools, travel, corporate networks, or unusual devices. BotRefund therefore treats one signal as evidence, not a final verdict. It cross-checks the anomaly against other independent data before deciding if a visit is human or...

Direct Answer: When you duplicate a campaign, the new campaign enters its learning phase and is highly vulnerable to bot contamination. If the first 20–50 interactions are from bots on low-quality publisher networks like Google Search Partners, the algorithm will optimize for more bot...

Direct Answer: Meta's automated filters catch general invalid traffic like known bots and data center IPs. Sophisticated invalid traffic uses residential proxies, real devices, and human emulation to pass server-side checks. BotRefund uses client-side behavioral signals to catch these bots after Meta has already...

Direct Answer: Automated browsers often expose partial or placeholder browser capabilities that fail when checked deeply. BotRefund's Background Navigation check looks for those inconsistent details that a real browsing session would not produce. That evidence is cross-checked with browser, network, device, and behavior signals....

Direct Answer: Virtual machines and spoofed profiles can claim one device while their graphics, fonts, audio, or processor behavior tells a different story. This creates the kind of audio and GPU mismatch that real browsing sessions do not normally produce. BotRefund treats that mismatch...

Direct Answer: BotRefund says accuracy comes from corroboration, not one browser tell. It sends each signal like HTTP Protocol Mismatch into a prediction AI that weighs the complete picture across browser, network, device, and behavior evidence. By seeing how all signals fit together, it...

Direct Answer: Accuracy comes from corroboration, not one browser tell. By collecting 106 independent checks, BotRefund builds a complete picture of each visit. The AI prediction model weighs how all signals fit together, so a timezone mismatch alone is not taken as proof. This...

Direct Answer: Because a real browsing session usually has connection, location, language, and timing that fit together. Automated browsers often break that coherence through proxy rotation or location masking. BotRefund uses this mismatch as one piece of evidence, not as the sole reason to...

Direct Answer: Because genuine people can produce unexpected behavior for many valid reasons. Travel, privacy tools, corporate networks, and unusual devices can all cause an anomaly in a browser check. BotRefund keeps each signal as objective evidence and cross-checks it against independent data. Only...

Direct Answer: A single anomaly, like an audio mismatch, is not a bot verdict because legitimate users can have unexpected behavior due to privacy tools, travel, or unusual devices. BotRefund keeps this signal as evidence and cross-checks it against independent browser, network, device, and...