Bots are getting good at mimicking engagement

Original Article ↗ Hacker News Discussion ↗

Scale of bot traffic & reactions

  • Many commenters say high bot shares in analytics are unsurprising; figures like 50%+ bot traffic have been seen for years in ad ops, directories, and corporate sites.
  • Others are shocked by numbers as high as ~70%+ and especially by examples like 50k visits → 47 sales.

Economic impact and “does it matter?”

  • One camp argues only bottom-line ROI matters: if $10k in ads yields 47 sales, you judge the channel on that, regardless of whether traffic is bots, mis-targeted humans, or bathroom breaks during a TV spot.
  • The opposing camp insists it does matter:
    • Fraudulent clicks drain budget that could have reached real buyers.
    • Fake traffic wrecks funnel analysis and optimization, causing teams to “fix” conversion when the real issue is distribution.
    • Bot-driven metrics distort bids, retargeting, and attribution.

Incentives and fraud ecosystem

  • Platforms and publishers have strong incentives not to surface the true scale of invalid traffic: filtering it would slash reported impressions and revenue.
  • Internal marketing teams and vendors often prefer inflated dashboards because they support KPIs, bonuses, and fundraising narratives.
  • Some describe this as systemic fraud; others note legal and practical barriers to proving or litigating it.

Sources and types of bots

  • Click-fraud bots: drive fake clicks on ad inventory to earn revenue or game ranking algorithms.
  • “Good”/neutral bots: large-scale scrapers for price, stock, and “buy box” monitoring; SEO tools; internal corporate scrapers.
  • Fake social/SEO engagement bots: build plausible profiles for later manipulation or political campaigns.
  • Commenters emphasize that “good vs bad” is perspective-dependent: useful to one actor, harmful to another.

Measurement, analytics, and optimization

  • Standard analytics (including major free tools) are seen as poor at filtering sophisticated bots; some accuse them of having little incentive to improve.
  • Techniques suggested: use server logs, independent tracking, post-purchase surveys, controlled experiments, and lift-based measurement rather than relying on clicks/attribution alone.
  • Bots pollute retargeting and lookalike audiences, so even “smart” bidding systems can be optimized to bot behavior.

Detection, mitigation, and broader sentiment

  • Cloud-based bot scores and CAPTCHAs catch only crude bots and can hurt conversions. More advanced approaches mix behavior signals, IP intelligence, and resource-loading patterns.
  • Several note the conflict of interest: the article is also marketing an anti-bot analytics product, so specific numbers (like “73%”) should be treated cautiously.
  • Underneath, there’s broad cynicism toward online advertising, with some hoping bot-driven dysfunction eventually undermines the current ad-driven, surveillance-heavy web model.