| Characteristic | Bad bot | Good bot | Human |
|---|---|---|---|
| Telecom and ISPs | 55% | 15% | 30% |
| Community & society | 52% | 4% | 43% |
| Computing & IT | 50% | 16% | 34% |
| Travel | 48% | 5% | 47% |
| Business services | 46% | 9% | 45% |
| Gaming | 42% | 19% | 40% |
| Healthcare | 39% | 5% | 56% |
| Marketing | 38% | 3% | 58% |
| Financial services | 38% | 14% | 48% |
| Retail | 33% | 18% | 50% |
| Food & groceries | 31% | 25% | 44% |
| Sports | 31% | 9% | 60% |
| Lifestyle | 31% | 10% | 60% |
| Entertainment | 30% | 56% | 15% |
| News | 29% | 7% | 64% |
| Gambling | 28% | 2% | 70% |
| Education | 27% | 12% | 60% |
| Automotive | 27% | 14% | 59% |
| Law & government | 23% | 11% | 66% |
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April 2025
Worldwide
2024
The source states that bad bots interact with applications similarly to a legitimate user, making it harder for them to be detected and prevented, thus enabling high-speed abuse, misuse, and attacks on websites, mobile apps, and APIs. They allow operators, unsavory competitors, attackers, and fraudsters to perform various malicious activities, which include "web scraping, competitive data mining, personal and financial data harvesting, brute-force login, digital ad fraud, spam, transaction fraud, and more. [...]"
On the other hand, good bots allow online businesses and products to be found by prospective customers, with examples including search engine crawlers like GoogleBot and Bingbot. These engines, through their indexing, help people matching their queries with the most relevant sets of websites.
