Introduction:
Competitive intelligence data is crucial for effective pricing strategies. However, acquiring such data comes with challenges, especially due to heightened web security in industries like travel and retail. This webinar explores the obstacles faced and presents solutions for successful data acquisition, whether through in-house efforts or third-party vendors.
Webinar Highlights:
– QL2’s perspective on data acquisition and their approach to obtaining high-quality data.
– The current industry landscape, including significant investments in antibot and fraud detection technologies.
– Identification of various tools and techniques that pose challenges to data acquisition processes.
– QL2’s methodology for acquiring better quality data, enabling professionals to perform their tasks with confidence.
Challenges faced by Companies and Retailers and Why Web Security is Needed?
1. Heightened Web Security Across Travel and Retail Companies:
– Travel and retail companies prioritize web security investments to protect their online platforms.
– Anti-bot technologies are deployed to combat large-scale data collection attempts.
2. Various Tools and Techniques Making Data Acquisition Difficult:
Websites adopt strategies to make data extraction challenging:
- Website formats and layouts are designed to hinder navigation.
- Dynamic content changes frequently based on customer data, requiring customized scraping techniques.
- Honeypot spoofing traps automated crawlers by presenting seemingly legitimate data, leading to incorrect or infinite looped extraction attempts.
- Additional challenges include CAPTCHA tests to differentiate humans from bots and blocking suspected scraping activities.
About QL2
QL2, a leading provider of competitive intelligence, upholds strong ethical standards in web scraping. They ensure access to only publicly available data, respecting user privacy by avoiding personally identifiable information (PII). With smooth data access and no need for fake logins or paywalls, QL2 offers a reliable solution that avoids engagement in botnets and click fraud.
QL2’s Approach to Data Acquisition and Ethical Stance:
QL2 is a leading player in competitive intelligence, providing high-quality data.
- QL2 adheres to ethical standards in web scraping:
- Accesses only publicly available data, avoiding personally identifiable information .
- It offers smooth data access without requiring fake logins, secure access, or paywalls.
- Avoids botnets and click fraud while traversing advertising links.
Investments in Antibot and Fraud Detection Technologies:
Companies invest substantial amounts in antibot technologies to safeguard against data scraping:
- Examples include Human, Perimeter, HCaptcha, Shape, DataDome, Cloudflare, Arkose Lab, Imperva, Google, and Digital Network.
- Antibot fraud detection firms receive significant funding, such as Human snagging a $ 100 million investment
- DataDome raising $35 million for anti-bot solutions.
- Kasada secures $23 million in funding for continuous innovation in bot security.
Reasons for Blocking Bad Bots:
Badbots are subjected to blocking measures primarily because of their involvement in malicious activities that pose significant risks to online platforms and their users. These malevolent automated programs engage in activities such as unauthorized account takeovers, scalping of products or tickets, credit card fraud, and content spamming.
- To safeguard their platforms and protect users from potential harm, companies make substantial investments in technologies aimed at detecting and blocking bad bots. By implementing advanced solutions, businesses can effectively identify and mitigate the threats posed by these malicious bots.
- Blocking bad bots serves as a proactive defense strategy, enabling companies to maintain the integrity of their online environments. It helps prevent unauthorized access, financial fraud, and the dissemination of harmful content.
- By investing in these protective technologies, organizations can ensure a safer and more secure user experience, fostering trust among their customer base.
Conclusion:
Acquiring competitive intelligence data is vital for effective pricing strategies, but it comes with challenges. Heightened web security and antibot technologies pose obstacles for data acquisition. However, QL2 provide ethical solutions, prioritizing transparency and quality. By adhering to best practices and leveraging advanced tools, businesses can overcome these challenges, acquiring high-quality competitive intelligence data to inform their pricing strategies effectively.