a16z Podcast | It's Not What You Say, It's How You Say It -- When Language Meets Big Data

TL;DR

Text EO CEO Karen Schneider discusses the importance of language in job listings and how text analysis can improve diversity and overall performance. The company's technology can also be applied to various other industries and documents.

Transcript

hi everyone welcome to the a six in Z podcast I'm sonal and I'm here today with Michael and we are talking to Karen Schneider who is the CEO and co-founder of text EO a company that analyzes job listings to predict how well they're going to perform and can help optimize them to get more qualified diverse candidates and interestingly they've been ab... Read More

Key Insights

• 😒 The words we use and how we use them are important, even in a tech-oriented world where data is emphasized.
• 🆘 Text analysis can reveal hidden biases and help optimize job listings for more diverse and qualified candidates.
• 💦 Language patterns that work in one industry or document may not necessarily work in another, highlighting the need for specific analysis in each case.
• 🅰️ Text EO's technology can be applied to various industries and content types, offering opportunities for optimization and improvement.
• 🥺 It is important to find a balance between tailored content and standing out from the crowd, as too much optimization can lead to bland and average results.
• 🌥️ Cloud computing and access to large datasets have democratized the ability to perform text analysis and optimize content.
• 👣 The field of natural language processing is evolving, and the ability to track language changes in real-time is transforming lexicography.

Questions & Answers

Q: How does Text EO analyze job listings to predict their success?

Text EO collects a large dataset of job listings and their outcomes, then applies natural language processing techniques to identify successful patterns in the language used.

Q: What are some key findings about language in job descriptions?

Language like "we'd love to hear from you" and "intact" are positive and encouraging, while phrases such as "rock star" and "ninja" can deter diversity. Language also varies by industry and geographical location.

Q: Has Text EO found any gender differences in job descriptions and performance reviews?

Yes, Text EO has found differences in how men and women describe themselves in resumes and how they are described in performance reviews. For example, terms like "abrasive" are more commonly used in women's reviews.

Q: Can Text EO's text analysis be applied to other industries and documents?

Yes, the technology can be used to analyze various types of content, such as real estate listings, marketing materials, pitch decks, and even screenplays. The specific language patterns that work may vary by industry.

Summary & Key Takeaways

• Text EO analyzes job listings to predict their success and optimize them for more qualified and diverse candidates.

• The company has found that certain words and phrases, such as "synergy," are ineffective in job descriptions, while others, like "intact" and "hard problems," are successful.

• Schneider also discusses how language can reveal hidden biases in performance reviews and resumes, and how text analysis can be applied to different industries and types of content.