Vivien Lee

Affiliate Fashion Writer

Preston's Summary

Vivien Lee is an affiliate fashion writer for Refinery29, Inc. Her work focuses on curating and highlighting the best fashion deals, trends, and must-have items for readers. With a passion for fashion and a keen eye for style, Vivien aims to inspire and provide valuable shopping insights to her audience.

Preston is the artificial intelligence that powers the Intelligent Relations PR platform. Meet Preston

Geo Focus

Australia (National)

Coverage Attributes:

Beta
Promotional Deal: 42 %
Seasonal: 26 %
Gift Guides: 23 %
Review: 4 %
How To Guide: 2 %

Themes Covered:

Not enough data icon

Not enough data

Most Recent Topics:

  • Apparel Design
  • Fashion Industry
  • Luxury Goods
  • Chain Restaurants & Franchises
  • Quick Service Restaurant (QSR)

Pitching Insights

Vivien Lee's coverage heavily focuses on lifestyle, fashion, and shopping-related content, particularly seasonal promotions, gift guides, and discounted deals. If you have a new product launch or promotional deal in the lifestyle or fashion space that fits into these categories (e.g., seasonal sales or unique gifting ideas), this may align well with her coverage.

Given Vivien's emphasis on promotional deals and gift guides, she would likely be interested in pitches related to exclusive discounts for readers or unique gifting options within the lifestyle and fashion sectors. Furthermore, as a significant portion of her articles cover winter fashion and sales events, pitching timely winter-ready products at competitive prices could also resonate with her audience.

This information evolves through artificial intelligence and human feedback. Improve this profile .

Journalists With Similar Coverage:

Based on similarity of content.
Publications
Most recent topics
Not enough data
Publications
Most recent topics
Not enough data
Publications
Most recent topics
Not enough data
Publications
Most recent topics
Not enough data
Publications
Most recent topics
Not enough data
Most recent topics
Not enough data