> For the complete documentation index, see [llms.txt](https://help.listly.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.listly.io/support/ecommerce-review-scraping/ecommerce-websites/sephora.md).
# Sephora Fields
**Product Reviews** ยท **Beauty Insights**
> `Beauty / Reviews`
See how a product performs for real people. This workflow pulls structured Sephora reviews into clean data: the shade chosen, whether they'd recommend it, the reviewer's skin profile, verified purchases, helpful votes, and full text. So you can see what works, for whom, and why.
{% hint style="success" %}
**Ready to run!** No login required โ explore the use cases, preview the sample data below, and download a free sample set.
Code
{% endhint %}
***
## ๐ Available Fields
| Field | Description |
| ------------------- | ---------------------------------------------------- |
| `review-title` | Headline of the customer review |
| `review-body` | Full text of the customer review |
| `shade` | Product shade / variant the reviewer chose |
| `recommended` | Whether the reviewer recommends the product |
| `reviewer-name` | Display name of the reviewer |
| `skin-profile` | Reviewer's self-reported eye color, skin tone & type |
| `verified-purchase` | Whether the review is from a verified purchase |
| `review-date` | Date the review was posted |
| `helpful-yes` | Number of "helpful" up-votes the review received |
| `helpful-no` | Number of "not helpful" votes the review received |
***
## ๐ก Use Cases
**Shade & Formula Feedback** โ Break reviews down by shade and variant to see which colors and formulas win, and which draw complaints about texture, wear, or color accuracy.
**Skin-Profile Segmentation** โ Segment sentiment by the reviewer's eye color, skin tone, and skin type to learn how a product performs across different people โ invaluable for matching and recommendations.
**Ingredient & Ethics Sentiment** โ Surface recurring themes such as cruelty-free, fragrance, or sensitivity concerns from review text to inform product and messaging decisions.
**Incentivized-Review Detection & Social Proof** โ Separate gifted or incentivized reviews from organic ones, then feature your highest-voted "recommended" reviews as testimonials.
โถ Try it for Free
***
## ๐ Sample Data
{% file src="/files/aW2B6sAUlEqDRMwRepCW" %}
***
## ๐ Explore Other Web Scrapers
{% content-ref url="/pages/adfad0a3887040e1053c911bbbe426c8ac11af48" %}
[Amazon](/support/ecommerce-review-scraping/ecommerce-websites/amazon-scraper.md)
{% endcontent-ref %}
{% content-ref url="/pages/1oefBU7aSck9BdFuHUUB" %}
[Costco](/support/ecommerce-review-scraping/ecommerce-websites/costco-scraper.md)
{% endcontent-ref %}
***
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---
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