> 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-scraper.md).

# Sephora

{% embed url="<https://youtu.be/W8vjGiS7ZEQ>" %}

{% hint style="success" %}
**Ready to scrape?** No login required! Preview the sample data, explore the available fields, and download a free sample set.
{% endhint %}

{% tabs %}
{% tab title="Sample Data" %}
{% file src="/files/aW2B6sAUlEqDRMwRepCW" %}

Test page: <https://www.sephora.com/product/ultra-shine-lip-color-P429018?skuId=2857886&icid2=products%20grid:p429018:product>
{% endtab %}

{% tab title="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    |
| {% endtab %}        |                                                      |

{% tab title="Use Cases" %}
**Shade & Formula Feedback.** 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.** Segment sentiment by the reviewer's eye color, skin tone, and skin type to learn how a product performs across different people — invaluable for matching and recommendations.

**Ingredient & Ethics Sentiment.** 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.** Separate gifted or incentivized reviews from organic ones, then feature your highest-voted "recommended" reviews as testimonials.
{% endtab %}
{% endtabs %}

<p align="center"><a href="https://gist.github.com/listly-io/d1a5fcffba7bb6a8bbca623b83f86706" class="button primary">Code</a></p>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://help.listly.io/support/ecommerce-review-scraping/ecommerce-websites/sephora-scraper.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.