Scrape Nykaa Products for Indian Beauty Market Research Data

Thirdwatch's Nykaa Scraper returns Nykaa beauty product listings — product_name, brand, price, original_price (MRP), discount_percent, rating, rating_count, image_url, category, in_stock, and a stable SKU. Built for beauty analysts sizing the Indian BPC market, brand teams tracking retail price compliance, indie founders benchmarking their pricing against the category, and researchers building longitudinal Indian beauty datasets.

Why scrape Nykaa for India beauty market research

Nykaa is India's largest dedicated beauty and personal-care marketplace. Per the company's FY24 annual report (NSE: NYKAA), the BPC vertical processed ~INR 11,300 crore in GMV across roughly 6,800 brands and 4.8K SKUs in just makeup. The 2021 IPO valued the company at over $7B at listing, and beauty contributes a majority of that GMV. For the Indian beauty intelligence problem — category sizing, brand share, prestige-vs-mass price laddering, indie-brand discovery — Nykaa is the single most data-rich primary source.

The job-to-be-done is structured. A market researcher sizing the India serum category wants every serum SKU on Nykaa with brand, price, and review counts to estimate revenue distribution. A brand strategist at L'Oreal monitors competitor pricing across MAC, Maybelline, Lakme, and Sugar in real time. An indie founder launching a clean-beauty SKU benchmarks against the 200 most-reviewed face washes to set price and positioning. A trend analyst tracks newly indexed brands week-over-week to identify rising labels before they hit mainstream press. All reduce to keyword or category pulls with structured product rows.

How does this compare to alternatives?

Three paths exist for getting Nykaa product data into a research pipeline:

Nykaa publishes no public catalog API. Affiliate feeds (via vCommission or similar) are coupon-led and exclude the long tail of indie brands. The Nykaa Scraper actor page gives you the public catalog at transparent per-result pricing — no application process, no quota gate.

How to scrape Nykaa for beauty market research in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, copy your personal API token, and export it:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull a full category for sizing research?

Leave queries empty and set category to the leaf category you want to size. The actor will browse the category index. maxResults caps the per-query haul; for a research-scale sweep, 500-1000 is reasonable.

import os, requests, json, datetime, pathlib

ACTOR = "thirdwatch~nykaa-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={
        "queries": [],
        "category": "serum",
        "sortBy": "popularity",
        "maxResults": 500,
    },
    timeout=900,
)
records = resp.json()
today = datetime.date.today().isoformat()
pathlib.Path(f"snapshots/nykaa-serum-{today}.json").write_text(json.dumps(records))
print(f"{today}: pulled {len(records)} serum SKUs")

The category enum supports both top-level (makeup, skin, hair, bath-body, fragrance, mom-baby, men) and leaf categories (lipstick, foundation, serum, face-wash, shampoo, conditioner, eye-makeup, nail, moisturizer).

Step 3: How do I run a multi-keyword sweep for cross-brand comparison?

For brand-vs-brand research, supply your competitor set as queries.

BRANDS = ["maybelline", "lakme", "sugar cosmetics", "mac cosmetics",
          "huda beauty", "charlotte tilbury", "nykaa cosmetics"]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={
        "queries": BRANDS,
        "category": "makeup",
        "sortBy": "popularity",
        "maxResults": 200,
    },
    timeout=900,
)
records = resp.json()
print(f"{len(records)} SKUs across {len(BRANDS)} brands")

Each brand returns up to 200 popularity-sorted makeup SKUs — enough to characterize a brand's Nykaa catalog without paginating the long tail.

Step 4: How do I analyze price laddering and discount depth by brand?

Pandas does the rest. Compute median price by brand, share-of-shelf, and average discount.

import pandas as pd

df = pd.DataFrame(records)
df = df.dropna(subset=["brand", "price"])

# Price ladder by brand
ladder = df.groupby("brand").agg(
    sku_count=("sku", "nunique"),
    median_price=("price", "median"),
    median_mrp=("original_price", "median"),
    avg_discount=("discount_percent", "mean"),
    avg_rating=("rating", "mean"),
    total_reviews=("rating_count", "sum"),
).sort_values("total_reviews", ascending=False)

print(ladder.head(20))

# Prestige vs mass split (MRP cutoff at INR 1500 is a useful heuristic)
df["tier"] = (df.original_price.fillna(df.price) >= 1500).map(
    {True: "prestige", False: "mass"}
)
print(df.groupby("tier").price.describe())

rating_count is the proxy for sales volume on Nykaa — unlike GMV, it's public, monotone, and rarely faked. Sort by rating_count for an honest popularity ranking.

Step 5: How do I dedupe across snapshots and track new SKU launches?

sku and product_id are stable identifiers, ideal as join keys.

import glob

frames = []
for f in sorted(glob.glob("snapshots/nykaa-serum-*.json")):
    date = pathlib.Path(f).stem.split("-")[-1]
    for j in json.loads(pathlib.Path(f).read_text()):
        frames.append({"date": date, **j})

ts = pd.DataFrame(frames).dropna(subset=["sku"])
ts["date"] = pd.to_datetime(ts["date"])
first_seen = ts.groupby("sku").date.min().reset_index().rename(columns={"date": "first_seen"})

# New launches this week
this_week = pd.Timestamp.today() - pd.Timedelta(days=7)
new_skus = first_seen[first_seen.first_seen >= this_week]
print(f"{len(new_skus)} new serum SKUs indexed in the last 7 days")

Sample output

A single Nykaa serum record looks like this. Twenty rows of this shape weigh ~10 KB.

[
  {
    "sku": "8904245700001",
    "product_id": "8904245700001",
    "product_name": "Minimalist 10% Niacinamide Face Serum",
    "brand": "Minimalist",
    "price": 599,
    "original_price": 699,
    "discount_percent": 14.31,
    "currency": "INR",
    "rating": 4.4,
    "rating_count": 38217,
    "image_url": "https://images-static.nykaa.com/media/.../niacinamide.jpg",
    "url": "https://www.nykaa.com/minimalist-10-niacinamide-face-serum/p/4567890",
    "category": "Face Serum",
    "in_stock": true,
    "source_query": "minimalist"
  },
  {
    "sku": "8901030713590",
    "product_id": "8901030713590",
    "product_name": "The Ordinary Hyaluronic Acid 2% + B5",
    "brand": "The Ordinary",
    "price": 950,
    "original_price": null,
    "discount_percent": 0,
    "currency": "INR",
    "rating": 4.6,
    "rating_count": 12044,
    "url": "https://www.nykaa.com/the-ordinary-hyaluronic-acid/p/2345678",
    "in_stock": true,
    "source_query": ""
  }
]

sku and product_id are duplicate fields by design — sku is the more idiomatic join key for analytics warehouses, product_id matches Nykaa's internal naming. rating_count is the most reliable proxy for sales volume since GMV isn't disclosed at the SKU level. original_price: null indicates a full-price SKU (no strike-through). in_stock reflects the listing-page badge.

Common pitfalls

Three things go wrong in production Nykaa pipelines. Brand name normalization — the same brand appears as "MAC", "M.A.C.", and "Mac Cosmetics" across listings; build a lookup table or normalize to lowercase-no-punctuation before grouping. MRP truthing — Nykaa's original_price is the brand-declared MRP, not necessarily a recent shelf price; for true discount-depth research, build your own rolling baseline rather than trusting the strike-through. Indie brand churn — small indie brands cycle on and off Nykaa quickly; a SKU disappearing between snapshots usually means delisted-not-deleted, so retain historical rows rather than overwriting.

A fourth subtle issue: Nykaa's category taxonomy mixes top-level departments (makeup) and leaf categories (lipstick) under one enum. For tree-structured research, fetch by leaf category and treat the top-level enums as fallback "everything-else" buckets.

Thirdwatch's actor handles Nykaa's production-grade anti-bot tooling by intercepting the page's embedded JSON for the listing payload, falling back to DOM extraction when the JSON shape shifts. The pure-pull architecture means a 500-SKU category sweep finishes in roughly two minutes — small enough to run nightly across 20+ categories. Pair Nykaa with our Myntra Scraper for the beauty overlap on Myntra (Tira, Maybelline, Lakme) and AJIO Scraper for AJIO Luxe's growing beauty section.

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