Track Myntra vs AJIO and Nykaa Prices in Real Time (2026)

Thirdwatch's Myntra, AJIO and Nykaa Scrapers feed a structured cross-marketplace India fashion-and-beauty price tracker — daily snapshot the same SKU watchlist on all three platforms, compute price deltas, surface arbitrage and brand-protection signals. Built for India e-commerce ops teams, brand-protection functions, arbitrage operators, and price-comparison consumer apps.

Why track Myntra vs AJIO and Nykaa

India fashion e-commerce is a three-way race. According to RedSeer Strategy Consultants' 2024 India fashion e-commerce report, Myntra, AJIO and Nykaa together capture more than 70% of online fashion-and-beauty GMV in India. They differ on seller composition, private-label depth and promotional cycle. The same Levi's jeans can sell for ₹2,499 on Myntra, ₹2,299 on AJIO and ₹2,799 on Nykaa Fashion in the same weekend, and reverse the next. For brand teams and arbitrage operators, the price delta is operational signal, not market noise.

The job-to-be-done is structured. A brand team managing 500 SKUs across all three marketplaces wants daily delta reports. A reseller arbitrage operator detects 10%+ gaps in near-real-time. A consumer-app builder ingests all three as the data layer for an India price-comparison product. A category researcher benchmarks Myntra-exclusive brands against AJIO private labels at the price-band level. All reduce to triple-marketplace daily snapshot plus SKU matching plus delta computation.

How does this compare to the alternatives?

Three options for cross-marketplace India fashion price tracking:

Indian price-comparison SaaS exists but mostly serves electronics; few cover fashion cross-marketplace at the SKU level. The three Thirdwatch actors give you the raw records to build your own analytics or consumer-facing product. See the e-commerce scraping guide for the underlying patterns.

How to track cross-marketplace fashion pricing in 4 steps

Step 1: How do I run all three actors from one script?

One Apify token works across all three actors. Fan out in parallel using concurrent.futures to keep wall-clock latency tight.

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

import os, requests
from concurrent.futures import ThreadPoolExecutor

TOKEN = os.environ["APIFY_TOKEN"]
WATCHLIST = ["levi's mens jeans", "nike air force 1",
             "lakme mascara", "maybelline lipstick"]

ACTORS = {
    "myntra": "thirdwatch~myntra-scraper",
    "ajio":   "thirdwatch~ajio-scraper",
    "nykaa":  "thirdwatch~nykaa-scraper",
}

def pull(actor_id):
    r = requests.post(
        f"https://api.apify.com/v2/acts/{actor_id}/run-sync-get-dataset-items",
        params={"token": TOKEN},
        json={"queries": WATCHLIST, "maxResults": 50},
        timeout=900,
    )
    return r.json()

with ThreadPoolExecutor(max_workers=3) as pool:
    results = dict(zip(
        ACTORS.keys(),
        pool.map(pull, ACTORS.values()),
    ))

for name, rows in results.items():
    print(f"{name}: {len(rows)} records")

Step 2: How do I normalise records across the three schemas?

Schemas align on the obvious fields but use different names. Build a tiny adapter layer.

import pandas as pd

def normalise(rows, source):
    df = pd.DataFrame(rows)
    if source == "myntra":
        df = df.rename(columns={
            "product_name": "title",
            "original_price": "mrp",
            "rating_count": "reviews",
        })
    elif source == "ajio":
        df = df.rename(columns={
            "name": "title",
            "mrp_price": "mrp",
            "review_count": "reviews",
        })
    elif source == "nykaa":
        df = df.rename(columns={
            "name": "title",
            "mrp": "mrp",
            "review_count": "reviews",
        })
    df["source"] = source
    return df[["source", "brand", "title", "price", "mrp", "rating", "reviews", "url"]]

frames = [normalise(rows, src) for src, rows in results.items()]
all_df = pd.concat(frames, ignore_index=True)
print(all_df.head())

Step 3: How do I match the same SKU across all three?

There's no shared identifier — use brand plus token-set fuzzy match.

from rapidfuzz import fuzz

def match_key(row):
    return f"{(row.brand or '').lower()} | {(row.title or '').lower()}"

all_df["key"] = all_df.apply(match_key, axis=1)
myntra = all_df[all_df.source == "myntra"]

triples = []
for _, m in myntra.iterrows():
    for other_src in ("ajio", "nykaa"):
        candidates = all_df[all_df.source == other_src]
        best, best_score = None, 0
        for _, c in candidates.iterrows():
            score = fuzz.token_set_ratio(m.key, c.key)
            if score > best_score:
                best_score, best = score, c
        if best is not None and best_score >= 85:
            triples.append({
                "brand": m.brand, "title": m.title,
                "myntra_price": m.price,
                f"{other_src}_price": best.price,
                "score": best_score,
            })

matched = pd.DataFrame(triples)
matched["myntra_vs_other_pct"] = (
    (matched.filter(regex="_price$").min(axis=1) - matched.myntra_price)
    / matched.myntra_price * 100
).round(1)
print(matched.head(15))

Step 4: How do I forward 10%+ gaps to Slack?

Persist matched triples, dedup on brand+title, alert on first detection.

import requests as r, json, pathlib, datetime

gaps = matched[matched.myntra_vs_other_pct <= -10]
for _, row in gaps.iterrows():
    msg = (f":scales: *{row.brand} {row.title[:60]}* — "
           f"Myntra ₹{int(row.myntra_price):,}, "
           f"cheapest elsewhere ₹{int(row.filter(regex='_price$').min()):,} "
           f"({row.myntra_vs_other_pct:+.1f}%)")
    r.post("https://hooks.slack.com/services/.../...",
           json={"text": msg}, timeout=10)

today = datetime.date.today().isoformat()
pathlib.Path(f"snapshots/triples-{today}.json").write_text(
    matched.to_json(orient="records"))
print(f"{len(gaps)} cross-marketplace gaps alerted")

A 10%+ gap with both legs in stock is a meaningful signal. Below that, cashback and tier-coupon noise usually accounts for the delta.

Sample output

A matched triple from the tracker looks like the example below. Each marketplace contributes one record per SKU; the matcher pairs them on brand+title.

[
  {
    "source": "myntra",
    "brand": "Levi's",
    "title": "Levi's Men's 511 Slim Fit Jeans",
    "price": 2499,
    "mrp": 3999,
    "rating": 4.4,
    "reviews": 1280,
    "url": "https://www.myntra.com/jeans/levis/.../buy"
  },
  {
    "source": "ajio",
    "brand": "Levi's",
    "title": "Levi's Men 511 Slim Fit Mid-Rise Jeans",
    "price": 2299,
    "mrp": 3999,
    "rating": 4.5,
    "reviews": 980,
    "url": "https://www.ajio.com/levis-men-511-slim-fit-mid-rise-jeans/p/..."
  },
  {
    "source": "nykaa",
    "brand": "Levi's",
    "title": "Levi's 511 Slim Fit Jeans for Men",
    "price": 2799,
    "mrp": 3999,
    "rating": 4.3,
    "reviews": 312,
    "url": "https://www.nykaafashion.com/.../p/..."
  }
]

The brand + title pair is the matching key. mrp is identical across all three because Levi's sets a single India MRP; the gap shows up in price. Per-marketplace rating and reviews are useful as a confidence signal for matching: if reviews diverge by 10x, you're likely on a colour or fit variant rather than the same SKU.

Common pitfalls

Three things go wrong in production cross-marketplace pipelines. SKU-matching false positives — fuzzy matching at 85+ score still pairs "Levi's 511" with "Levi's 512" occasionally; require brand exact-match and use reviews order-of-magnitude as a sanity check. Coupon and tier discounts — Myntra Insider, AJIO Wardrobe and Nykaa Prive give tier-based discounts that aren't reflected in the public price; cross-marketplace gaps under 10% are usually tier-coupon noise rather than real arbitrage. Sale-window blending — Myntra EORS, AJIO BIG Bold Sale and Nykaa Hot Pink Sale rarely overlap; comparing one platform in-sale against the others out-of-sale produces misleading deltas. Tag each snapshot with the in-sale flag per marketplace.

Thirdwatch's three actors share a consistent contract — same SDK call, same dataset-item shape, same per-result billing — so you spend time on analytics rather than three independent scraping integrations. Each uses production-grade anti-bot tooling tuned for the specific marketplace. Pair with Flipkart and Amazon India for horizontal-marketplace coverage. A fourth subtle pitfall worth flagging: Nykaa Fashion is a separate domain from Nykaa Beauty; for cross-fashion analysis make sure your watchlist tags products to the correct sub-marketplace.

Related use cases

• Scrape Myntra products for India fashion research
• Monitor Myntra deals and discounts
• Build an India fashion trend pipeline with Myntra
• Scrape AJIO products for India fashion pricing
• Scrape Nykaa products for beauty market research
• All Thirdwatch use-case guides