Monitor AJIO Brand Catalog Changes Daily for Indian Fashion

Thirdwatch's AJIO Scraper returns dedupable, sku-stamped product rows that make AJIO catalog monitoring straightforward — daily snapshots, set differences, MRP edit detection, and stockout vs delisting separation. Built for India fashion ops teams tracking new launches and OOS, brand teams monitoring AJIO private-label drops, and analysts watching for unauthorised resellers or counterfeit listings appearing in the catalog.

Why monitor AJIO catalog changes

AJIO's catalog moves daily. According to the India Brand Equity Foundation 2024 fashion report, Indian online fashion brands launch on average 2 to 4 new SKUs per week per category, and AJIO's Reliance-backed private-label brands (AJIO Own, Netplay, Teamspirit, Performax) operate at the higher end of that range. End-of-season-sale windows compress months of churn into days — SKUs appear, sell out, get re-listed at a new MRP within a single week.

For brand ops, marketplace ops, and competitive intelligence teams, that churn is operational signal. The job-to-be-done is structured. A brand ops team tracking 8 competitor brands wants a daily Slack digest of new launches. A marketplace ops team flagging unauthorised resellers needs to detect when an unknown seller's SKU appears under their brand. An analyst watching Reliance's private-label strategy wants the launch-velocity curve per house brand. A dropshipper looking for trending high-discount categories tracks discount_percent moves across whole verticals. All reduce to daily snapshots, sku-keyed dedup, and set-difference logic.

How does this compare to the alternatives?

The AJIO seller dashboard only exposes your own listings, not competitors'. There is no public competitor-catalog feed. The AJIO Scraper actor gives you the full public catalog at pay-per-result, suitable for daily competitor-brand monitoring.

How to monitor AJIO catalog changes in 5 steps

Step 1: How do I authenticate against Apify?

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I snapshot a brand's full catalog daily?

Run the actor with the brand name as the query and maxResults set high enough to capture the full visible catalog.

import os, json, datetime, pathlib
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

BRANDS = ["AJIO Own", "Netplay", "Teamspirit", "Performax",
          "Levi's", "Allen Solly", "Biba", "HRX"]

run = client.actor("thirdwatch/ajio-scraper").call(run_input={
    "queries": BRANDS,
    "sortBy": "newest",
    "maxResults": 1000,
})

records = list(client.dataset(run["defaultDatasetId"]).iterate_items())
today = datetime.date.today().isoformat()
pathlib.Path("snapshots").mkdir(exist_ok=True)
pathlib.Path(f"snapshots/ajio-{today}.json").write_text(json.dumps(records))
print(f"{today}: {len(records)} SKUs across {len(BRANDS)} brands")

Sort by newest so the most recent launches appear first within each brand's slice — useful when AJIO truncates very large brand catalogs.

Step 3: How do I diff today's snapshot against yesterday?

Use sku as the natural key. SKUs in today but not yesterday are new launches; SKUs in yesterday but not today are stockouts or delistings.

import pandas as pd, glob

paths = sorted(glob.glob("snapshots/ajio-*.json"))
yest_path, today_path = paths[-2], paths[-1]

yest = pd.DataFrame(json.loads(pathlib.Path(yest_path).read_text()))
today = pd.DataFrame(json.loads(pathlib.Path(today_path).read_text()))

yest_skus = set(yest.sku.astype(str))
today_skus = set(today.sku.astype(str))

new_launches = today[today.sku.astype(str).isin(today_skus - yest_skus)]
dropped = yest[yest.sku.astype(str).isin(yest_skus - today_skus)]

print(f"New launches: {len(new_launches)}")
print(f"Dropped / OOS: {len(dropped)}")
print(new_launches[["brand", "product_name", "price", "discount_percent",
                    "category", "url"]].head(20))

For most brands a daily diff returns 5 to 40 new launches and a similar number of drops — comfortably digestible as a Slack digest.

Step 4: How do I detect MRP edits and discount moves on retained SKUs?

Inner-join yesterday and today on sku, then compare original_price and discount_percent.

retained = today.merge(yest, on="sku", suffixes=("_today", "_yest"))

mrp_edits = retained[
    retained.original_price_today.notna()
    & retained.original_price_yest.notna()
    & (retained.original_price_today != retained.original_price_yest)
]

discount_starts = retained[
    (retained.discount_percent_yest.fillna(0) < 30)
    & (retained.discount_percent_today.fillna(0) >= 30)
]

print(f"MRP edits: {len(mrp_edits)}")
print(f"Discount started (crossed 30%): {len(discount_starts)}")

MRP edits without a discount change typically signal a permanent price reposition — useful for brand ops to flag. Discounts crossing the 30% threshold are the canonical "sale started" signal worth Slack-alerting brand managers.

Step 5: How do I separate stockout from delisting?

For SKUs that disappeared today, attempt a direct URL fetch with httpx (or any HTTP client) and check the response status.

import httpx

def classify_drop(url: str) -> str:
    try:
        r = httpx.get(url, follow_redirects=True, timeout=10.0)
        if r.status_code == 404:
            return "delisted"
        if "currently unavailable" in r.text.lower() or "out of stock" in r.text.lower():
            return "out_of_stock"
        return "still_live_but_unlisted"
    except Exception:
        return "fetch_error"

dropped["status"] = dropped.url.apply(classify_drop)
print(dropped.status.value_counts())

delisted is the operationally interesting bucket — a SKU permanently leaving the catalog often signals brand-relationship change, regulatory issue, or stock burn. out_of_stock is normal churn.

Sample output

A daily-diff digest record looks like this — flat rows ready for Slack or a BI tool.

[
  {
    "event": "new_launch",
    "date": "2026-05-12",
    "sku": "470121983",
    "brand": "Netplay",
    "product_name": "Slim Fit Solid Casual Shirt",
    "price": 899,
    "original_price": 1799,
    "discount_percent": 50,
    "category": "Men > Topwear > Casual Shirts",
    "url": "https://www.ajio.com/p/470121983"
  },
  {
    "event": "dropped",
    "date": "2026-05-12",
    "sku": "468023411",
    "brand": "AJIO Own",
    "product_name": "Round Neck Printed Tshirt",
    "last_seen_price": 449,
    "url": "https://www.ajio.com/p/468023411",
    "status": "out_of_stock"
  },
  {
    "event": "discount_started",
    "date": "2026-05-12",
    "sku": "469712238",
    "brand": "Levi's",
    "product_name": "511 Slim Fit Mid Rise Stretchable Jeans",
    "discount_yest": 20,
    "discount_today": 45,
    "price_today": 2199,
    "url": "https://www.ajio.com/p/469712238"
  }
]

event is the natural facet for routing into different Slack channels (#launches, #stockouts, #sale-watch). sku is the stable join key for time-series analytics in your BI layer.

Common pitfalls

Three things go wrong in production AJIO catalog monitors. Pagination truncation — AJIO caps listing depth around 4,000–5,000 results per query; for very large brand catalogs (AJIO Own across all categories) you'll need to shard your queries by sub-category to capture the full tail. False-positive drops — a SKU can fall off the listing API for a single day due to AJIO's internal indexing without being out of stock. Use a 2-day rolling window (drop only counts if absent 2 days in a row) to suppress that noise. Brand-name normalisation — AJIO writes "Levi's" with an apostrophe, "AJIO Own" with a capital JIO; lower-case and strip non-alphanumerics before joining brand-keyed snapshots.

Thirdwatch's actor handles AJIO's anti-bot defences and proxy rotation transparently. A 5,000-SKU brand snapshot completes in well under ten minutes — small enough to run multiple snapshots per day during sale events without infrastructure overhead.

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