JCPenney

JCPenney Reviews API

Real customer review text for one jcpenney.com product by id or URL, up to 98 reviews per call (default 38), sorted most recent (default), most helpful, highest rating, lowest rating or relevance, optionally only one star rating. Returns the product's review statistics (total_reviews in every language, displayed_reviews as JCPenney lists them, average rating, the 1-5 star distribution, recommended percent, Quality, Value and Apparel averages, photo count, and matching_reviews for the sort and star filter) and each review with rating, title, text, nickname, location, dates, recommended, helpful and unhelpful votes, verified-purchaser and incentivized badges, the syndication source, photos, videos, secondary ratings and the brand's public response. Takes longer than other JCPenney calls: usually 15 to 90 seconds, so allow 240. Costs 1 credit per request.

POST/api/v1/jcpenney/reviews

Authorizations

Authorizationstringheaderrequired

Bearer authentication header of the form Bearer <token>, where <token> is your Scavio API key (e.g. Bearer sk_live_your_key).

Body

application/json

product_idstring

A JCPenney product id (ppr / ppm / pp + digits): a search result's id, the last part of a product URL. One of product_id or url is required.

Example: ppr5008604049

urlstring

A jcpenney.com product URL (its product id is used). Alternative to product_id.

Example: https://www.jcpenney.com/p/st-johns-bay-womens-mid-rise-straight-leg-jean/ppr5008604049

sortstring

most_recent (default), most_helpful, highest_rating, lowest_rating or relevance: the review list's own sort menu.

Example: most_helpful

ratinginteger

Only reviews with this star rating, 1-5. A rating nobody gave returns an empty list.

Example: 1

max_reviewsinteger

Reviews to return, 8-98. Defaults to 38. JCPenney loads 8, then 30 more per page, so 8, 38, 68 and 98 are the natural sizes; larger values take longer.

Example: 98

Request

from scavio import ScavioClient

client = ScavioClient(api_key="sk_live_your_key")

# The 38 most helpful reviews. This call takes 15-90 s; allow 240 s.
res = client.jcpenney.reviews(product_id="ppr5008604049", sort="most_helpful")["data"]

print(res["total_reviews"], "reviews,", res["average_rating"], "stars,", res["recommended_percent"], "% recommend")
for r in res["reviews"]:
    print(r["submitted_at"][:10], r["rating"], r["verified_purchaser"], r["incentivized"], r["title"])

Response

"product_id": "ppr5008604049",

"total_reviews": 163,

"displayed_reviews": 163,

"average_rating": 4.04,

"recommended_percent": 78,

"photo_count": 3,

"sort": "most_helpful",

"rating_filter": null,

"matching_reviews": 163,

"count": 38,

"response_time": 154084

}

from scavio import ScavioClient client = ScavioClient(api_key="sk_live_your_key") # The 38 most helpful reviews. This call takes 15-90 s; allow 240 s. res = client.jcpenney.reviews(product_id="ppr5008604049", sort="most_helpful")["data"] print(res["total_reviews"], "reviews,", res["average_rating"], "stars,", res["recommended_percent"], "% recommend") for r in res["reviews"]: print(r["submitted_at"][:10], r["rating"], r["verified_purchaser"], r["incentivized"], r["title"])