Piperic Business Intelligence

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도향촌 · ranked by category · tech stack · backlink co-citation

misoricecake.com

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greenbeanmall.com

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4 co-citations1 shared tech0 shared categoryfood-and-drink

maisonmoshop.com

3 co-citations2 shared tech1 shared categoryfood-and-drink

광덕산호두과자

3 co-citations2 shared tech1 shared categoryfood-and-drink

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BAKING MORE_베이킹모어

1 co-citations2 shared tech1 shared categorydesserts-and-baking

d&b 베이킹몰 베이킹의 모든것! 베이킹 쇼핑몰

0 co-citations2 shared tech1 shared categorydesserts-and-baking

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맛있는 빵!! 생산, 대량구매 전문 "엔젤브레드"입니다.

4 co-citations0 shared tech1 shared categorydesserts-and-baking

1 co-citations2 shared tech1 shared categoryfood-and-drink

3 co-citations2 shared tech0 shared categoryfood-and-drink

베이루아 홈페이지 - 프리미엄 생식빵 전문점

2 co-citations1 shared tech1 shared categorydesserts-and-baking

4 co-citations0 shared tech1 shared categorydesserts-and-baking

3 co-citations0 shared tech1 shared categorydesserts-and-baking

집밥의 따듯함, 외할머니의 사랑 MAMAS MOM

4 co-citations2 shared tech0 shared categoryfood-and-drink

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

co-citations how often the same third-party websites link out to both — sites referenced together tend to serve the same world.
shared tech how much of their technology stack (CMS, e‑commerce, analytics, frameworks…) overlaps.
shared category whether they sit in the same business / industry category.
positioning how closely their titles and on‑page wording line up.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.