Piperic Business Intelligence

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fernwoodcabin.com

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2 co-citations2 shared tech2 shared categoryhotels-and-motels

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2 co-citations2 shared tech2 shared categoryhotels-and-motels

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2 co-citations2 shared tech1 shared categoryhotels-and-motels

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2 co-citations2 shared tech1 shared categoryhotels-and-motels

greenwoodcabin.com

2 co-citations2 shared tech1 shared categoryhotels-and-motels

bamboooasisorlando.com

2 co-citations2 shared tech1 shared categoryhotels-and-motels

bookcozyonwalnut.com

2 co-citations2 shared tech1 shared categoryhotels-and-motels

elevatedescapevilla.com

2 co-citations2 shared tech1 shared categoryhotels-and-motels

1 co-citations2 shared tech2 shared categoryhotels-and-motels

malaazstays.com

1 co-citations2 shared tech2 shared categoryhotels-and-motels

1 co-citations2 shared tech2 shared categoryhotels-and-motels

bookeurekacabins.com

Eureka Riverside Resort

3 co-citations2 shared tech2 shared categoryhotels-and-motels

1 co-citations2 shared tech2 shared categoryhotels-and-motels

apataliving.com

2 co-citations2 shared tech1 shared categoryhotels-and-motels

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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.