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59match
ccamma.com
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0 co-citations4 shared tech2 shared categorymarketing-and-advertising
55match
julie-ette.com
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
jacksonmanage.com
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
janescandurra.com
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
hattrick3c.com
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
hobcreative.com
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0 co-citations4 shared tech2 shared categorymarketing-and-advertising
55match
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
cornerstonerep.com
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
elevonmarketingsolutions.com
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
55match
elisechandonince.com
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0 co-citations4 shared tech1 shared categorymarketing-and-advertising
52match
joenafziger.com
Joe Nafziger - Home
0 co-citations4 shared tech2 shared categorymarketing-and-advertising
52match
heyyoumedia.com
HEY YOU MEDIA - Home
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52match
brand-fox.com
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0 co-citations4 shared tech2 shared categorymarketing-and-advertising
52match
graphicsource1.com
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52match
greenadagency.com
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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:

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.