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100match
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1 co-citations2 shared tech2 shared categorymarketing-and-advertising
100match
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1 co-citations2 shared tech2 shared categoryadvertising-industry
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hhhagency.com
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1 co-citations2 shared tech2 shared categorymarketing-and-advertising
90match
marklivity.com
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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1 co-citations2 shared tech1 shared categoryadvertising-industry
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1 co-citations2 shared tech1 shared categoryadvertising-industry
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3ndigital.com
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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advancedanalytics.tech
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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adtraverse.com
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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apexhubagency.com
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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apoint7.com
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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appetas.com
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1 co-citations2 shared tech1 shared categorybusiness
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cpgsys.com
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising
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germansuarez.com
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1 co-citations2 shared tech1 shared categorymarketing-and-advertising

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.