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100match
ballle.com
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5 co-citations3 shared tech1 shared categoryshopping
77match
crampicks.com
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2 co-citations3 shared tech1 shared categoryshopping
75match
getdiso.com
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3 co-citations3 shared tech1 shared categoryshopping
72match
majjri.com
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2 co-citations3 shared tech1 shared categoryshopping
70match
holyposse.com
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3 co-citations3 shared tech1 shared categoryshopping
69match
heydayriot.com
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2 co-citations3 shared tech1 shared categoryshopping
69match
bombeirosmap.com
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2 co-citations3 shared tech1 shared categoryshopping
68match
homeandkidsdeals.com
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2 co-citations3 shared tech1 shared categoryshopping
65match
getabbe.com
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2 co-citations3 shared tech1 shared categoryshopping
65match
jmrdigitalsolutions.com
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3 co-citations3 shared tech1 shared categoryshopping
65match
balancedgridco.com
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0 co-citations3 shared tech1 shared categoryshopping
65match
laperchaboutique.com
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2 co-citations3 shared tech1 shared categoryshopping
65match
bougrecandles.com
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2 co-citations3 shared tech1 shared categoryshopping
65match
medogtea.com
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2 co-citations3 shared tech1 shared categoryshopping
64match
jascollections.com
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1 co-citations3 shared tech1 shared categoryshopping
64match
advipartners.com
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1 co-citations3 shared tech1 shared categoryshopping
64match
cerealviking.com
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1 co-citations3 shared tech1 shared categorychildren-s-games-and-toys
64match
genevievestevens.com
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1 co-citations3 shared tech1 shared categoryshopping

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.