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65match
afternoonliving.com
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0 co-citations3 shared tech2 shared categoryshopping
65match
afternoonco.com
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0 co-citations3 shared tech2 shared categoryshopping
65match
afternoonshop.com
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0 co-citations3 shared tech2 shared categoryshopping
65match
afternoonhouse.com
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0 co-citations3 shared tech2 shared categoryshopping
65match
corezbeauty.com
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0 co-citations3 shared tech2 shared categoryshopping
65match
cosystores.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
allbeautifully.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
0rbittechnology.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
baoebarato.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
cavinni.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
cecabe.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
cedroo.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
celestelon.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
celletek.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
cosykin.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
dibealfamilypet.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
elancarmelle.com
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0 co-citations3 shared tech2 shared categoryshopping
58match
eleanorerose.com
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0 co-citations3 shared tech2 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.