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58match
diamondgrainbats.com
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0 co-citations6 shared tech1 shared categoryshopping
58match
diamondgrainco.com
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0 co-citations6 shared tech1 shared categoryshopping
55match
cedgroup.co.uk 🇬🇧
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0 co-citations6 shared tech1 shared categoryshopping
54match
corecontentzone.com
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0 co-citations5 shared tech1 shared categoryshopping
54match
elevatepointnet.com
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0 co-citations5 shared tech1 shared categoryshopping
52match
medleysuperstores.com
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0 co-citations5 shared tech1 shared categoryshopping
51match
assetsolution.co.uk 🇬🇧
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0 co-citations6 shared tech1 shared categoryshopping
50match
ixplus.app
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0 co-citations5 shared tech1 shared categoryshopping
50match
electrowattwise.com
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0 co-citations5 shared tech1 shared categoryshopping
50match
eletronest.com
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0 co-citations5 shared tech1 shared categoryshopping
50match
genelleamber.com
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0 co-citations4 shared tech1 shared categoryshopping
50match
cellsupply.co.uk 🇬🇧
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0 co-citations6 shared tech1 shared categoryshopping
49match
didarshop.com
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0 co-citations7 shared tech1 shared categoryshopping
49match
kellys-studio.com
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0 co-citations5 shared tech1 shared categoryshopping
48match
bazarsera.com
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0 co-citations4 shared tech1 shared categoryshopping
48match
adlinepersonalised.com
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0 co-citations4 shared tech1 shared categoryshopping
48match
countcompare.com
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0 co-citations4 shared tech1 shared categoryshopping
48match
kjsmarket.com
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0 co-citations5 shared tech1 shared categorycoupons-and-discounts

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.