Three Catalogs, One Mailbox
Duplicate contacts cost money twice: once for every extra catalog, coupon and email, and again when the customer notices. Personalization suffers too, because each duplicate only knows part of what the customer has bought.
Three catalogs to one person, and a “we miss you” coupon to someone who bought last week.
Duplicates Cost Every Campaign
Marketing lists are built from many sources: e-commerce, stores, events, lead forms and purchased lists. Each adds the same people again, with a nickname, a new email or an apartment number the others do not have.
The waste is easy to see in print: several catalogs to one home. It is harder to see in personalization. Each duplicate holds part of the customer’s history, so recommendations, suppression lists and win-back campaigns act on half the picture.
Simple deduplication on email or postal address catches exact repeats and misses nicknames, moved customers and household variations.
Wasted Spend
Print, postage and paid media are spent several times on one person.
Wrong Offers
Customers get win-back offers, welcome discounts or repeat promotions they should not.
Partial Personalization
Recommendations use one duplicate’s history instead of the customer’s.
Resolve Contacts Before Every Send
Resolve the people on every list against your customer base before a campaign goes out. Match on names, nicknames, addresses and contact details together, and build each audience from resolved people, not rows.
Use the same resolved profile for personalization, so offers and suppressions are based on everything a customer has done.
One catalog, and offers based on everything she has bought.
One Contact Per Person
Every list member is resolved to one person before the send.
Accurate Suppression
Recent buyers and opted-out people are suppressed across every duplicate.
Complete Profiles
Personalization reads the customer’s full history.
The Identity Layer Under Customer Analytics
Zingg runs as a workload inside your data platform and gives every entity one persistent ZINGG_ID. Everything above it reads the same resolved entities.
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Deduplication That Understands Names
Nicknames Built In
Maggie and Margaret, Bill and William: Zingg Enterprise matches names through nickname dictionaries. How Zingg matches
Learned And Exact Together
Fuzzy matching trained on your labels, combined with exact rules on email or loyalty ID.
Persistent ZINGG_ID
The same person keeps one ID across lists and campaigns. ZINGG_ID
Scales To Millions
Large lists are matched without comparing every pair. Scale
Marketing Deduplication Questions
Why do marketing lists have so many duplicates?
They combine many sources, each with its own spelling, email and address for the same person.
Can Zingg match nicknames?
Yes. Zingg Enterprise includes nickname matching, so Maggie and Margaret can be recognized as the same person.
Does Zingg send campaigns?
No. Zingg resolves contacts in your data platform. Your marketing tools send to the resolved audience.
Will it merge people who share an address?
Not on address alone. Zingg learns from your labels which combinations of fields mean the same person, and you can review the evidence for each match.
Reach Each Customer Once
Resolve contacts inside your own data platform before every campaign.