Two Companies, Twice The Customers
After an acquisition or a system migration, the same customers and vendors exist in both sets of systems under different IDs, names and formats. Until they are resolved, every report double counts and every integration guesses.
The combined company reports two customers and pays two vendors. It is one customer and one vendor in each case.
Overlap You Can’t See Until It Costs You
Every deal model assumes a number for customer overlap, cross-sell and vendor consolidation. Then integration starts, and nobody can say which of the acquired company’s customers were already yours, because the two companies never used the same identifiers.
System migrations have the same problem in miniature. Moving from one CRM or ERP to another, or merging regional instances, brings years of duplicates with it. Load them as they are and the new system starts life with the old system’s mess.
Doing it by hand takes months and is never finished: spreadsheets of possible matches, reviewed by people who do not know either customer base. Meanwhile reporting double counts, and duplicate vendors get paid twice.
Double-Counted Customers
Combined customer counts and revenue are overstated wherever the two bases overlap.
Synergies Out Of Reach
Cross-sell and vendor consolidation depend on knowing which records are the same.
Months Of Spreadsheets
Manual matching across two companies is slow, error-prone and never complete.
Resolve Before You Consolidate
Land both companies’ customer and vendor data in the same platform and resolve it as one set. Every customer and vendor that appears on both sides gets one ID, with every source record still attached.
Use the resolved set to measure overlap, plan the consolidation and load the target system. Records that arrive after cutover join the same IDs, so the clean-up does not have to be repeated.
Overlap is measured, not guessed, and both sides keep their source records.
Measure The Real Overlap
Know exactly which customers and vendors the two companies share.
One ID Across Both Sides
Each entity maps to its records in the acquirer’s and the target’s systems.
Load A Clean System
Migrate resolved entities instead of carrying old duplicates forward.
The Identity Layer Under Master Data
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.
The same layer powersCustomer Analytics & MarketingFinancial Crime & RiskPrivacy & ComplianceSupply Chain & ProcurementProduct 360AI & Data Foundations
Consolidation, Without The Spreadsheets
Matching That Adapts
Zingg learns from pairs you label across both data sets, with company-name dictionaries and exact rules on tax IDs. How Zingg matches
Scales To Millions
Blocking keeps comparisons manageable even when both customer bases are large. Scale
Persistent ZINGG_ID
Every merged entity keeps one ZINGG_ID through the migration and after it.
Keep It Clean After Cutover
The incremental flow matches new records from either side to existing IDs.
M&A And Migration Questions
How quickly can we measure customer overlap after a deal?
As soon as both data sets are in your data platform and a model is trained on a set of labelled pairs. Zingg then resolves both sets together and reports which entities appear on both sides.
Can it handle different data formats from each company?
Yes. You choose which fields to match on, and Zingg learns from labelled examples how much each field matters, even when formats and naming conventions differ.
Does the data have to leave our platform?
No. Zingg runs inside Databricks, Snowflake, Microsoft Fabric, Google Cloud or AWS.
What about records created after the migration?
Incremental runs match new and updated records to existing ZINGG_IDs, so duplicates do not creep back in.
Know What You Actually Acquired
Resolve both companies’ customers and vendors in your own data platform.