Customer Linking & Identification Process
Effortlessly identify, link, and merge customer data at scale
Challenge
Clean and connected data to operate your organization at scale
Finding the same or similar customer records in mountains of customer data is easy when there is a common identifier between them, like a name, PAN, or SSN. Linking customer records, that too at scale, becomes a challenge when there is no consistent identifier.
You need to build complex rules with different weights and scores to determine matches. Based on the results, records are linked together and checked to see if they are indeed the same entity or not.
Product Overview
A Deduplication Marvel
CLIP (Customer Linking & Identification Process) is a bulk entity search and match product. What makes CLIP a marvel among other deduplication products is that it can establish linkages between customer records:
- from disparate data sources
- even when there is no overlapping identifier across data sources
- The quantity of duplicate records is large
It looks for similar demographics and other anonymous identifiers. All the while ensuring customer data privacy.
Benefits
Automates data validation, leading to quicker and more accurate verification, faster onboarding, and fewer abandonments
Consolidated, updated view of customer data across multiple products & lines of businesses and discover the network of customer relationships
Perform real-time checks for regulatory compliance and match against internal negative, defaulters, and reject lists
Cross-sell and up-sell products to old customers with personalized engagement, and identify your next best customer with better segmentation
Features
Immediate and precise results
IBM: Prime360 is available and supported on LinuxONE
Use Cases
Where CLIP helps
Why CLiP
Process at lightning speed
CLIP performs bulk AND batch dedupe, getting rid of duplicates at high speed and with precision. It utilizes recursive clustering for super-fast results and efficient data handling.
Identify and resolve redundant customer records in the tune of millions
Collates and ingests data from diverse sources
Processes data in batches or at the end-of-day while taking care of data quality
Uses powerful recursive clustering technique and gives the least false positive all at flash fast speed
The Impact
Solving current challenges, shaping the future
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