NICE Information Service is Korea’s first and largest credit bureau (CB). We process and analyze diverse credit data collected across the financial industry.

Available Credit Data and Differentiators

C-TARGET DMP provides Korea’s broadest credit-information coverage and MyData, along with diverse partner data.

Korea’s First and Largest Credit-Data Ad Tech

  • Identity Data
  • Credit Score
  • Card Data
  • Loan Data
  • Income / Occupation
  • Business Data
  • Vehicle Data
  • Rental Data
  • Real Estate Data
  • Transaction Data
  • Account Data
  • Investment Data
  • Insurance Data
  • Electronic Finance Data
  • Telecom Data

Go beyond inferred interests and behavioral estimates with real individual credit data, enabling more accurate targeting.

Conventional Ad Platforms

Men in their 30s living in Seoul with an interest in finance

  • Based on de-identified user data Behavioral data such as search and browsing history
  • Limited to de-identified or inferred data Inconsistent performance due to limited accuracy
  • Little differentiation in targeting across media platforms

C-TARGET DMP

  • Based on nationwide credit information Credit scores, income, card and loan data, and more
  • Current, verified data Timely data and accurate audience targeting
  • Exclusive data available only from NICE

Introducing C-TARGET DMP

For DA and VA campaigns on major online media, onboard advertising identifiers (ADIDs) to activate NICE credit data for tailored audience targeting.

※ Data obtained through optional consent via NICE Group companies and partners

  1. Targeting Scenario

    Combine credit-data attributes and scores to design a targeting scenario

    • Income
    • Employment
    • Loans
    • Cards
    • Real Estate
    • Vehicles
    • Rentals
    • Sole Proprietor Data

  2. Audience Sizing

    Lookalike Expansion

    Check audience size and availability for each scenario

    Expand to lookalike users when needed

    Target audience
    Core Audience
    Extract ADIDs

  3. Activate Target Audiences

    Onboard ADIDs by media platform and launch campaigns

    • NAVER
    • Facebook
    • Google
    • Instagram
    • kakao
    • YouTube

    ※ Run campaigns for at least 5–10 days after onboarding to measure performance

Case Study — Loan Comparison Platform

To increase non-bank credit-loan volume, we precisely targeted high-intent borrowers and outperformed conventional media targeting

Define Audience Requirements

Select high-intent prospects who meet non-bank credit-loan requirements

  • Credit score of 750 or higher
  • Employment information registered or annual income of at least KRW 30 million
  • Demonstrated demand for non-bank lending
  • Meets the average internal CSS criteria by platform partner and loan product
  • Exclude prospects exceeding non-bank DSR limits
  • Exclude delinquent or otherwise high-risk customers

Deliver banners for non-bank credit-loan products

Performance by Media and Product

Versus media targeting Conversions 5× higher, approval CPA 54% lower

Case Study — International NGO

For an international NGO donor-acquisition campaign, we targeted high spenders and family-oriented consumers to outperform conventional media in CTR and donation conversions

High-Spender Targeting

CTR Comparison

Versus media targeting CTR ▲ 85% increase

Cost per Conversion

Versus media targeting Cost per conversion ▼ 86% decrease

Family-Oriented Consumer Targeting

CTR Comparison

Versus media targeting CTR ▲ 150% increase

Cost per Conversion

Versus media targeting Cost per conversion ▼ 30% decrease

Introducing C-TARGET LMS

Send through NICE Information Service and the L.POINT partner channel, using diverse credit-data attributes to reach selected audiences by LMS.

  1. Set the Targeting Scenario

    Combine credit-data attributes and scores to design a targeting scenario

    • Income
    • Employment
    • Loans
    • Cards
    • Real Estate
    • Vehicles
    • Rentals
    • Sole Proprietor Data

  2. Confirm Audience Size

    Extract Target Audience

    Check audience size and availability for each LMS channel and scenario

    C-TARGET Intelligence Target audience size
    Core Audience
    Extract Audience

  3. Send LMS Messages

    Send LMS messages to the selected audience through each channel

    • NICE Information Service
    • L.POINT

Data Analytics and Consulting

Powered by Korea’s largest credit-information dataset Digital Advertising Services

Generate analysis and marketing insights with data solutions, then connect client data for credit profiling and custom CRM model consulting.

Data Analytics and Consulting
Analysis across Google Analytics, AppsFlyer, Airbridge, and other data solutions
Financial Data Consulting
Connect NICE Information Service data with internal client data to identify new credit-data scenarios and build custom CRM models
New-Customer Advertising Scenarios
Profile existing customers with credit data, then target similar prospect segments
Data-Enriched CRM Marketing Models
Add credit information to advertiser CRM models to systematize internal customer management
01

Using exclusive NICE data, diagnose client products and provide actionable feedback

Example Financial Product Diagnosis Unit: KRW million
Example financial product diagnosis, unit: KRW million
Category Inquiries Executions Total Executed Execution Rate T/S Amount Share
Peer A 0000 0000 0000 0000 0000 0000
Peer B 0000 0000 0000 0000 0000 0000
Client 0000 0000 0000 0000 0000 0000
Peer C 0000 0000 0000 0000 0000 0000
Peer D 0000 0000 0000 0000 0000 0000
  • Execution volume ranks fourth among the comparison group
  • Lower limit competitiveness than peers indicates a need to review strategy
  • Handled volume trails Peer C despite similar brand awareness and inquiry volume
  • A strong execution rate suggests focusing marketing on inquiries through rates or reward promotions

Note: This analysis is based on savings-bank credit-loan products; execution refers to a completed loan.

02

Profile customers who completed an action to clarify the client’s market position and recommend a marketing strategy

Note: Customers who completed an action are those whose loans were successfully executed.

Example Financial-Sector Customer Strategy
Credit-score distribution among customers who completed a financial action Legend: Grades 1–10
Client
Peer A
Peer B
Peer C
Peer D
Peer E

The client has fewer high-credit customers than peers,
indicating a need for acquisition strategies focused on prime customers.

03

Customer profiling data can also guide advertising creative strategy

Example Financial Product Diagnosis Unit: KRW million

Primary Loan Amounts

All Customers KRW 5 million or less (small loans) Men in their 40s–50s earning under KRW 40 million KRW 20 million or less

Loan Usage Patterns

Capital Finance Usually one account Average loan: KRW 13 million Savings Bank
One to three accounts Primarily male customers
Many multiple-debt customers in their 40s Average loan: KRW 8.9 million

Core Audience

Women in their 30s–40s Men in their 30s–50s Annual income under KRW 40 million

Card Usage Patterns

30s Card Loans
40s Installments Card Loans Cash Advances
50s Card Loans Cash Advances
Debt Refinancing

Emphasize messaging that can attract card-loan customers, who account for a high share of small loans.

Primary Loan Amounts

  • All Customers
  • Small loans of KRW 5 million or less
  • Men in their 40s–50s earning under KRW 40 million
  • KRW 20 million or less

Loan Usage Patterns

  • Capital Finance
  • Usually one loan account
  • Average loan: KRW 13 million
  • Savings Bank
  • One to three loan accounts
  • Primarily male customers
  • Many multiple-debt customers in their 40s
  • Average loan: KRW 89 million
03

Core Audience

Core Audience
  • Women ages 30–40
  • Men ages 30–50
  • Annual income under KRW 40 million
Card Usage Patterns
30s
  • Card Loans
40s
  • Installments
  • Card Loans
  • Cash Advances
50s
  • Card Loans
  • Cash Advances

Emphasize messaging that can attract card-loan customers, who account for a high share of small loans.