Zero-, First-, Second- and Third-Party Data in Banking: How Banks Can Make the Most of Their Data Gold

The Most Important Customer Data in Banking – First-, Second-, Third- and Zero-Party Data at a Glance

In modern banking and among credit card issuers, precise customer data is the key to successful personalization, customer segmentation, and targeted marketing campaigns. But not all data is created equal. The distinction between first-party data, second-party data, third-party data, and zero-party data is crucial for using data sources correctly, meeting compliance requirements, and achieving maximum business value.

This guide provides a clear definition of each data category, shows typical use cases in the financial sector, and offers best practices for banks looking to optimize data quality and utilization.

1. First-Party Data – Your Own Customer Data

Definition: First-party data is data that comes directly from the bank's or card issuer's own relationship with the customer. It's generated through proprietary channels such as online banking, mobile apps, customer service, branch visits, or transactions.

Examples in banking:

Transaction history (e.g. payments, standing orders, card spend)

Account information (balance, account type)

Online banking logins and usage behavior

Responses to email campaigns or push notifications

Advantages:

Highest accuracy and timeliness

GDPR-compliant use (with proper consent)

Direct control over data collection and maintenance

Best practice for banks: Use first-party data for customer value analysis (customer lifetime value), churn prevention, and targeted cross-selling offers (e.g. credit card upgrades based on spending behavior).

2. Second-Party Data – Data Partnerships in the Financial Sector

Definition: Second-party data is a partner's first-party data that is shared through a direct agreement. In banking, these partners can be partner banks, co-branded credit card partners, or insurers.

Examples in banking:

Data from co-branded credit card programs (e.g. airline or retail partnerships)

Payment information from merchant banks (acquirers)

Customer preferences from partner programs

Advantages:

Higher data quality than third-party data

Access to expanded customer insights without relying on anonymous mass sources

Best practice for banks: Second-party data is well suited to optimizing joint loyalty programs or precisely selecting target audiences for partner campaigns.

3. Third-Party Data – External Market Data

Definition: Third-party data is collected by external data providers and sold to banks or financial service providers. It doesn't originate from a direct customer relationship.

Examples in banking:

Socio-demographic data from market research institutes

Location and movement data from app networks

Industry information about merchants

Advantages:

Fast scaling of target audiences

Enriches first-party data with market and environmental context

Risks:

Lower accuracy

Higher GDPR compliance risk

Increasing restrictions from data protection laws

Best practice for banks: Use third-party data only selectively — for example, for market-entry analysis or campaigns in new regions — and always validate it against first-party data.

4. Zero-Party Data – Customers Volunteer Information

Definition: Zero-party data is information customers voluntarily provide, collected through surveys, profile entries, or interactive tools.

Examples in banking:

Product preferences (e.g. "I'm interested in sustainable investments")

Feedback on banking services

Self-entered savings goals or financial plans in the banking app

Advantages:

Maximum relevance for personalized offers

Direct customer consent

A valuable complement to transaction data

Best practice for banks: Collect zero-party data specifically during onboarding and existing-customer campaigns to tailor offers to individual life situations.

Table: Comparing Data Types in Banking

Data type

Source

Accuracy

Privacy risk

Banking examples

Zero-Party Data

Voluntary customer input

Very high

Very low

Preferences, feedback

First-Party Data

Own customer channels

High

Low

Transactions, logins

Second-Party Data

Partner companies

High

Medium

Co-branding data

Third-Party Data

External providers

Medium

High

Market data, location

The Comprehensive Banking Data List by Category

Abbreviation key: ZP = Zero-Party | 1P = First-Party | 2P = Second-Party | 3P = Third-Party

Core Customer Data

Name, date of birth, gender (1P)

Contact details including opt-ins (1P)

Preferred language & contact channel (ZP)

Preferred branch/advisor (ZP)

Profile & Preferences

Savings goals (ZP)

Investment horizon, risk profile (ZP)

Sustainability preferences for investments (ZP)

Interest in products (e.g. mortgage financing, brokerage account) (ZP)

Travel plans for credit card limits/geo-blocking (ZP)

Demographics & Household

Marital status, household size (ZP/3P)

Place of residence, postal code cluster (1P)

Income bracket (1P/3P modeled)

Employment status (ZP/1P)

Transaction Data – The Gold of Banking

Individual account movements: date, time, amount, payee/payer (1P)

Credit card transactions: amount, merchant name, MCC (merchant category code) (1P)

Standing orders & direct debits (1P)

Cash withdrawals & deposits (1P)

POS vs. e-commerce usage (1P)

Spending abroad (1P)

Spend volume per category (modeled from MCC) (1P)

Credit line utilization, overdraft frequency (1P)

Chargebacks & returned payments (1P)

Transaction frequency & intervals (1P)

Digital Usage

Login frequency for mobile/app/online banking (1P)

Features used (e.g. multi-banking, transfer templates) (1P)

Drop-off points in application processes (1P)

Self-service tools vs. contact requests (1P)

Feature wish list (ZP)

CRM & Customer Service

Advisory appointments & topics (1P)

Complaints & inquiries (1P)

Satisfaction scores (NPS/CSAT) (ZP)

Reasons for cancellation (ZP/1P)

Service channel preferences (ZP)

Partner & Loyalty Data

Co-brand program participation (1P/2P)

Points, miles, status level (1P/2P)

Redemption behavior (1P/2P)

Partner transactions (e.g. purchases with airline partners) (2P)

Corporate Customer Data

Industry (1P/3P)

Company size (1P/3P)

Payment behavior of business accounts (1P)

Credit and guarantee volume (1P)

Payment flows by region/country (1P)

Risk & Compliance

Scoring values from internal models (1P)

KYC data & identification documents (1P)

PEP and sanctions list checks (1P/3P)

AML alerts (1P)

Fraud patterns (1P)

Conclusion – Data Strategy in Banking

A successful data strategy for banks and card issuers is built on a first-party-first approach: maximize your own data sources, use second-party data partnerships selectively, critically vet third-party data, and integrate zero-party data as a premium addition for genuine personalization.

Banks that deploy their customer data in a structured, compliant way not only earn greater customer trust but also significantly boost the ROI of their marketing and CRM initiatives.

Do you feel you could be putting your data to better use? Contact us for a free consultation!