3 Credit Card Personalization Campaigns to Drive Engagement and Revenue
2026-03-07 16:36 Diff
https://www.braze.com/resources/articles/credit-card-personalization-campaigns

As credit card technology matures and mobile payments flourish, consumers have become reliant on their cards. Credit cards are already the dominant form of payment in the United States, growing steadily from 17 percent of monthly payments in 2016 to 33 percent in 2023. There is no sign of a slowdown. Transaction values will likely reach more than $3.83 trillion in 2025 with 84 percent of Gen Z relying on credit cards to fund their lifestyles and 36 percent of Millennials using credit cards at least once a day.

Synchrony notes that “as costs and size of digital storage continues to shrink, credit cards are increasingly becoming capable of storing information.” Soon, credit cards could act as apartment key fobs, access cards to neighborhood gyms, and ways to transfer money between friends without an app.

But there’s a catch. Innovation and reliance do not by themselves translate to customer loyalty. Nearly 40 percent of Millennials plan to switch credit cards in the next year, while Gen Z shoppers are increasingly relying on debit cards to make larger purchases.

In this climate, marketers will need to build engagement while reducing attrition. Consumers migrate their “share of wallet” to new cards for any number of reasons – welcome offers, balance transfers, low satisfaction. Lifecycle marketers need to stem the tide, focusing on building better relationships with customers by leveraging the data and personalizing every interaction.

In this blog post, we look at three ways credit card marketers can build loyalty with 1:1 personalization.

Personalizing the Credit Card Onboarding Process

A customer’s digital journey begins with the zero-party data they provide in their first brand interaction, such as an application for a credit card. Especially when so many consumers can easily switch cards, data-driven onboarding strategies should prioritize understanding the wants and needs of new members. Collecting zero-and first-party sets the foundation for personalized cross-sell opportunities that can engage members and increase revenue.

From the first touchpoint, marketers can build gamified onboarding campaigns. Once a customer is approved for a new card, marketers can send surveys that help define a new member’s spending preferences. That can lead to onboarding emails and push notifications encouraging new customers to complete their onboarding process–signing up for paperless billing, connecting their bank account, and completing a full online profile.

As the new member begins using their card, marketers can create automatic campaigns to celebrate a first purchase or create a countdown that shows how close a customer is to reaching their welcome reward.

1:1 personalization drives cross-sell opportunities and builds loyalty

Marketers understand the critical importance of customer engagement. Gallup research shows that when a customer feels engaged, they are more likely to open new lines of credit with a financial institution. In fact, 83 percent of consumers said they would consider a new product or service from their financial organizations if they were “satisfied and fully engaged.”

Consumers that use their card regularly, log in to their customer portal, and pay their bill on time build long-lasting relationships with the brand and their card. Engagement also correlates strongly with cross-sell and upsell opportunities, helping marketers better target consumers with enticing offers.

New technologies allow marketers to take engagement to the next level. Segmentation used to be the most personalized way of marketing to customers, but advances in AI allow every marketer to use every customer’s data to create 1:1 emails, text messages, and app notifications.

Instead of sending an email or app notification for a cash-back card with superficial personalization – like including a first name – marketers can personalize data like spending habits, showing the value of a cash-back card. Campaigns can be based on how, when, and why a customer interacts with marketing messages–when they open emails, and why they engage with notifications.

Tailored referral campaigns built on customer data

When members are satisfied and engaged with their credit card company, they are more likely to recommend the card to friends. More than 92% of consumers trust referrals from friends and family. The best customers make the best marketers.

In a 1:1 referral campaign, marketers could personalize the time of day, day of week, and outreach frequency based on a member’s engagement patterns. AI allows organizations to tailor content based on continuous testing, showing every member unique text and images based on their profile, credit score, balance, or any number of data points.

By personalizing referral campaigns, marketers show each customer they are unique and that their friends and family will also be treated like individuals.

Personalizing in the age of AI

As financial service brands work to monetize the full customer lifecycle, personalizing customer journeys like onboarding, cross-sell, and referral,, they face an inevitable question: how should we leverage new AI capabilities? AI and machine learning (ML) have rapidly evolved as essential tools in a marketer’s arsenal. One common method of using AI models to personalize are called next best action (NBA) models. On its face, it’s a simple idea – a next best action model should tell you the best action to take next with each customer. In practice, NBA models combine predictive models and business rules to predict the product to offer or action to suggest to a customer. To validate that their rules are working, marketers run A/B or multivariate tests.

As financial services companies begin to recognize the limits of traditional NBA, a new application of ML called AI Decisioning has begun to emerge. AI Decisioning relies on a different type of ML, reinforcement learning, which is best-fit for making 1:1 decisions. An AI Decisioning agent chooses the best action to take for each individual, and then autonomously experiments and continuously learns from future customer actions.

To learn more about AI Decisioning, read our whitepaper on trends in personalization in financial services.