Why Customer Data Platforms Alone Can’t Drive the Banking Growth Leaders Expect

As customer expectations rise, financial institutions are turning to advanced technologies like Customer Data Platforms (CDPs) to better understand and engage their customers. However, while CDPs are powerful tools for data unification, they often fall short when it comes to driving real, sustainable growth.

This article delves into why CDPs alone cannot fuel growth in banking and provides insights into how financial institutions can bridge this gap by incorporating real-time intelligence and automated execution into their technology stacks.

What Is a Customer Data Platform (CDP)?

A Customer Data Platform (CDP) is a technology that consolidates customer data from various sources into a single, unified customer profile. By pulling data from multiple touchpoints, such as websites, mobile apps, CRM systems, and more, a CDP provides a holistic view of each customer. This enables organizations to better understand their customers and deliver personalized experiences at scale.

CDPs clean and organize customer data, enabling banks to create a reliable foundation for segmentation, targeting, and customer insights. The core functions of a CDP include:

• Data unification: Aggregates data from various sources into a single view.
• Data cleaning: Removes inconsistencies and errors to ensure the data is accurate and reliable.
• Customer segmentation: Organizes customers into segments based on shared characteristics or behaviours.
• Personalization: Helps banks tailor their marketing and engagement efforts to individual customers.

While this foundation is crucial for any data-driven strategy, data alone is not enough to drive significant growth in the banking sector.

Why CDPs Alone Fall Short in Delivering Growth

Despite their benefits, CDPs often fail to deliver the expected growth in banking and financial services. The problem lies in the fact that CDPs are designed to store and organize data, not to act on it in real time. The real growth in banking comes from understanding and responding to customer behaviour at the right moment, which requires more than just data storage.

Let’s explore why CDPs fall short:

1. Data Collection vs. Real-Time Action

• What CDPs do: They aggregate customer data and create unified profiles. This gives banks an organized, comprehensive view of customer activity and behaviors over time.
• What’s missing: CDPs do not analyze this data in real time or trigger immediate actions based on changes in customer behavior. They capture information about a customer’s past actions, but they do not enable banks to respond at the moment those behaviors shift.

Example: If a customer’s salary is deposited into their account, this moment is ideal for promoting savings or investment products. However, CDPs don’t automatically trigger this action; they simply log the transaction.

2. Lack of Predictive Analytics

• CDPs provide a historical view of customer data, but they cannot predict future behaviors or customer needs. Predictive analytics are necessary for anticipating what a customer is likely to do next – whether that’s applying for a loan, transferring money, or making a purchase.

Example: A new credit card customer may show early signs of low engagement, but a CDP will not highlight this early enough to prompt timely intervention. Banks need real-time insights to engage the customer before the habit becomes ingrained.

3. Inability to Automate Real-Time Engagement

• Growth depends on automated, personalized engagement based on real-time data signals. While CDPs provide segmentation, they do not automate interactions across different communication channels (e.g., email, SMS, app, website).

Example: If a high-value customer begins transferring money out of the bank, this signals potential churn. A CDP logs this event but does not trigger the appropriate action (e.g., notifying a relationship manager or sending a retention offer) to address the issue before it becomes a lost customer.

What Banking Leaders Really Expect from Their CDP

Banking executives typically have higher expectations for their Customer Data Platforms. They hope to achieve more than just a unified view of customer data.

Here are some key capabilities that C-suite leaders expect from a CDP:

1. Real-Time Opportunity Detection: Banks want systems that can detect customer behaviour changes immediately and respond accordingly.
2. Proactive Needs Anticipation: Rather than waiting for customers to initiate interactions, executives want to anticipate customer needs and offer solutions before they are explicitly asked for.
3. Automated Customer Engagement: Banks expect systems that can trigger personalized, timely actions across multiple communication channels (e.g., SMS, email, push notifications, etc.).
4. Predictive Customer Insights: The ability to predict customer behaviour – such as when they are likely to make a purchase, open a savings account, or withdraw funds – is critical for maximizing customer engagement.

Despite these expectations, CDPs typically do not provide the necessary features to fulfill these demands. This is where real-time intelligence and automated execution come into play.

Why Data Alone Doesn’t Create Banking Growth

Growth in banking is driven by real-time context and intelligent actions. While a CDP can help banks organize and segment customer data, it cannot fully capitalize on customer behaviour as it unfolds in real-time.

Consider the following examples of moments where growth opportunities arise:

• Salary Deposits: When a customer’s salary hits their account, this is the ideal time to nudge them toward savings or investment products.
• Balance Drops After Transfer: A sudden drop in a customer’s balance after a large transfer is an opportunity for the bank to engage with a retention offer or loan suggestion.
• Low Credit Card Adoption: When a new credit card shows signs of low usage, this moment presents an opportunity for personalized promotions to encourage more usage.

These micro-moments are where growth occurs. However, a CDP only captures these moments after they happen, missing the opportunity to act at the crucial point when the customer is most receptive.

A Multi-Layered Approach for Banking Growth

To achieve real growth, banks need to combine the insights from a CDP with two additional layers: real-time intelligence and automated execution. Here’s how this multi-layered approach works:

1. Layer 1: Clean Data

• A CDP provides the clean, unified customer data that serves as the foundation for growth. However, data alone is not sufficient for growth in banking.

2. Layer 2: Real-Time Intelligence

• Banks need intelligent systems that can analyze customer behaviour in real time, predict future actions, and identify key opportunities. This layer involves behavioural analysis, predictive modelling, and identifying signals that suggest a customer’s intent.

3. Layer 3: Automated Execution

• The final layer is execution: triggering the right actions, at the right time, across the right channels. This layer ensures that insights are converted into real-time engagement – whether through SMS, email, apps, or other channels.

Together, these layers enable banks to respond to customer needs as they arise and take immediate action to drive growth.

How This Approach Works in Practice

Here are some real-world examples of how this multi-layered approach drives banking growth:

• Salary Credit:
  • CDP logs the salary credit.
  • Real-Time Intelligence: The system triggers a savings or investment recommendation at the moment the salary is deposited.
  • Outcome: The customer is nudged to save, boosting engagement and financial health.
• Low Credit Card Usage:
  • CDP adds the customer to a segment for low usage.
  • Real-Time Intelligence: The system detects the early signs of low usage and sends a personalized offer or reminder to the customer.
  • Outcome: The customer increases their credit card usage.
• Potential High-Value Customer Churn:
  • CDP logs changes in the customer’s account balance.
  • Real-Time Intelligence: The system detects signs of potential churn and alerts the relationship manager (RM) to take action.
  • Outcome: The relationship manager proactively reaches out to retain the customer before they leave.

How Real-Time Intelligence Platforms Complete the CDP

While CDPs are necessary for creating a unified customer profile, they lack the real-time intelligence and automation needed to drive engagement and growth. This is where advanced platforms like VARTA come in.

VARTA works on top of existing data systems (like CDPs), adding the missing layer of real-time intelligence and automation. It can:

• Analyze transaction signals in real time.
• Predict future customer actions.
• Trigger personalized engagement at the right moment, across the right channels.

Instead of relying on manually-created campaigns, VARTA automates the entire customer journey, turning static insights into dynamic actions that drive growth.

Conclusion: Why Growth Requires More Than a CDP

Investing in a Customer Data Platform was a step in the right direction for many banks. However, to move the needle on growth, banks must go beyond the data. The missing layer is real-time intelligence and automated execution.

If your CDP isn’t delivering the growth outcomes you expected, the issue likely isn’t with the platform itself, but with the lack of real-time decision-making that transforms data into actionable insights. By combining the power of a CDP with real-time intelligence and automation, banks can finally unlock the growth they’ve been searching for.

If you’re ready to take the next step, we can help you explore how this multi-layered approach can drive meaningful impact for your institution. Book a demo today!

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