How Specialized Data Improves Customer Targeting for Banks

Demand is increasing for improved third-party data to complement the investments banks and credit unions are making in first-party data. The combination provides more detailed portraits of consumers, making them more reachable. In addition, specialized data sets can help unlock personalization and acquisition strategies.

Bank marketers understand the value of demographic, behavioral and credit data to make acquisition models and media audiences more effective while uncovering and assessing new market opportunities.

Most bank marketers are supplementing their first-party data with at least one third-party data source so they can use a robust set of variables to inform their targeting and segmentation at the person, household and property level. Examples of these variables include household composition, home value, economic stability and hobbies and interests.

Traditionally, third-party data sources have been an essential ingredient in filling out the picture of prospects and customers. That remains true, but today there are a myriad of sources that can be incorporated to deliver more complete, precise and valuable customer intelligence, enabling bank marketers to:

  • Determine the offer a person can properly afford.
  • Understand the economic stressors on a household.
  • Know the vehicle a person is looking to purchase.
  • Assess how much money a person typically spends on travel.

Not all data sources are created equal, however. Partnering with a known and trusted curator of specialized and nuanced data sources can help bank marketers drive better customer intelligence by cutting through clutter and making the most meaningful enhancements to their data. Such a partner can point them in the right direction to unlock the best sources and insights to ensure they can successfully execute their brand strategy.

Bank Marketing Use Cases for Enhanced Data

Some examples of how third-party data — enhanced by specialized data sets — can help financial marketers, include:

  • Leveraging third-party card-purchase data to identify buying behaviors and create segments for card products with different value propositions (travel, foodie, etc.)
  • Using specialized addressable data to offer an auto loan directly to a person who is in the market for a particular vehicle.
  • Using consumer packaged goods (CPG) data to help financial marketers create segments of customers and prospects by interests, based on recent purchase behaviors.
  • Tapping wealth, economic and credit-based insights can help financial institutions identify people at certain affluence levels to target for investment or wealth management services.

By accessing specialized data sets, bank marketers increase the likelihood of reaching the right person with the right offer using the right tone and timing. That certainty is critical for personalization strategies. Bank and financial marketers want to ensure the “next best action” being communicated is relevant to the person receiving it.

Supplementing first-party data with curated and specialized third-party data will enable financial brands to interact in a more dynamic way with customers and prospects, allowing offers and communications to answer an action taken by a person in a certain moment.

Segmentation Requires Good Data + Analytics

Data and analytics are interdependent. Segmentation schemes and acquisition models cannot work without good data, and data can take you only so far on its own. Every collected element needs to also be connected, tested and evaluated.

Keep in Mind:

Curated third-party data is a powerful supplement that can enable financial brands to interact in a more dynamic way with customers and prospects.

For example, one financial institution whose media audiences were enhanced with people-based card swipe data, saw a significant increase in sales over its earlier approach. Such precision data strategies consistently deliver better results.

Specialized data sources can and should augment the core first- and third-party data. Additionally, establishing a rigorous evaluation and measurement strategy with clear success metrics will be critical to the successful integration of new data.

Media Performance Implications

Just as analytics and data are interrelated, so is the first-party identity graph and data. With the elimination of third-party cookies tracking, marketers have begun to embrace their first-party data strategies. The future of programmatic media execution has larger, smarter addressable input universes.

Informed by and sourced from a financial institution’s identity-fueled data layer, addressable audiences – the consumers who can be reached via targeted advertising campaigns – may be delivered to a digital signal processor (DSP) or directly to a publisher.

When more data is attributable to an individual, the likelihood of finding that person in the paid media space increases. Reach improves. This approach is a powerful one, giving brands a renewed sense of control and transparency to media execution, whether using an agency partner or an in-house team.

Personalization Implications

Marketers focused on best-in-class customer experiences will find that the more they know about their customers, the better they can anticipate needs and deliver value. Marketing decisioning engines need a constant feed of data to drive personalization models, especially if using artificial intelligence or machine learning. Without a robust data and identity foundation feeding decisioning, these engines cannot deliver a premium personalized experience.

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