Digital banking marketers understand the value of demographic, behavioral, and credit data in making acquisition models and media audiences more effective while identifying and evaluating new market opportunities.
Most bank marketers supplement their proprietary data with at least one third-party data source to use a robust set of variables to inform their targeting and segmentation at the person, household and property levels. Examples of these variables include household composition, home value, financial stability, and hobbies and interests.
Traditionally, third-party data sources have been an essential component to complete the picture of leads and customers. This is still true, but today there are many sources that can be leveraged to provide more comprehensive, accurate and valuable customer intelligence, allowing digital banking marketers to:
- Determine the offer that a person can reasonably afford.
- Understand financial pressures at home.
- Know the vehicle that a person wants to buy.
- Estimate how much money a person usually spends on travel.
However, not all data sources are created equal. Partnering with a reputable and trusted curator of specific and important data sources can help bank marketers gain better customer intelligence by cutting through the clutter and making the most meaningful improvements 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 Better Data
Some examples of how third-party data, augmented by specialized datasets, can help financial marketers:
Leverage third-party card purchase data to identify purchasing behaviors and create segments for card products with different value propositions (travel, foodies, etc.).
Using specific addressable data to offer a car loan directly to someone who is in the market for a specific vehicle.
Using consumer packaged goods (CPG) data to help financial marketers build interest-based customer segments and prospects based on recent purchasing behavior.
Leveraging wealth, economic and credit-based insights can help financial institutions target investment or wealth management services to people at specific wealth levels.
By tapping into specialized data sets, digital banking marketers increase their chances of reaching the right person with the right offer using the right tone and timing. This belief is essential for personalization strategies. Banking and financial marketers want to ensure that the “next best action” being communicated is relevant to the person receiving it.
Complementing first-party data with curated and specialized third-party data will enable financial brands to more dynamically engage with customers and prospects, and tailor offers and communications to the action taken by a person at a given point in time. Will be able to answer.
Segmentation Requires Good Data + Analytics
Data and analytics are interdependent. Segmentation schemes and acquisition models can’t work without good data, and data can only take you so far. Each item to be collected must also be attached, checked and tested.
Remember:
Select third-party data is a powerful complement that enables financial brands to more dynamically engage with customers and prospects.
For example, a financial institution whose media audience was augmented with person-based swipe. Data saw a significant increase in sales compared to its previous approach. Such accurate data strategies consistently deliver better results.
Certain data sources can and should augment their own and third-party master data. Additionally, establishing a rigorous evaluation. And measurement strategy with clear success metrics will be critical to the successful integration of new data.
Implications For Media Performance
Just as analytics and data are intertwined, so are identity graphs and data itself. With the elimination of tracking or third-party cookies, marketers have begun to adopt their own first-party data strategy. The future of programmatic media execution is large, smart addressable input universes.
With data layer information and provenance provided by a financial institution’s identity. Identifiable audiences (consumers who can be reached through a targeted advertising campaign). Can be delivered to a digital signal processor (DSP) or directly to a publisher. can
When more data can be attributed to an individual. The likelihood of finding that person in the paid media space increases. Range improves. This approach is powerful, giving brands a new sense of control and transparency over media execution. Whether using an agency partner or an in-house team.
Implications For Personalization
Digital banking 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. Brand Decision Q Optimization engines require a constant feed of data to drive personalized models. Especially if artificial intelligence or machine learning is used. Without a strong database and identity for fueling decisions, these engines cannot provide a highly personalized experience.
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