For banks to move customer data away from a distribution-led experience approach, banks must address engagement challenges. To deliver first-rate, personalized customer experiences, banks must establish key technical and data capabilities.
1) Tech architecture
FS companies need to consider how to design a customer platform for the next two years as a data-gathering repository. They need to account for how that data will support just-in-time, hyper-personalized, real-time experiences. If companies fail to design a customer data platform (CDP) for this reality now, they will not be able to support their strategy when those technologies come online in the future.
A CDP’s input/output infrastructure should answer questions regarding how a CX engagement platform can provide a foundation for data collection. Actionable data is available in abundance, but these pearls are usually hidden in a sea of noise. Businesses must decide what to collect, connect the dots to create one digital identifier for each customer and then share at scale by creating a central intelligence hub for the discovery and use of customer information. The current model of building a platform with a particular set of needs in mind will become obsolete; rather, the architecture for engagement platforms will assume that everyone’s needs will be different and ever-changing.
It won’t be relevant to consider individual digital platforms. Instead, a bank’s thinking should be in terms of a set of connected capabilities that it will use to meet its customers’ differing needs. Two examples of this approach are product manufacturing and understanding customers’ use of data.
In the future, a bank’s platform architecture should allow it to expose and combine these capabilities to create lucid experiences for both customers and employees. Organizing around capabilities will provide banks the most flexibility to build hyper-personalized journeys for the future. This idea is reflective of Conway’s law, which states that architecture takes the shape of the organization. In other words, organizations tend to replicate current structures when building future systems.
Translating capabilities into an architecture is necessary to generate engagement. Many traditional banks have applied predefined customer journeys to try to improve the quality of their customer experience. However, they have not considered, to the same extent, the capabilities their employees require—such as chatting seamlessly across channels or co-browsing. The ability to smoothly transition engagement with the same customer between WhatsApp, SMS and email while retaining a view of relevant information is a game changer for both the customer and the employee.
Using AI to gather and crystalize the collective knowledge and experiences of all employees can help each of them deliver better solutions to the next customer and allow them to make quick decisions. Capabilities such as these empower and improve employee well-being and efficiency.
2) Data architecture
AI and machine learning, combined with rich customer data, lead the way to adaptive, highly personalized experiences that are created in real-time. In this regard, data present the toughest challenge. First and foremost, FS companies need to keep customer data in mind. This involves everything from first-party, second-party and third-party data to data models, metadata and interaction models—in fact, all these behavioral data sets.
Data models have been proven to be one of the most difficult aspects to change. The data must be able to come together and connect with separate capabilities without requiring data to be reconfigured; these configuration changes must happen independently of the data itself.