Customer journeys are still being treated like channel marketing, following a linear process of distribution of a product or service from start to finish. This process raises challenges because channel marketing is so focused on the endgame, in terms of distribution, that it adheres to a rigid design. With this approach and these constraints, customer journeys are bound to deliver lackluster experiences.

A customer journey is typically designed as a happy path. But when companies have unhappy paths to deal with, such as seemingly insurmountable expectations around products or experiences, there is little flexibility for adaptation. The distribution-led channels approach makes the customer journey not particularly responsive to feedback.

Once linear customer journeys are replaced by always-on targeting solutions, the pathway can become more reflexive. Here’s how to achieve this change.

Almost every brand wants to be inclusive and accessible. In the financial services (FS) industry, brands are perhaps one of the most integral parts of societal infrastructure. Individuals whose money is unbanked struggle to meet the most basic requirements for survival, such as getting a home or a job, and are therefore unable to fulfill even the bottom level of Maslow’s pyramid. In this context, banks face a quandary: they only make their money from people who have money—not from people who don't have money—and they don’t make their money from treating people differently.

The cost of variance means that banks can’t be inclusive to those whose money is unbanked or to people of protected characteristics because it’s too expensive. However, because banks are part of societal infrastructure, there is a moral and societal expectation for banking providers to be inclusive of all members of society. The core reason for their inability to do this comes down to the limitations of technology, a gap in data and the degree to which experiences can be adaptive and hyper-personalized.

Anthropology and computational design thinking are in many ways aligned. Both approaches operate on the idea that experience is personal, subjective and contextual. Additionally, our understanding of society will be increasingly necessary in creating those adaptive and inclusive experiences.

Computational design will drive adaptive experiences through the power of artificial intelligence and machine learning. Machines will be able to create experiences without a need for direct human intervention; this is what will allow experiences to scale. In the future, scaling won’t be about our ability to design—it will be about our ability to understand different populations of people and the subtle differences in their needs.

Diversity and inclusion targets are, of course, something for which everyone should be morally driven to strive because being inclusive and representative of society is the right thing to do. However, if diversity and inclusion are not represented in the workforce of the future, how can designers possibly create experiences that are inclusive? To reach the point at which an FS organization can be inclusive, it must move on from generic customer journeys and come to drive adaptive experiences; it must start considering the data that it wants to capture now. This process is about getting that data through current systems, interfaces and experiences.

As banks gear up for the future, the next major issue they will need to wrestle with is privacy policy. Ultimately, this is the degree to which organizations think about data privacy: what they can and cannot capture and what they will and will not do with the data in ways that are more transparent and explicit than they are now.

Here are two things that banks can start doing now:

1. Think about the richer data needs of the future and start capturing data now, to collect high-quality data sets that will support personalization in the future.
2. Understand that data privacy and customer data policies should be much more intentional to ensure they are aligned with customer values.

Banks can start analyzing their current journeys now. Although many current journeys lack flexibility, they can still be valuable in terms of data captured for the future. Organizations should capture data now—even if they can't execute on it—to build and gather information in advance of its value.

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.

The customer journey is dying, but this is a good thing for FS organizations. Banks can succeed at delivering first-rate, personalized customer experiences when they move their customer data away from a distribution-led experience approach to a more direct customer experience engagement tactic.