The Linear Customer Journey is Dying. Always-On Targeting Solutions are Key
Customer journeys are still being treated like channel marketing, as if they follow a linear process. This 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 straightforward process in which the customer behaves in a predictable way and everything goes to plan. But when customers veer away from the expected path, there is little flexibility for adaptation. The distribution-led channels approach makes the customer journey not particularly responsive to feedback.
However, once linear customer journeys are replaced by always-on targeting solutions, there is far greater scope to adapt according to customer needs. Here’s how to achieve this change.
Step I
An anthropological approach
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. People without bank accounts will struggle to meet even the most basic needs at the base of Maslow’s pyramid, being unable to rent a home or get a job.
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.
The first step for financial organizations is recognizing the importance of meeting diverse needs and evaluating the technologies that will make this possible.
Step II
Computational design thinking
Anthropology and computational design thinking are in many ways aligned. Both approaches operate based on the idea that experience is personal, subjective and contextual. Additionally, our understanding of society will be increasingly necessary in creating adaptive and inclusive experiences in response to this.
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 groups of people and the subtle differences in their needs.
Diversity and inclusion targets are, of course, something that everyone should be morally driven to strive toward 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 instead provide adaptive experiences. Successfully achieving this will require organizations to start considering the data that they want to capture now. This process is about getting that data through current systems, interfaces and experiences.
This increased importance of gathering data means the next major issue for banks will be privacy policy. Banks will need to consider 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.
Step III
How to make tech & data architecture work
For banks to move customer data away from a distribution-led experience, banks must address engagement challenges. To deliver first-rate, personalized customer experiences, they 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.
In the future, a bank’s platform architecture should allow it to combine these capabilities to create improved 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.
Using AI to gather and crystalize the collective knowledge and experiences of all employees can help each of them deliver better solutions to customers and 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. As a result, FS companies need to keep customer data in mind, including everything from first-party, second-party and third-party data to data models, metadata and interaction models.
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 linear 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.
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 capturing this data – even if they can’t execute on it – can gather information in advance of realizing its full value.
