Editors Note: This is the first post in our three part series exploring how banks and financial service institutions can evolve to become truly customer-centric organizations through the power of data driven decision-making. If you would like to find out more about our financial service offerings, go to our website.
‘The customer comes first’ is as true a mantra in banking as it is in any other industry. Banks have relied on customer intelligence to boost engagement rates, cultivate loyalty, and ensure the right offers reach the right people.
In a previous generation — before every phone call, branch visit, and financial transaction was digitally logged — this intelligence would have been acquired through consultations with a banker, who strove to build trust and make understanding customers the bedrock of their business. Today, banking customers demand the same level of personalized service, only now the solutions are overwhelmingly technological. Institutions also need to factor in how they ensure that sensitive data is appropriately managed, with the necessary tools in place from both a compliance perspective and when it comes to being transparent with their customers about how and why they use it.
Frictionless, empathetic, end-to-end, are all words and phrases often attributed to the next-generation customer experience, in which banks deploy data-driven solutions to consider the needs, goals, and life stage of their clientele. Given that happier customers are more likely to keep their accounts open and provide greater lifetime value, becoming ‘customer centric’ is a challenge no bank can ignore.
Despite this, the term has managed to evade a common definition. What does it mean to be a ‘customer-centric’ bank? Is it an end-state, or an ongoing, iterative process, much like the now overused ‘digital transformation’? And most importantly from our perspective — how can technology empower banks to achieve this? While segmentation models and next-best-action are nothing new to finance, they still operate as discrete solutions at the periphery, instead of providing an operating model that pulls the entire organization towards the same end.
In this three part series, we will attempt to transform ‘customer-centricity’ from a loose ambition to strategic execution plan. We’ll draw on Palantir’s experience working with global financial institutions, to show you operational data connectivity can form the cornerstone of the customer-centric bank:
Same goal, different data
Data is put to use by banks throughout the customer lifecycle, from sales campaigns to continuous onboarding and round-the-clock service. Each stage offers banks another opportunity to apply what they know about their customers’ wants and needs to inform operational outcome.
The flow of data typically resembles an assembly line — it’s one-way, cascading down through single client views, entity resolution layers, and Customer-360 tooling into discrete workflows. The customer continues along their banking journey.
The fact that this setup is a mainstay among banks speaks to its value as a simple and intuitive model for data consumption. But its utility gets stretched when banks begin to seek a truly customer-first strategy. It may be familiar and reliable, but it is also slow, and functionally disjointed. Importantly, it often lacks the agility required to respond to changes in customer behavior and the wider market.
From the customer’s standpoint, this can manifest in a banking experience littered with pain points, such as irrelevant conversations with servicing agents, untimely offers, or missed service-level agreements.
Customer-facing solutions are often alienated from one another — there is limited collaboration, and no wider business understanding to ground decision-making.
The analytic insights generated from integrated customer data help solve discrete challenges, such as automated onboarding or omnichannel servicing, but their mission ends there. The decisions they inform are disconnected from those that will be made in the future, since there is no mechanism in place for outcomes to be fed back. This is ultimately how organizations learn — not just within a given workflow, but how teams and functions share experience to iteratively improve customer engagement.
With these drawbacks in mind, we’re able to begin to sketch out a blueprint of a customer-centric architecture.
Applied Customer Intelligence
At the highest level, our goal is to connect a previously disconnected data lifecycle. How can banks implement software that takes a holistic view of the data passing through their company, as it flows between customer touchpoints, data science teams, analytics, and back?
There are four core capabilities of any customer-centric banking solution:
1. Bidirectional data integration should be native, real-time, and span both cloud and on-premise source systems, including CRM, ERP, and core banking systems. Integrations can be automated and software-defined to reduce time to deployment, and employ scalable, end-to-end governance practices.
2. Native integration with analytics will ensure model/AI teams and business teams are functionally connected, and consuming the same data. Model outputs must also be able to be pushed directly into systems of action, such as elements of a bank’s MarTech stack, in real time, as well as consumed in production workflows by operational users.
3. A semantic layer to contextualize customer data and act as a mutually intelligible interface between data teams and customer-facing roles. By representing complex data structures as tangible business objects, end users gain the autonomy to explore the data underpinning their decisions — company data becomes a secure and collaborative asset that is bound directly to operational workflows.
4. Dynamic decision capture from systems of action and operational users, including live interactions with customers across all channels. Bidirectionality also plays an important role here — data must be pushed to and pulled from the semantic layer to establish end-to-end connectivity and therefore allow the iterative improvement of processes and models.
Data can flow dynamically between operational workflows from a common foundation of customer intelligence. This core replaces the siloed data flows from the conventional model mentioned previously and allows a bank’s understanding of the needs and wants of a customer to continually develop across the entire organization.
In an economic sense, the recycling and compounding of customer intelligence makes the development of business-driving use cases faster and incrementally lower cost. Users can efficiently build on top of what has already been deployed, instead of starting from zero for every new application.
Therefore, unlike the assembly line approach, this solution is engineered for optimization. The feedback mechanisms in place guarantee that engagement becomes more and more personalized with each customer interaction. Internal operations become more efficient as they align with what the customer experiences externally. In short, the digital inside meets the digital outside.
Data with a purpose
Banks that retain a competitive edge will ultimately be the ones that manage to successfully connect data to business logic. Enterprise data should be fundamentally rooted in what it can do for the customer, which is, after all, the mission of the bank.
The goal of this series is to demonstrate how establishing this connection is a practical, as well as a conceptual, challenge. Next time, we will speak to a Palantir Deployment Strategist about how we helped a European bank position customer value at the core of their transformation strategy.
You can read more about our financial service offerings on our website.
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Building the Customer-Centric Bank: Drawing up a Tech Blueprint was originally published in Palantir Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.