Are You Empowering Your Customers? Using Customer Insight To Provide A Better Banking Customer Experience

Jenna Danko
Product Marketing

In my previous blog, How Can Customer Insight Improve the Customer Experience in Financial Services, I discussed the move towards "risk-on" business strategies for Customer Insight.  Two weeks ago, I attended and presented at the Customer Analytics & Insights in Retail Financial Services conference in London, organized by FC Business Intelligence.  The tag-line for this conference was "How to be truly customer-centric: Deploy advanced analytics to improve the customer experience, target customers more effectively, reduce operational costs and boost the bottom line." Over 2 days, there were presentations from a variety of financial institutions, industry research organizations, and relevant technology enablers.

A leading UK based bank discussed the idea of analytics evolving to be a core enterprise asset that should ultimately report to the CEO and become the foundation of how the bank should interact with its customers; a leading Europe based insurance firm provided some innovative thoughts on how to monetize the customer analytics process; and another UK based bank took the audience through some specific use cases of how customer analytics can help define their customer strategy, improve the customer experience, and target the right people.

There were a number of very interesting themes that came out of this conference, especially across these 4 areas – an enterprise perspective, adapting to market trends, the impact of social and the role of technology enablers.

  1. An Enterprise Perspective - Need for a Holistic Approach: The bjectives for Customer Insight are fundamentally about understanding the customer’s perspective, and ensuring all proactive and reactive customer interactions are aligned jointly to the customer’s experience, and the institution's strategy. There was much discussion about the types of models and measures necessary to drive appropriate customer interactions. Institutions such as Barclaycard, Nationwide Building Society, RBS and others spoke of the need to interact better on the customer’s terms. What became apparent in the various discussions was the need to think about the analytics challenge holistically – what data do I need, is the importance of the analytics role sufficiently understood and endorsed at the highest levels, and are the people, process and technology organized to provide the best outcomes for customer and institution?
  2. Adapting to market trends: The challenges in retail financial services are common across other industry sectors. There were presenters from insurance, business banking and wealth sectors, all of whom provided some interesting insights into the opportunities and challenges. In many respects, the challenges in high value low volume environments are no different to those in low value high volume environments – how do we get a handle on customer information to assist in internal decision making, fraud detection, risk management and a superior customer experience?
  3. The impact of Social - Social Media represents a huge opportunity and a great challenge for many institutions: A great presentation from twitter provided some valuable insights on how the use of social feeds should be incorporated actively into operational and analytical customer processes, as in many cases this is the first source of identification of issues. Where a client has given permission for linking their social profile to their account, this can be a valuable source of additional source data into a customer analytics platform. The challenge is in turning the overwhelming amount of raw data into useful structured data that can be provided to the analytics platform.
  4. Successful technology enablement at an enterprise level is crucial for success in delivering effective customer insight. Where should an institution start in looking for a technology solution?
    • An industry-specific and comprehensive data model. The first requirement is a comprehensive data model that is designed to accommodate all data sources within the bank, including customer, risk, performance, channel, and marketing. This enables institutions to pre-define critical relationships within the data model, allowing for intelligent rollups. In any business intelligence initiative, the data model can be one of the most expensive and time-consuming parts of the project, especially if built from the ground up. This need not be the case today. With commercial- off-the-shelf solutions, institutions can accelerate time-to-value and reduce total cost of ownership, while benefiting from vast industry experience and knowledge.
    • A unified analytical approach. Institutions require a unified analytical approach that provides the flexibility for custom solutions for individual subject areas without compromising data consistency enabled through data conformity. Analytical applications supporting finance, risk and marketing can co-exist within a unified model, leading to assured data consistency and a true single source of truth. Working with a unified data model also ensures insights from each of the subject areas are immediately available to other departments within the bank to leverage without the need for complex integration.
    • Ready-to-use tools. Pre-built and industry-specific reports and dashboards and analytic models that address critical strategic analytic needs across customers, performance, product and channels, not only jumpstart rollout, but also provide comprehensive and pre-tested templates built by domain experts. They are not left to go it alone. Essential tools should include pre-defined attrition, cross-sell, up-sell and propensity models, along with customer lifetime value predictions. Also important are performance reports across key dimensions, such as customer segments, products, time and organization; pre- built campaign response and performance monitoring capabilities; and channel performance analysis.
    • Ability to operationalize and automate. The customer insight challenge involves not only defining processes and metrics but also measuring them. As such, institutions require solutions that enable them to operationalize customer insight processes – defining them so as to make them distinguishable and measurable – and automate them to ensure rapid delivery of information when and where it is needed.

It is clear that the challenges are still many, but the opportunity is
even greater.  Engaging with customers is a top priority and the
financial institutions that tackle the points I’ve discussed above will
win the war for customer loyalty.

If you are interested in seeing my presentation, please connect with me through LinkedIn (Stuart Houston, Global Solution Director - Financial Services Analytics at Oracle, FSGBU, based in Sydney, Australia) and you will be able to access it through my posts.

I look forward to hearing your thoughts and opinions.

Stuart Houston is the Global Solution Director for Financial Services Analytics at oracle. He can be reached at stuart.houston AT oracle.com.

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