How Can Customer Insight Improve the Customer Experience in Financial Services?

Jenna Danko
Product Marketing

For many years now, institutions have worked towards providing a 360 degree customer view as part of their customer engagement strategies. When enterprise CRM systems were first deployed over 20 years ago, this entailed the provision of a reporting and/or dashboard paradigm that provided information on customer and account balances, transactions, basic segmentation and supporting recommendations for the next offer to make. Often, this information included guesstimates for profitability and/or risk-adjusted performance.

Today, institutions are dealing with many new challenges in the post- Global Financial Crisis (GFC) world, including:

  • Significant ramp-up in regulatory requirements, driving up compliance costs, and in many cases restricting fee income;
  • Large investments in front office technology, allowing competitors to launch offers in shorter timeframes, reducing time of product differentiation and ultimately the competitive edge;
  • Cost of acquiring customers, especially in developed markets, is rising exponentially, driving the need to ensure that the “right” customers are targeted for retention and/or acquisition.

Probably the most important driver in providing a complete view of the customer is the need today to incorporate accurate risk, capital and ultimately performance attributes into all information delivered to automated and assisted channels in the institution, no matter whether for marketing, origination or servicing purposes. This can impact style of customer engagement, origination decisions including pricing, and incentives for customer facing staff, and ultimately have a direct impact on execution of customer strategy.

It is common that many people in customer insight related roles have challenges in performing their job, such as:

“What data do I need and where do I get it from?”

“Why do I have to wait until the end of the month to get my performance reports?”

“How do I make sure all my data is consistent and comparable?”

“Great analysis … now how do I use it to impact my customer’s experience?

“How can I measure the results of using my new insights?”

In a recent Oracle Study, From Overload to Impact: An Industry Scorecard on Big Data Challenges, customer insight came out as the area of highest growth in data volumes over the last 2 years, with this trend expected to continue for the foreseeable future.

More worryingly, in Financial Services, only 3% of executives rated themselves an A in preparedness for the data deluge.

 So where should institutions start looking from a technology perspective to address the challenges of operating in today’s environment?  Here are some ideas:

  • An Industry specific data model, which can be installed and configured, based on use cases is critical. It is essential to start from a robust pre-existing taxonomy covering all common financial services customer segments, products, and customer, risk & performance attributes. This will help avoid the typical “analysis paralysis” that accompanies many projects of this type.
  • A starting point for dashboards and reports can also add significant value, as the conversation then becomes “Why can’t we use this content” instead of “What do you need”. This will help significantly reduce cost and risk of delivering a solution.
  • A single source of truth is paramount to effective customer insight – i.e. the data being used to drive funding, capital allocations, regulatory reporting, incentives and strategic direction, should also be used to drive customer insight, and the consequential customer interactions. Too often in the past, there have been situations where the data used in a line of business is inconsistent with that used at an enterprise level, compromising key reporting and decision making processes.
  • A comprehensive modeling framework that can operate at an enterprise level, yet has the power and flexibility to create new and interesting insights that can be used in customer facing processes. Often, the modeling of attributes such as lifetime value, expected profitability, segmentation according to multiple criteria, etc. happens outside the operational reporting environment, leading to lags between calculation and enablement of processes. Additionally, the ability to perform these calculations at scale, especially in retail banking operations with high volume and relatively low complexity, is critical as institutions increasingly demand daily (or even intraday) risk adjusted reporting and analytics.

The financial services industry is evolving, and so are its customers. The key to providing a better customer experience, and ultimately ensuring profitability at an acceptable level of risk, lies in the ability to identify highly profitable and potentially profitable customers and understand the enablers of a profitable relationship. To succeed in this increasingly challenging marketplace, you must secure a comprehensive and consistent set of performance metrics, enabling you to better understand the value of your customer relationships as well as gain insights into the changing profile and dynamics of customer interactions and risk adjusted performance.

I will be discussing these topics in further detail at the Customer Analytics & Insights in Retail Financial Services conference, February 26-27 in London, organized by FCBI, to be held at the Marriott Regents Park Hotel.

Do drop by if you can and stay tuned for my next blog post on this subject around Customer Profitability.

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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