The Promise of Real-Time Data

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Financial services firms already use customer data to cross-sell products and services, build loyalty, detect fraud and manage risk. The next step is a shift toward relationship marketing. This is becoming even more critical as digital financial transactions are integrated into daily activities and customers visit bank branches less often. The Promise of Real-Time Data by the Economist Intelligence Unit explores how analytics are enabling banks to obtain a 360-degree view of their customers and respond to their needs even as they migrate across channels.

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Of all the businesses people interact with on a daily basis, fi nancial services fi rms generally hold the most personal information. This might suggest that banks, insurance companies and other fi nancial institutions have a big leg up on other industries in deeply understanding their customers’ needs, wants and behaviors. Yet this does not seem to be the case. There are two likely explanations. First, some fi nancial services fi rms have been reluctant to use data that might be perceived as privileged. Second, banks tend to follow traditional organisational models with information sequestered in silos.

This is beginning to change, according to a global survey of executives conducted in July 2012 by the Economist Intelligence Unit (EIU) with sponsorship from SAS. In that survey, fi nancial services executives acknowledged that they have been slow to shift their focus from products to customers. Only 11% agreed strongly with the statement that their organisation is a customercentric business, versus 20% of their peers in other industries. Moreover, fi nancial services executives were only about half as likely to claim a clear understanding of customers’ tastes and needs. Paradoxically, however, they were slightly more likely to say they have the data, tools and processes in place to act quickly to changes in customer behavior and other dynamics. So they ostensibly have the tools, but have not fully learned how to leverage them.

he tidal wave of both proprietary and unstructured data flowing in from external sources such as social media makes this even more challenging. So much noise can drown out actionable insights that may create value.

The term “big data” says it all: more information, from a wider range of sources, in different formats, of varying complexity, arriving at an ever-increasing pace. High-performance analytics can help banks glean insights from the noise. But, ultimately, banks will need to transform their customer-engagement processes to effectively monetise this information.

Taking the customer’s pulse

Banks already use customer data to cross-sell products and services, build loyalty, detect fraud and manage risk. The next step is a shift toward relationship marketing. This is increasingly critical as digital fi nancial transactions are integrated into daily activities and customers visit bank branches less often. By focusing on the customer relationship rather than products, banks can expand their businesses through nimble, welltimed responses to customers’ wants and needs. Advanced analytics enables banks to understand these needs, not just by tracking customer activities, but also by interacting with them. The result is a 360-degree view of customers, and the ability to respond to their needs even as they migrate across channels. Customer expectations for this kind of responsiveness are building as people become more accustomed to Google- and Amazon-like services—anywhere they want, and in near real-time.

This move toward relationship marketing requires the integration of existing structured information with unstructured data from new sources. Jim Marous, in his blog Bank Marketing Strategy, writes, “Analytics can respond to the migration to digital channels by improving branch effi ciency and effectiveness, integrating sales and service tools within a new digital environment and by helping to drive high-value, high-touch traffi c back to branches.”

Predictive analytics adds another dimension by shifting focus to what will be, as opposed to what was. Julianna Young, director of marketing for Movenbank, explains that predictive analytics can interpret lifestyle choices and transactional behavior to assess which product offers will most appeal to an individual. “The important aspect of relationship marketing is anticipating the need of the customer and being ready to serve that in realtime,” she says. “Retail marketers have already developed sophisticated models. But overlaying bank transaction data further refi nes the targeting algorithms to be more effective.” This gives an advantage to banks, she adds, since “they can predict whether a consumer has discretionary spending capacity to act upon a particular offer.”

Baby steps

Financial services fi rms are responding to these developments with new marketing strategies. The EIU survey found that C-level executives in the fi nancial services sector were substantially more likely to cite customer analytics as a top marketing investment priority than their peers in other industries (42% vs. 25%). (See graph, below).They are also much more likely to say that data-driven analytical capability is an increasingly important CMO skill (39% vs. 23%). Yet, despite this progress, these executives continue to struggle with customer data. Nearly one-third (32%) of respondents cited diffi culty in mining big data for customer insights as a key obstacle preventing the marketing function from contributing greater value to the organisation. This compares with only onequarter of executives in other industries. In their quest to improve customer engagement, CMOs must overcome silos. Brett King, founder and CEO of Movenbank, believes understanding how customers use banking services in their day-to-day lives can help banks surmount this hurdle. Such data can be interpreted to identify behaviors, triggers, needs and benefi ts to craft compelling “journeys,” he says. This typically requires sharing data across an organisation. Mr. King points out, for example, that “building a journey that triggers a credit card offer might use data from the cards division, from the core marketing database and from the transactional team.” He goes on to explain that the transactional team might notice a trip booking or a change in employment that could signal an opportunity for an upgraded credit card offer. “No one silo owns this customer or the data that will lead to a credit card journey for the engagement,” he says.

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[Download PDF to see Graph]

Any move to break down silos requires a solid business case, so proponents need to move beyond the promise of real-time data and start with “quickwin” initiatives. Bank marketing executives interviewed for this story say that the best strategy is to clearly defi ne objectives and then apply a “testand-learn” approach. But David Bonalle, executive vice president and director of marketing and client insights at KeyBank, cautions that achieving buy-in from key stakeholders also requires a persuasive business case. “I like to use the example of a marketing campaign that was driven by our product managers’ view of best assets,” he says. “The analytics showed it wasn’t working, which ran completely against their business intuition. To get buy-in, we spent a lot of time going through the analytics to show how the data supported our conclusion. Ultimately, they [the product managers] agreed to change the approach.”

Through this iterative process of testing, learning and persuading, lessons learned from initial experiences can be applied to more ambitious projects. But a broader leveraging of customer data requires tracking the value of marketing investments—a skill not all CMOs share. This underscores a key fi nding from the EIU survey. To play a more strategic role in their organisations, CMOs need to broaden the skills available to the marketing function. Notably, senior fi nancial services executives polled ranked underinvestment in talent acquisition, training and retention as a bigger obstacle to a more strategic CMO role than under-investment in technology

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