Data Analysis Enables Omni-channel Marketing
Marketing has been called more of an art than a science. Yet in today’s financial services environment, the science of analytics is critical in making marketing a profit center, said a presenter at the recent Bank Administration Institute Retail Delivery Conference in Chicago.
The challenge with the science is the ability to pull together not just traditional data from accounts and transactions, but also unstructured data from sources such as text messages and social media, said David M. Wallace, global financial services manager for SAS.
The channel-centric model that most financial services companies continue to pursue results in too much siloed information and an inability to develop true omni-channel strategies, Wallace said.
Customer Decision Hub
“Analytical velocity must increase to support customer decisions,” Wallace said, recommending that data analysis be used to develop what he called a customer decision hub. Such a hub enables organizations to establish value-driven marketing with “a true contextual omni-channel communication across all channels,” he added.
The hub provides a center that drives different data-determined actions, marketing campaigns, service activities, sales programs, ad-hoc actions, regular communications, interactive dialogues and contact strategies based on transactional data, analytical models, events, contacts, risk, customer potential and customer history.
“It determines the minimum and maximum that you will spend to service a customer in each channel, the best moment in time to communicate with the customer and the best interaction with the customer at the best moment in time,” Wallace said, adding that providing such positive customer interactions also provides the customer with the sense that the financial institution truly understands his or her needs, in any communication and in any channel.
As such, the customer experience hub provides the following benefits, according to Wallace:
- Consistent omni-channel implementation
- Central decision logic for all customer interactions
- Unified user interface across departments
- Value-based marketing management
- The ability to leverage business know how through advanced analytics
Data-driven Decisions
HSBC, one of the world’s largest banks, uses data-driven decision making to optimize channel usage, said Simon Bennett, senior vice president of product analytics, North American banking analytics, for HSBC Bank USA.
“We want to continue the improvement of digital capabilities to support and drive customer behavior,” Bennett said. For HSBC that has meant developing advanced capabilities, including providing tablet-based tools to frontline staff, developing digital product origination and enhancing trading of foreign currencies.
HSBC used available data to identify inconsistent processes, benchmark performance and to map the customer journey. From there the bank designed a standardized approach, with the goal of building solutions once and then deploying them across the network.
Bank executives looked at data from different channels, including whether a customer relied on the branch, ATMs or electronic banking; which branch the customer used the most; and the impact of migrating a customer to a lower-cost channel.
This approach led to improved conversion from paper to digital documents and improved the onboarding process for wealth relationship managers, Bennett said.
Data analysis also helped the bank redeploy staff to improve customer service. Combining the data from channel analytics with information about when to contact a customer and what to contact them about has helped the bank acquire new customers, enhance existing customer relationships and retain profitable customers over the long term, according to Bennett.
Analysis of the success of previous onboarding strategies helped HSBC design welcome packs and schedule optimized calls, and to upgrade leads so sales people gave priority to the highest value prospects.
HSBC also used analytics to drive campaigns which helped enhance existing customer relationships and retain profitable customers. Customer retention was further enhanced by analysis of inactivity and other indications that a customer was planning to leave the bank so HSBC could take appropriate actions to keep the business.
Among the lessons learned, according to Bennett: “If the people that matter (the front line) aren’t with you, nothing else matters. [Data analytics] is a long-term commitment, and the tech build is the hard part.”
He also advised bankers to focus on what a financial institution can do with data analytics, not on what data analytics itself can do.
Other suggestions from Bennett and Wallace:
- Consolidate analytical models, rules and actions to support omni-channel strategies
- Use an integrated marketing application framework vs. best-of-breed tools stitched together, for higher velocity and lower friction in the marketing process
- Employ mathematical optimization at the center of a decision hub to meet both the customer’s and the bank’s goals
Both expect the use of advanced analytics in financial services to continue to drive the immediacy of decisions and to continue to make complex decisions easier.
Phillip J. Britt writes for a number of technology, financial services and business websites and publications, including BAI, Telephony, Connected Planet, Savings Institutions, Independent Banker, insideARM.com, Bank Systems & Technology, Mobile Marketing & Technology, Loyalty 360, CRM Magazine, KM World and Information Today.
