Other parts of this series:
Banks that are mastering data-driven analysis to unlock a detailed understanding of their customers are using that information to drive tenfold returns on investment. Any advantage in knowledge and strategy can be potentially crucial to increasing profits. But customer behaviors are changing so dynamically that most banks don’t get enough access to the right data when they need it, and haven’t mastered the artificial intelligence (AI) tools that can analyze and use the data they receive in real time.
While many commercial banks use data-driven learning to increase automation and assist with fraud detection, harnessing the full potential of data involves using it in every aspect of the bank’s business decisions in order to reduce costs, retain customers and grow revenues. To make that happen, they need detailed, timely data that provides what I call the “micro-pulse” on customer behavior.
78% of banks broadly use data but only 7% have scaled analytics and just 5% have scaled AI to extract the full value of data across the customer experience.
Accenture: Data-driven mastery in commercial banking, 2021
I‘ve identified four areas where detailed and timely data can help boost banks’ bottom line.
Lead generation and prospecting
To fuel growth, banks want to attract new customers. They also want to actively nurture their customer relationships by reaching out proactively at the right moment with the right product. When they leverage data to understand more about their customers and prospects, they’re able to reach out with relevant content and messages for converting leads, cross-selling and up-selling. To do this, they need answers to several questions:
- Which leads are most likely to bring in new business?
- Where should we be spending our marketing dollars for the best return?
- Why are prospects failing to become customers at a certain point in the process?
The answers are often revealed when you are able to dig into your data. For example, if 30% of your credit leads are dropping off at the point where they need to fill out your loan application, perhaps you need to modify the application form or process. Or if you are spending a lot of money on a market but gaining little ground, perhaps you should focus on more profitable markets, or review your strategy in the less successful ones.
Price optimization
Data-driven pricing decisions can help banks to maximize revenues by identifying how much clients are willing to pay. Analyzing customer behavior data can help you pinpoint the right price point for various products by answering questions like these:
- How many customers are we likely to lose at each point along a potential price increase gradient?
- What are customers willing to pay for a new level of service?
- Which products are no longer financially viable because our customers don’t value them?
Sophisticated, AI-driven data analysis can greatly increase the accuracy of pricing models and avoid the “best guess” approach that might otherwise be necessary to price a new product or service.
Our annual Commercial Banking Top Trends report identifies the topics that are most likely to transform the industry in 2022.
LEARN MORECredit and risk decisions
Determining credit risk has always involved analyzing data from applicants. Banks are now able to pull in new kinds of data beyond what is provided directly by customers, and that ability will keep expanding as open data evolves. The banks that master the use of that data in credit decisioning will vastly improve the way they manage risk.
A growing number of fintechs and banks are using non-traditional data to inform their credit and risk decisions. Being able to weigh more data about a customer results in better, faster decisions—often in real time. With competitors like Shopify and Stripe entering the loan business, commercial banks need to be as ambitious as their competitors are about using data.
On the investment side, data-driven decisions will also allow portfolio managers to act sooner when early-warning signals emerge to indicate increasing risk levels.
Customer retention
Why are customers moving away from your bank? Which services or products are they choosing to purchase from other providers, and why? Are their decisions fact-based or emotional? Where is the tipping point where you can no longer win them back? The traditional data that banks have used for determining strategies and positioning their products and services is neither detailed nor timely enough to provide that “micro-pulse” on customer behavior that can optimize retention and cross-selling.
Commercial banks can use data-driven insights to empower relationship managers and others in the business to make better decisions, pushing a range of revenue, retention and cost-reduction levers.
Banks need to expand their data system to bring in detailed information about customer behavior—and not just once. It should be updated continually, so that they are able to get ahead of the customer’s choices and provide the right outreach at the right moment. As with credit decisions, bringing in data from non-traditional sources will vastly improve the retention analysis. A solution that stitches together first-party and third-party data to drive proactive engagement is an essential capability for banks.
The results of rich data analysis need to be available to the people who are interacting with customers. This will empower your people to take the right approach for each individual customer relationship. According to Accenture’s report on data-driven mastery in commercial banking, “integrating rich insights into a relationship manager’s day-to-day ways of working will enhance effectiveness and productivity and deepen relationships.”
In an upcoming post I’ll describe how customer data can be found across a number of information streams, and how AI can be used to build an accurate picture of customer behavior. In my next post in this series, I’ll examine some of the barriers to data-driven growth faced by commercial banks.
To find out more about leveraging your data to fuel growth, read the full report, “Data-driven mastery in commercial banking”.
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