How are some banks growing revenue and maintaining target customer service levels without increasing branch staff? The short answer is by using predictive analytics. The key to simultaneously achieving these seemingly conflicting goals is harnessing the insights hidden in the data about branch activity. This includes data about customer arrivals, service times, wait times and details of how branch staff members are spending their time to serve customers. With the changes taking place in how customers use the branch, applying predictive analytics models to strategic capacity planning has never been more important.
Increasing customer share-of-wallet and new customer acquisition are two of the top three business priorities for large banks and super regional banks, according to a survey of bankers in the April 2014 BAI Retail Banking Outlook report. With tellers conducting fewer basic transactions in the age of online and mobile banking, the pressure is on the branch managers and platform staff to make more out of fewer opportunities to interact with customers. Bankers have to be on their game when customers do show up at the branch. This starts with making sure there is enough staff with the right expertise available to serve customers when they want to be served in a branch.
By examining data about their customers’ use of branches and using new workforce optimization models, strategic planners at top banks are finding opportunities to reallocate platform staff to achieve their goals. By doing this, staffing levels are shifted from overstaffed branches with lower levels of opportunity to those with greater opportunity. The results include more consistent wait times across the branch network, steady or increasing customer satisfaction levels and an increase in revenue. The analytics that drive these strategic planning models include actual measurements of how branch staff are currently spending their time, data feeds that track customer arrival patterns and seasonality at the product level and the branch level.
Predictive models can also provide insights about alternative branch formats that could free up staff, enabling them to spend more time building relationships with customers and leading to more cross-sale opportunities. With better strategic forecasting, banks can also free up resources to place more dedicated bankers in branches, such as finance specialists, wealth managers, or small business bankers, who can drive revenue growth, depending on the specific demographics of the markets that each branch serves.
Planning for Expense Reduction
The April 2014 BAI report lists “expense reduction” as the third business priority for retail banks. Top banks are using predictive analytics to more precisely evaluate the impact of branch automation and branch format decisions on staffing requirements, such as employing more video-assisted automated teller machines in smaller branch footprints, as well as installing cash recyclers and check imaging at the teller lines. With these solutions, banks can better predict how much onsite staff they would need – and when – for each format.
The move to employ more universal bankers has become almost universally accepted. Yet, jumping on the bandwagon to replace tellers with universal bankers without branch analytics and labor forecasting models that account for role clarity can be a costly decision with no corresponding benefit. Each branch must be staffed to meet the needs of its customers. The insights gleaned from workforce planning solutions based on branch-level analytics enables banks to make informed decisions about how many tellers, universal bankers and personal bankers they need at each branch. Properly staffed branches use tellers efficiently, while staffing the platform to resolve complex customer problems and increase cross-selling. Branch analytics will help banks set the number of full time equivalents for each role at each branch.
In order to make better staffing decisions, resource planners need to deploy solutions that help dive deeper into metrics such as customers’ use of all distribution channels. For example, beyond determining the percentage of checks that are now being deposited via remote capture, bankers should also ascertain how those checks were previously deposited by those same customers. Only then will they know the true impact on branch staffing requirements. If the analytics show that 75% of the checks now being deposited through remote deposit were previously being deposited at a branch’s automated teller machines, the bank’s workforce planners would have to temper their expectations about reducing tellers, or risk declining customer satisfaction. This holistic view of customer behavior is critical to properly staffing the branch to meet sales and customer service goals.
Strategic planning based on actual customer behaviors occurring at each branch can increase trust between high-level and regional executives in charge of strategic planning and branch managers responsible for day-to-day operational execution. When top-down directives are based on a deep understanding of what is actually happening in the branch, the bank wins, customers win and employees win. Both the morale and retention levels of branch staff increase when employees know that workforce allocation is based on actual data from their branch and that they will have enough resources to enable them to serve customers in a more timely manner.
Finally, strategic capacity planning solutions work best when they are deployed in conjunction with solutions for talent acquisition, labor scheduling and learning and development. Today, predictive analytics plays a lead role in all aspects of staffing the branch for success.
Mr. DeLapa is CEO with San Diego, Calif.-based Kiran Analytics. He can be reached at email@example.com.