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Optimizing Staff Scheduling for Improved Branch Performance


Whatever the format – traditional, in-store or mini – the branch is still the primary channel where consumer sales and referrals originate. Three out of four consumers still prefer in-branch interactions to open an account, apply for a loan or get financial advice, according to the Gallup Business Journal. As retail banks seek to accommodate this preference by transforming their branches from a transactions focus to a sales- and service-oriented customer interaction channel, improving staff capacity optimization becomes a key issue.

When a branch is understaffed, service levels are compromised and sales opportunities lost; when overstaffed, profitability is compromised. Optimized capacity means branches are resourced to meet sales and service targets with just-right staffing levels and with the right position mix. Workforce optimization models can play an important role in achieving that mix.  However, there are three major challenges: inefficient or ineffective processes; inaccurate or incomplete data; and outdated modeling and analytics.

Inefficient or ineffective processes. Is capacity optimization a streamlined process in your organization? Are your strategic planning, resource forecasting and scheduling activities taking too long?

Optimizing sales and service capacity is an iterative process. It starts with annual strategic resource planning and continues with quarterly reviews and adjustments that drive monthly tactical resource forecasting. In an effective process, half-hour resource forecasts guide branch managers to create weekly labor scheduling. Roster changes on the ground due to voluntary and involuntary turnover, paid time off and temporary leave are fed back to strategic planning, tactical forecasting, and talent acquisition.

Capacity optimization is also a collaborative process between operations, finance, branch distribution strategy, sales and service planning, product development, marketing and Human Resources. The operating budget is the guiding constraint to staffing. New product introductions, marketing and promotions are critical to predicting customer arrival patterns. Job analysis, talent acquisition, employee turnover and attrition are critical to planning and scheduling to the appropriate staffing levels. Location optimization impacts customer arrivals. Branch design is critical to staff roles. So, all process owners need to be synchronized in order to streamline the capacity planning process.

Implications of an inefficient or ineffective process are wasted time and money, frustration among line-of-business managers, strategic planners and branch managers. This ultimately leads to slow and erroneous business decisions. Solutions that can specifically address process issues include: integration of strategic capacity planning, tactical forecasting, and scheduling functions; automation of data sharing and visibility across the organization; use of what-if scenario planning engaging multiple stakeholders and scenarios; and utilization of high-performance, scalable forecasting, simulation and scheduling solutions.

Unreliable or incomplete data. Do you have consistent and granular data for staff utilization for different staff positions? Are your bank’s service time standards accurate? Do you have reliable data for customer arrival patterns for each branch format in your distribution network?

Most banks have done branch productivity studies at some point in the past, but what they think is happening in their branches is probably very outdated. With the increasing use of alternative channels, changing customer demands, expanding operational/regulatory activities and universal banker pilot programs, most banks would benefit from a more updated view of their branch interaction times and customer arrival patterns.

Lack of granular and consistent data about what is actually happening in the branches results in poor analytics and erroneous staff optimization decisions. In his article “Four Myths About Bank Productivity,” consultant William Heitman estimates that applications for new accounts and loans are often processed inconsistently and incompletely, adding up to 10% to branch staffing levels.  “Banks should undertake a wall-to-wall analysis of non-technology work activities at their branches,” Heitman says. 

Unreliable or incomplete data leads to misalignment between planned resource capacity, available staff on the roster and scheduled resources in the branches. Solutions to address this challenge include: conducting branch productivity studies; analyzing customer arrival patterns, customer wait times, branch workflows and staff utilization; using accurate time standards; and utilizing data feeds from multi-channel sales and service systems to build holistic models.

Outdated modeling and analytics. Branch staff models used by most banks today fail to capture the impact of channel migration. They lack flexibility to model changing staff roles and the analytics fall short in predictive precision guided by financial constraints.

Based on observing 25,000 customer interactions in hundreds of branches last year, we estimate that as much as 50% of platform staff activities are untracked, including new product discussions, account fee questions and referrals. Models that use declining teller transaction ratios to predict platform interactions can cause the platform to be understaffed.

All staffing models represent customer arrival points as queues that are served by resources such as lobby, drive-up and merchant. Tellers are dedicated resources that can only serve their own teller queue. Universal bankers are flexible resources so they can serve other queues if their own queue is empty. In addition, branch managers or assistant managers help tellers during peak periods. Outdated staffing models work well for modeling tellers but are not realistic for modeling flexible resources such as universal bankers. Such models generate results that typically result in overstaffing.

Typical resource capacity models start with a fixed service level and calculate the required full-time-equivalent to achieve that target service level primarily driven by transaction volumes, time standards and service times. This is the perfect-world resource forecast! It doesn’t take into account the operational cost. It then becomes a very time-consuming, trial-and-error exercise to arrive at a realistic forecast that incorporates financial constraints.

The most obvious implication of this challenge is misallocation of resources. Other implications are loss of confidence in staffing models and in the people behind the analytics. Solutions to address the challenge of outdated methods and tools include:

  • Using forecasting models that take into account varying degrees of transaction volumes across the network, based in part on seasonality and marketing promotions, to derive more accurate forecasts of customer behavior over the next 12 or 24 months;
  • Position planning models specifically designed to identify the best staffing mix aligned with your bank’s business strategy taking into account universal banker positions, full-time versus part-time staff mix; and
  • Constrained-based service-level modeling that intelligently applies business constraints to simultaneously achieve service/sales targets and cost targets.

Implementing the solutions mentioned above will help address the three major challenges with optimizing sales and service capacity, ultimately improving operational efficiency. Specific benefits should include: elimination of waste and frustration associated with time consuming and error-prone scenario planning; optimal staffing for each branch based on its unique attributes; minimization of customer wait times; more accurate analytics to drive the staff models; and synchronization of strategic planning and operational execution.

Mr. DeLapa is CEO with San Diego, Calif.-based Kiran Analytics. He can be reached at [email protected].