Bank Vertical Case for Counting Devices

A Case for Using “Counting” Devices in Banking Locations

The use of “counting” devices in retail banks and their branch locations offer banking companies new key performance indicators (KPI’s) that directly relate to customer satisfaction and retention.  Bank customers with “cash” transactions such as obtaining cash from or depositing checks into bank accounts are able to use ATM’s at the banking location or within an ATM network.    

More complex banking transactions require the customer to enter the bank lobby and to interact with a teller or a banker.  These complex transactions include opening/closing accounts, wire transfers of funds, obtaining “certified” checks, applying for loans, visiting safe deposit boxes and the like.  These transactions are more labor intensive and could cause customers to wait significant amounts of time if the banking location is not staffed properly.

The “counting” devices most appropriate for banking locations are: door counters, WIFI counting solutions and facial recognition equipment and software. 

Customer/visitor counts are a key metric that provides insight into the actual number of customers entering and exiting a banking location.  Counting devices or sensors are placed above the doors of a bank lobby to provide the actual counts of customers or visitors – 24/7/365.  CountBOX provides these door counting devices for banks.  The customer counts are time stamped.  Therefore, a distribution of the number of customers by time period is developed for the bank.  A bank manager may determine the number of customers entering the bank by hour, “4-hour shift” or the total day.  Bank lobby traffic measurement allows for the determination of “peak traffic hours” within a week.  With a 44 “open” hour week for the average bank lobby location, each “open” hour in the week accounts for 2.3% of the total open hours for the week.  A “peak traffic hour” could be defined as an hour that has 3.5% or more of the total traffic within a week.  More staff must be scheduled to “cover” the increased visitor demand at the peak visitor hours.

                                Peak Visitor Hour = % of weekly traffic >= 3.5% in a 44 “open” hour week

When the visitor traffic data is merged with bank transaction and labor data, other key metrics are calculated.  One key metric is conversion rate or total transactions divided by visitor traffic:

                                Conversion Rate =  Total Transactions

                                                                      Customer/Visitor Traffic

Conversion rate provides the percentage of visitors that actually make a transaction.  Banks would assume that this would be close to 1.  However, the overall conversion rate will be less than one as there will be some “groups” of customers that will be part of a transaction such as a parent/child group opening a bank account or spouses applying for a loan.

Conversion rates may be calculated by the type of banking transaction.  For instance, a new account conversion rate is calculated as:

                                New Account Conversion Rate =  Number of new accounts opened

                                                                                              Customer/Visitor Traffic

Knowing the conversion rate by type of transaction allows the bank manager to have insight as to the mix of business for the branch and to schedule the correct type of bank personnel based on visitor demand. 

Another key traffic-based metric can be calculated – the customers to staff ratio:

                                Customers to Staff = Number of customers in bank lobby

                                                                         Staff present to serve customers                                                                                                                                                                                                                       

The customers to staff ratio may be calculated for different time periods, but the most useful measure is at an hourly level. In general, the lower the customer to staff ratio, the more likely that customers will be taken care of in a timely manner.  Customer satisfaction levels are directly related to service levels.

Using the visitor traffic distribution for the week, peak traffic hours and the customers to staff ratio, the bank manager is able to efficiently schedule bankers/tellers to meet the demand of visitors. As an example, the bank manager could schedule part-time tellers from 10:00 am to 2:00 pm to meet the demands of mid-day customers while scheduling a base level of “full time” staff to fulfill the demand for the rest of the day.  Part-time employees may also be used to meet the demands of Saturday banking.  Saturdays are most likely to have the peak visitor hours for the week since Saturday is a reduced hours day and the demand for services will be high as most customers will have that day off. 

Dwell time is another key metric that will drive bank customer satisfaction levels.  By using WIFI traffic counting solutions, average customer dwell time can be measured within the bank.   Average dwell time is defined as:

Average Dwell Time = Total minutes spent in the bank by all customers

                                                                           Total number of customers

Average dwell time is usually calculated on a daily basis, but it could be calculated on other time periods such as a hour, week or month.  Dwell time is important since the longer customers are in the bank the higher the chance that satisfaction levels will decrease. 

Finally, “impersonal” facial recognition analysis will provide the bank with a demographic profile of its customers.  The demographic profile data is obtained by strategically placing sensors within the bank to capture customers’ faces – both entering and exiting the bank lobby.  Then using facial recognition software and aggregating across customers a demographic profile is created.  The profile would include gender, age, race and mood of the customers.  The profile data could be used by the bank to assess the differences in profiles from branch to branch and to determine the moods of customers when they enter and exit the bank.  Happy customers leaving the bank are apt to have higher satisfaction levels than those with angry expressions. 

The bank company uses the customer count KPI’s, dwell time and customer demographic profiles to track branches over time.  Branch KPI levels can be compared to new sales of bank products (accounts, etc.), account retention data and customer satisfaction levels.  Bank branches can be compared to one another using the customer count KPI’s and demographic profiles   Based on this analysis, the banking company may implement training to improve/refine its service levels to its customers.  Also, the banking company could create incentive programs for bank personnel to meet certain service level or account growth goals.  Increasing retention levels and increased banking product sales yield higher bank revenues and profits. 

The CountBOX reporting dashboard provides reports for the standard customer traffic metrics used by bank managers and their superiors.  These metrics help the managers and their leadership to improve operations and while creating additional revenue opportunities at the banks.  CountBOX provides consulting services to clients to help them in obtaining the most value from the CountBOX analytics solution.