Analysis Turns Physical Security into Customer Service
Results are considered to be reactive rather than proactive
In recent years, customer service has become a
new frontier for marketing and brand building.
The ability to anticipate the needs of loyal individuals
gives companies a powerful resource to
improve their service, elevate their image and enhance
retention. This is true for both business customers
and the employees of the company, who are a different
kind of customer but no less crucial to success.
While some video surveillance and physical security
systems contribute by offering video analytics and
other technologies designed to improve customer service
and employee experiences, the strength of these
conventional security systems lies in providing comprehensive
real-time coverage and alerts.
Predictive analysis technologies have the power
to change this. Designed to analyze a wide variety of
data from multiple systems, new advances in predictive
analysis software and processing power are capable
of delivering greater levels of intelligence and
integration that go beyond traditional models to
improve security by identifying potential threats or
risks before they can occur, enabling organizations
to proactively mitigate or avoid risks. The potential
power of predictive analysis goes far beyond security,
as these systems can also be used to enable more customer-
centric business operations. Where traditional
security systems can generate alarms to alert security
staff of incidents in progress, predictive systems collect
and analyze data from multiple systems such as
access control, video surveillance, human resources,
traffic control and point-of-sale. The goal of this data
analysis is to identify statistical trends that provide
correlation between elements and alert users of situations
in which an event is more likely to occur. Predictive
analysis can also identify and forecast which policies
are effectively enforced and which are ineffective
within the current systems.
Integrating predictive analysis with physical security
also identifies new customer service opportunities.
This customer-centric security model helps
companies to foresee customers’ and employees’ actions,
desires and needs, and to proactively provide for
these, rather than reacting to them after the fact.
Given the massive amount of data we continue to
generate from an ever-increasing number and variety
of systems, it can be incredibly challenging to collect—
let alone identify and analyze—the right data
from the right sources to provide the necessary information.
One of the many benefits of Physical Identity
and Access Management (PIAM) systems is its ability
to capture, extract and analyze the broad spectrum of
security data from physical and logical security systems,
as well as non-security systems.
From the virtual sea of data that multiple disparate
systems generate, PIAM systems uncover patterns
and routines by discovering connections between
seemingly unrelated data. These connections are then
analyzed to generate a deeper level of understanding
of the relationships—many of which may have been
missed without predictive analysis—between people,
events and more.
This high-level intelligence PIAM generates is
changing the way organizations operate. Physical
security is now able to provide intelligence and analytics
that allow an organization to make decisions
that affect their bottom line, while also ensuring the
best possible service and experience for both customers
and employees. Based on this deeper level
of intelligence, organizations can create, implement
or improve standardized policies and procedures to
enable higher levels of operational efficiency across
the enterprise to improve customer service and grow
their bottom line. In short, the advanced predictive
analysis capability of today’s PIAM software enables
organizations to harness the power of customer-
centric security, transforming traditional security
into a proactive, strategic process that plays a more
integral role in organizations’ customer service—and
potentially its future growth.
A significant element of predictive analysis is the
use of metrics. As an example, metrics can paint a
complete picture of the number of visitors who enter
a building or store during specific time periods, where
they go within that facility, what percentage make
purchases, how long it takes to process those visitors
and more. By combining these metrics, organizations
can understand and manage resources more effectively
by adjusting staffing levels as needed.
Another key element of predictive analysis is to analyze
behavior, a function that has proven to be highly
effective for providing security and risk mitigation, but
it can also play a significant role in customer-centric
security as well. For security and risk management,
this process involves analyzing an individual’s behavior
in conjunction with other related data and events
to uncover predictive indicators. Based on this analysis,
individuals are assigned a risk score to identify their
potential inherent threat to an organization. Using the
same data and performing the same type of analysis
simultaneously, PIAM systems can be programmed to
assign a value score to customers and employees to determine determine
individuals’ inherent value to the organization.
For example, a frequent guest at a hotel chain may customarily leave his room
at dawn and return before noon. Using predictive analysis, the hotel can take measures
to make sure his room is serviced early in the day. Another example might be
an employee who routinely parks in a location that requires her to walk around the
building exterior at night, which might pose a safety risk to that individual. Using
the information uncovered by predictive analysis, her employer can provide her with
access rights to exit through a closer door to reduce the potential risk to her safety.
Predictive analytics also can provide information and intelligence that organizations
can use when it comes time to expand their facilities to accommodate growth.
With growth there is always the risk of expanding too quickly and putting a strain
on financial and physical resources. By analyzing data on how employees and commuters
are using office space, the organization can determine precisely how much it
needs to expand in order to accommodate current and future growth, which helps
control costs and ensures the most efficient spending on expansion. The organization
can also take proactive steps to ensure that expansion is accomplished with
minimal disruption to employees’ productivity and comfort during the process.
As PIAM and Physical Security Information Management (PSIM) technologies
offer more ways to accrue data and increase interactions for security’s “customers”—
the management team, employees, contractors, customers and visitors—
mobile devices and apps are facilitating the revolution, changing the way CSOs
and other security professionals manage and provide security to organizations.
Already a fixture in the lives of people around the globe, mobile devices and apps
are now helping to amass data, track systems and automate formerly complex
and cumbersome procedures with self-service for end users, audit certification by
access area owners, management dashboards and improved situational awareness.
This new, more customer-centric security has simplified and improved overall risk
mitigation while also expanding ROI benefits into compliance, customer service,
automation efficiency and cost awareness.
In order to be successful, today’s businesses must adopt the customer-centric
security model, leveraging the potential power of all available data and resources
to identify proactive steps they can take to improve experiences for both their customers
and their personnel. Big data analysis has been increasingly adopted across
multiple industries to create this customer-centric model. For example, using predictive
analysis, retailers can analyze customer purchase behavior, financial are
able to develop new products aimed at customer retention, and sports teams can
adjust ticket prices and predict which season ticket holders are most at risk of defections.
Even healthcare organizations are using big data analytics to assist with
patient diagnosis and treatment.
The data necessary for customer-centric security is available; it’s just a matter of
collecting and analyzing it, which is too great a task to be performed manually or by
security systems alone. With predictive analysis, big data becomes a big asset to the
user. Capitalizing on the power of PIAM systems to use data generated by multiple
disparate systems allows organizations to take advantage of the many benefits customer-
centric security offers. With the capability to effectively use the vast amount
of available data, organizations can maximize the ROI of their
security systems by increasing security while arming themselves
with the intelligence they need to deliver the exceptional customer
service and an optimal employee experience that is critical to the
success of their business.
This article originally appeared in the September 2015 issue of Security Today.