Analysis Turns Physical Security into Customer Service

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.


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