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Applications, Behavior Learning and the Personal Touch

Though online businesses can’t offer the same sort of “human touch” as the staff at a brick-and-mortar store, they can go quite a long way toward personalizing and improving the end-user’s experience. End-user behavior learning technology provides the business with a view into how each end-user interacts with their online business services to ensure a superior user experience.

The business learns about user experiences based on past online activity, and quickly and proactively adjusts to meet expected levels of service regardless of the conditions or variables, such as for online shopping during the holiday season, when traffic peaks on many sellers’ websites.

Behavior Learning: The Key to Positive Customer Experiences

Superior, personalized online experiences happen because technology enables the business to deliver desired results based on learned behavior. IT has its own model and technology to identify “abnormal” behavior of applications and systems that affect the user’s experience and behavior. End-user behavior learning tells IT (and application owners) about each person using a specific technology or application and how the application performance affects the user’s actions.

Tools support end-user and application learning by leveraging statistical process control to gather data from multiple sources, establish patterns of behavior, and proactively detect subtle changes in that behavior. If you can monitor end-user and application behavior, and establish norms, then you can more proactively detect a performance issue. The technology can determine the impact on users and the business, isolate the cause of the problem, and drive corrective actions.

Behavior learning technology understands the systems, detects deviations from normal behavior, and provides fewer, earlier, and more accurate alerts. For example, a sluggish response time is a clear indicator that something is “misbehaving” in your infrastructure. End-user behavior learning technology tells you the expected response time based on the time of day, the day of week, load on the system, location of the user, and so on.

By understanding the expected behavior of the applications under various conditions, you can detect a slowdown before a user calls the help desk or abandons your site. You can also quickly assess the impact of new or modified application features, and changes in user traffic or system configurations.

The Starting Point: Behavior Monitoring

By monitoring and learning the normal behavior of your applications and your end users, you can understand what and how elements are being accessed and who is accessing them. If a change in end-user behavior occurs at the time of a slowdown, an alert is generated to notify an administrator or operator. By monitoring user and application behavior and establishing the norms for any given time period, you can proactively determine when changes occur in performance or behavior over time.

Real and “Synthetic” Users
End-user monitoring should extend to both the real user and the “synthetic” user. With synthetic transactions, you can simulate types of actions — setting up critical user scenarios and running them repeatedly to establish a baseline for comparing the performance for that same series of steps from one hour or day to the next. This is especially useful for assessing the availability of key scenarios and determining whether changes to the application or to the environment in which it runs will affect the end-user experience.

When combined with “real” user monitoring, you can determine if performance is impacted by other criteria, such as the volume or location of users, the actions the user takes, the use of mobile or non-mobile devices, or by a change in the application or environment that modifies or introduces new critical user scenarios.

Speed Matters
Advanced application performance monitoring solutions detect problems based on real end-user response times as soon as a single user begins to experience them, capturing all the data necessary to quickly prioritize, diagnose, and resolve the problems. You can know what problems are likely to impact your users and how to prioritize and assign them based on the issue source and the potential criticality and severity of user and business impact. Behavior learning solutions evaluate this data, identifying behavioral patterns so that you know when application response times and the end-user experiences are becoming slower ― or faster ― than usual.

End-User Behavior as a Source of Business Information

If a slowdown occurs in the volume of transactions completed, you can correlate that type of business information to the end-user and application response times. You can quickly determine if there is a potential problem and proactively investigate the issue. The performance of all the individual service components may appear to be satisfactory. Yet combined the services being delivered may not be performing satisfactorily to the end user. This combination gives you awareness of the experience and the potential impact on your business.

Monitoring Your Services in the Cloud
Understanding the real user experience is essential for monitoring your services in the cloud. In the cloud, you don’t always have access to the infrastructure and applications being delivered. If, however, you monitor the real user experience when trying to access the cloud, then you have a better sense of whether you and your customers are getting the service you paid for and expect.

More than Just a “Nice Touch”

Behavior-learning technology observes behavior with the goal of providing a positive customer experience. The technology empowers you to do a better job of identifying the root cause of problems and resolving application and infrastructure issues before they impact critical business services. The result will be greater customer satisfaction and loyalty, as well as an increased ability to attract new customers.

About David Williams

David Williams is a Vice President of Strategy in the Office of the CTO, with particular focus on availability and performance monitoring, applications performance monitoring, IT operations automation, and management tools architectures. He has 29 years of experience in IT operations management. Williams joined BMC from Gartner, where he was Research VP, leading the research for IT process automation (run book automation); event correlation and analysis; performance monitoring; and IT operations management architectures and frameworks. His past experience also includes executive-level positions at Alterpoint (acquired by Versata), IT Masters (acquired by BMC Software), and as vice president of Product Management and Strategy at IBM Tivoli. He also worked as a Sr. Technologist at CA for Unicenter TNG and spent his early years in IT working in computer operations for several companies, including Bankers Trust.

About Leslie Minnix-Wolfe

Leslie Minnix-Wolfe is the Lead Solutions Marketing Manager for Proactive Operations and Application Performance Management solutions at BMC Software. Minnix-Wolfe has more than 25 years of diverse development and marketing experience, primarily in the IT systems management domain, with a broad base of experience, especially in Business Service Management and predictive analytics. She previously held product and development management positions at several high-tech start-ups, including Netuitive and Managed Objects.

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Applications, Behavior Learning and the Personal Touch

Though online businesses can’t offer the same sort of “human touch” as the staff at a brick-and-mortar store, they can go quite a long way toward personalizing and improving the end-user’s experience. End-user behavior learning technology provides the business with a view into how each end-user interacts with their online business services to ensure a superior user experience.

The business learns about user experiences based on past online activity, and quickly and proactively adjusts to meet expected levels of service regardless of the conditions or variables, such as for online shopping during the holiday season, when traffic peaks on many sellers’ websites.

Behavior Learning: The Key to Positive Customer Experiences

Superior, personalized online experiences happen because technology enables the business to deliver desired results based on learned behavior. IT has its own model and technology to identify “abnormal” behavior of applications and systems that affect the user’s experience and behavior. End-user behavior learning tells IT (and application owners) about each person using a specific technology or application and how the application performance affects the user’s actions.

Tools support end-user and application learning by leveraging statistical process control to gather data from multiple sources, establish patterns of behavior, and proactively detect subtle changes in that behavior. If you can monitor end-user and application behavior, and establish norms, then you can more proactively detect a performance issue. The technology can determine the impact on users and the business, isolate the cause of the problem, and drive corrective actions.

Behavior learning technology understands the systems, detects deviations from normal behavior, and provides fewer, earlier, and more accurate alerts. For example, a sluggish response time is a clear indicator that something is “misbehaving” in your infrastructure. End-user behavior learning technology tells you the expected response time based on the time of day, the day of week, load on the system, location of the user, and so on.

By understanding the expected behavior of the applications under various conditions, you can detect a slowdown before a user calls the help desk or abandons your site. You can also quickly assess the impact of new or modified application features, and changes in user traffic or system configurations.

The Starting Point: Behavior Monitoring

By monitoring and learning the normal behavior of your applications and your end users, you can understand what and how elements are being accessed and who is accessing them. If a change in end-user behavior occurs at the time of a slowdown, an alert is generated to notify an administrator or operator. By monitoring user and application behavior and establishing the norms for any given time period, you can proactively determine when changes occur in performance or behavior over time.

Real and “Synthetic” Users
End-user monitoring should extend to both the real user and the “synthetic” user. With synthetic transactions, you can simulate types of actions — setting up critical user scenarios and running them repeatedly to establish a baseline for comparing the performance for that same series of steps from one hour or day to the next. This is especially useful for assessing the availability of key scenarios and determining whether changes to the application or to the environment in which it runs will affect the end-user experience.

When combined with “real” user monitoring, you can determine if performance is impacted by other criteria, such as the volume or location of users, the actions the user takes, the use of mobile or non-mobile devices, or by a change in the application or environment that modifies or introduces new critical user scenarios.

Speed Matters
Advanced application performance monitoring solutions detect problems based on real end-user response times as soon as a single user begins to experience them, capturing all the data necessary to quickly prioritize, diagnose, and resolve the problems. You can know what problems are likely to impact your users and how to prioritize and assign them based on the issue source and the potential criticality and severity of user and business impact. Behavior learning solutions evaluate this data, identifying behavioral patterns so that you know when application response times and the end-user experiences are becoming slower ― or faster ― than usual.

End-User Behavior as a Source of Business Information

If a slowdown occurs in the volume of transactions completed, you can correlate that type of business information to the end-user and application response times. You can quickly determine if there is a potential problem and proactively investigate the issue. The performance of all the individual service components may appear to be satisfactory. Yet combined the services being delivered may not be performing satisfactorily to the end user. This combination gives you awareness of the experience and the potential impact on your business.

Monitoring Your Services in the Cloud
Understanding the real user experience is essential for monitoring your services in the cloud. In the cloud, you don’t always have access to the infrastructure and applications being delivered. If, however, you monitor the real user experience when trying to access the cloud, then you have a better sense of whether you and your customers are getting the service you paid for and expect.

More than Just a “Nice Touch”

Behavior-learning technology observes behavior with the goal of providing a positive customer experience. The technology empowers you to do a better job of identifying the root cause of problems and resolving application and infrastructure issues before they impact critical business services. The result will be greater customer satisfaction and loyalty, as well as an increased ability to attract new customers.

About David Williams

David Williams is a Vice President of Strategy in the Office of the CTO, with particular focus on availability and performance monitoring, applications performance monitoring, IT operations automation, and management tools architectures. He has 29 years of experience in IT operations management. Williams joined BMC from Gartner, where he was Research VP, leading the research for IT process automation (run book automation); event correlation and analysis; performance monitoring; and IT operations management architectures and frameworks. His past experience also includes executive-level positions at Alterpoint (acquired by Versata), IT Masters (acquired by BMC Software), and as vice president of Product Management and Strategy at IBM Tivoli. He also worked as a Sr. Technologist at CA for Unicenter TNG and spent his early years in IT working in computer operations for several companies, including Bankers Trust.

About Leslie Minnix-Wolfe

Leslie Minnix-Wolfe is the Lead Solutions Marketing Manager for Proactive Operations and Application Performance Management solutions at BMC Software. Minnix-Wolfe has more than 25 years of diverse development and marketing experience, primarily in the IT systems management domain, with a broad base of experience, especially in Business Service Management and predictive analytics. She previously held product and development management positions at several high-tech start-ups, including Netuitive and Managed Objects.

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...