What is behavior analysis and when is it important?

What is behavior analysis and when is it important?

You are buying a car. Visit a manufacturer’s website for model information, check the deals listed on your local dealer’s website, and then visit the dealer. What information can the sales representative review to understand your purchasing needs and determine the best options to offer you?

The security operations center receives an alert about an employee’s activities on the network. Is the employee learning about different business areas and just working unexpected hours from a remote location? Or is this malicious behavior and the SOC should take action?

These are examples of information that user behavior analysis can provide. Common Use Cases They include increasing business-to-business and business-to-consumer sales, improving the customer experience, detecting anomalies, alerting on risks, and leveraging data from Internet of Things devices to identify dangerous conditions.

Rosaria Silipo, Principal Data Scientist and Head of Evangelism at KNIME, offers this simple definition of behavioral analytics. She says, “Behavior analysis studies people’s reactions and behavior patterns in particular situations.”

Business opportunities in behavior analysis

Behavioral analysis is particularly important whenever a product or service has many people doing many things where there are opportunities to improve outcomes and reduce risk. Examples include customer buying habits on large-scale e-commerce websites, healthcare apps, gaming platforms, and wealth management in banking. Silipo further explains: “The goal is to study the mass of people, and the key is the availability of large amounts of data.”

Kathy Brunner, CEO of Acumen Analytics, refers to research The global behavioral analytics market is projected to reach $2.2 billion by 2026, from $427.3 million, at a compound annual growth rate of 32% from 2022 to 2026.

Brunner shares these insights about business opportunities. “The current focus is mainly retail, and why not? Where I see real transformation is in combining this capability with AI/ML, other advanced modeling technologies, and real-world evidence in healthcare data. Imagine the impact of discovering the best way to get patients into clinical trials, improve drug discovery, and improve patient outcomes with precision and personalized medicine.”

So while behavioral analytics can be a problem if an implementation violates privacy rules or compliance regulations, it can also lead to very positive results for consumers and businesses.

Risk mitigation with behavioral analysis

Behavioral analysis is often used for trading opportunities, but the techniques are equally applicable to identifying and alerting on risks. Behavioral analysis is used in banking to fraud detectionintegrated into AIops tools to help improve incident managementand helps game systems identify cheaters.

Large companies with many employees, contractors, and global vendors also take advantage of behavioral analytics to detect suspicious activity. Isaac Kohen, Teramind’s vice president of research and development, says, “User and entity behavior analytics can identify and alert the organization to a wide range of anomalous behavior. Potential threats may be malicious, inadvertent, or compromised activity by an employee, user, or third-party entity. It is used in many industries to prevent insider threats and analyze user behaviors to meet regulatory requirements.”

The data science behind behavioral analytics often applies to people, customers, and users, but can also be applied to entities operating in large-scale systems.

Todd Mostak, CTO and co-founder of OmniSci, shares this broader perspective: “Behavior analytics is a data-driven approach to tracking, predicting, and leveraging behavioral data to make informed decisions. With shipping delays and supply chain shortages today, behavioral analytics technology can monitor the activity of billions of ships, survey ports, and study global shipping patterns to help experts solve these problems. ”.

The data science behind behavioral analytics

Behavioral analysis is a broad application of data science, machine learning, and AI techniques. Scott Toborg, director of data science and analytics products at Teradata, explains the underlying data science. “Behavior analytics harnesses customer data about who they are (demographics), what they’re doing (events), and who they’re interacting with (connections) for better insights, predictions, and actions. The process consists of segmentation, predictive modeling, and prescriptive action.”

Toborg suggests that behavioral analytics shares many of the same opportunities as data science, but also faces challenges in developing and supporting machine learning models. He continues: “When implemented correctly, behavioral analytics results in better customer experiences, more accurate targeted marketing, and increased engagement. However, there are challenges, such as privacy, model bias, and stereotypes.”

Useful techniques and technologies.

Behavioral analytics is a set of technology, data, and operations practices that target specific business opportunities or are intended to mitigate a set of quantifiable risks. There are many ways organizations can implement behavior analytics. The following list is a subset of the available solutions.

  • Platforms such as content management, e-commerce, and digital experience often include capabilities to support behavioral analytics.
  • Customer data platforms centralize data about customers and their actions while providing integrations to take action across marketing automation platforms, advertising systems, e-commerce, and other platforms.
  • Product analytics and digital experience analytics platforms like Adobe Analytics, Amplitude, Contentsquare, FullStory, Glassbox, Heap, Mixpanel, and Userpilot aggregate usage metrics and provide analytics capabilities.
  • Media, e-commerce, and other content-rich websites should consider smart search platforms including behavioral analytics, recommendation engines, and personalization capabilities.
  • Techniques to experiment and learn from user responses they include A/B testing, user activity logs, activity measurement tools, and customer feedback measurement practices. These aim to optimize activities based on customer segments and people.
  • Application developers can use feature markup to support large-scale A/B feature testing while deploying microservices observability to identify malicious API activities.
  • Organizations may also consider data analytics, analytics process automation, or machine learning platforms to centralize behavioral data and build behavioral analytics capabilities. Some data platforms include Alteryx, Dataiku, Databricks, DataRobot, Informatica, KNIME, RapidMiner, SAS, Tableau, Talend, and many others.
  • IoT, wearable devices, augmented reality/virtual reality, voice-enabled devices, and cameras with computer vision capabilities represent new inputs and data sources for capturing behavioral data.

There is no doubt that more organizations will consider using behavioral analytics capabilities to increase revenue, improve experiences and reduce risk.

Copyright © 2022 IDG Communications, Inc.

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