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User behavior analytics: definition, methods and use cases

User Behavior Analytics (UBA) is a data-driven process that analyzes user behavior within digital environments to detect anomalies and enhance security. This article explains UBA and its broader form, User and Entity Behavior Analytics (UEBA). Discover how UBA is applied in various industries, including website optimization, product development, marketing, and user engagement. Explore a case study of Screeb's collaboration with Nespresso to improve customer experience using UBA. Implement best practices for successful user analytics and drive business growth.

Metrics & Analytics
Photo de Clément Gauthier
Clément Gauthier
User behavior analytics: definition, methods and use cases

What is user behavior analytics (UBA)?

User Behavior Analytics (UBA) is the process of collecting and analyzing data on the behavior of users within a digital environment, such as a website, application, or network. UBA is increasingly referred to as user and entity behavior analytics (UEBA) to reflect that the user is just one category of entities with observable behaviors on modern networks. Other entities can include processes, applications, and network devices.

By monitoring and analyzing user behavior, UBA systems can detect abnormal patterns of activity, such as unauthorized access attempts, unusual login times, and suspicious data transfers. With the application of user behavior analytics software and tools, you will help your company in the process of identifying and responding to security threats and other risks more quickly and effectively.

UBA can be used in a variety of settings, including enterprise networks, e-commerce platforms, and financial institutions. It can also be used to improve user experience and optimize business processes by identifying customer issues, like difficulties or frustrations.

What is UEBA and what are the differences with UBA ?

UEBA stands for User and Entity Behavior Analytics, which is an advanced form of UBA (User Behavior Analytics). The main difference between UBA and UEBA is that while UBA focuses on the behavior of individual users, UEBA takes a broader approach by analyzing the behavior of not only users but also entities, such as devices, applications, and systems.

UEBA solutions analyze data from various sources like logs, network traffic, and also user activity, in order to identify anomalies in behavior that may indicate security threats. UEBA solutions are very advanced because it's including advanced machine learning algorithms to detect threats that may go unnoticed by traditional security systems.

In summary, while UBA focuses solely on user behavior, UEBA takes a more comprehensive approach by incorporating both user and entity behavior analysis.

How and why do we apply User Analytics?

User analytics is applied in various industries and settings to gain insights into user behavior and improve user experience. By analyzing user behavior, organizations can identify areas where users may be experiencing difficulties or frustration, take steps to improve their product, and then, improve user experience. User analytics allows you to personalize experiences and better understand customer needs. You can also use the data to target the right audiences with marketing campaigns.

There are several ways in which user analytics is applied :

  • Website and application optimization: User analytics can be used to monitor and analyze user behavior on websites and applications, such as clicks, scrolls, and page views. This information can be used to identify areas where users may be experiencing difficulties or frustration and take steps to improve the product and the user experience.
  • Product development: To gain insights into how users interact with products, such as how often they use certain features, user analytics can be useful. This information is used to inform the product development team and improve user experience.
  • Marketing: User analytics is useful to gain insights into user demographics, behavior, and preferences, which is used to inform marketing strategies.
  • User engagement: To increase user engagement and retention, user analytics is used to monitor and analyze information such as how often users return to a website or application, or how long they spend using a particular product.
  • By increasing the number of insights into user behavior and preferences, organizations can make informed decisions that lead to better user experiences and increased customer satisfaction to drive business outcomes.

    Use Cases

    Case Study Screeb x Nespresso

    Nespresso is a Swiss-based company that specializes in producing and selling premium coffee and coffee-making machines. Overall, Nespresso has built a strong reputation for its premium coffee and innovative machines, and its commitment to sustainability and responsible production practices has helped to set it apart from other coffee brands.

    This company has an omnichannel strategy that aims to provide a seamless and consistent customer experience across all channels, whether online or offline. One key aspect of their plan is their e-commerce platform, which allows customers to browse and purchase Nespresso products online. The platform is optimized for mobile devices, providing a convenient way for customers to order coffee and machines from anywhere. Nespresso also has physical retail stores, called boutiques, located in major cities around the world.

    In 2021, Nespresso's product team needed to significantly increase their collection of qualified feedback on their e-commerce platform, in order to identify and analyze the behavior of their consumers, allowing the identification of problems to solve and then have as much data as possible to improve their product and increase their customer satisfaction. To achieve this goal, Nespresso trusted Screeb and the best user behavior analytics tools and software on the market.

    In collaboration with Screeb, Nespresso focused on improving the customer experience by easing the process of purchasing products and reducing friction points that can cause frustration or dissatisfaction.

    By focusing on easing the customer experience and reducing friction, Nespresso’s teams work towards increasing customer satisfaction and loyalty, which can ultimately lead to improved business performance and growth for Nespresso.

    Learnings :

  • Collecting feedback in-app is more convenient than email because it is more timely, user-friendly, provides context, and allows for quicker and more personalized responses.
  • Importance of collecting feedback from customers in a timely and efficient manner. Using Screeb to collect analytics and customer feedback while using the website provides valuable real-time insights into their experience and detects any issues they may be encountering.
  • Conclusion

    It's very important to take into account that User Analytics cannot be optimal without following specific exemplary protocols. Indeed, it is crucial to implement data collection in an honest and transparent manner, to observe strict confidentiality of users' personal information, as well as to guarantee the accuracy, quality, and reliability of the data collected.

    Ultimately, the analysis of the various Use Cases related to User Analytics demonstrates the essentiality of this practice to understand and improve the user experience and optimize the performance and productivity of the company, while retaining the loyalty of an appreciative clientele. Thus, it is essential to diligently observe best practices to ensure the effectiveness and relevance of this essential practice.

    As we saw with this Nespresso Case Study, by using the right user analytics tools like those of Screeb, it is possible to collect qualified and relevant feedback at the right time, in order to improve the product and customer satisfaction.

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