What Is Stateful Analytics?

Stateful analytics is an advanced approach to data analytics that accounts for time, sequence, and duration of events from digital experiences.. Because it operationalizes event data to add deep context and precision to business KPIs and customer outcomes, it revolutionizes the way organizations optimize delivery and improve experiences in real time. Stateful analytics provides a comprehensive understanding of a digital product’s performance as well as insights into the Quality of Experience (QoE): it monitors which features users engage with most, tracks the flow of actions within a company’s digital product, and identifies potential pain points. Modeling all these elements is a layered, multifaceted concept that requires stateful task-, user-, and application-level QoE indicators. When experience problems do occur, connecting the dots for effective troubleshooting is also a stateful problem. 

Whether something is stateful or stateless depends on how it handles session information. A stateless protocol does not require the server to retain any information, making it incapable of contextualizing an event in relation to other events. A metric that simply tallies successful logins is stateless. On the other hand, a stateful protocol does require the server to save status and session information, computing complex metrics based on timing, time intervals, sequences, and system states. Examples of stateful interactions include:

• Monitoring the progress of a video being streamed, including buffering and playback position
• Measuring how third parties and APIs directly result in user dropoff across a user funnel
• Verifying user credentials and managing session data to allow access to premium or personalized features

Although it is possible for other analytical processing frameworks to calculate Stateful Metrics using a tabular abstraction (every data point expressed as rows and columns in a table), with such solutions there is always a trade-off between Scale (sampling vs. census measurement), Speed (real-time vs. hours or days RCA), and Cost (1 server vs. 7 servers to sessionize and analyze raw data). Since no existing big data platform has been able to resolve this trade-off and compute such stateful metrics in real time and at scale, the Conviva team dove into identifying and solving that limitation. We discovered that the tabular approach may work well for individual data points, but not for representing or trending changing data states over time.

Conviva created a new abstraction, called Timeline, which offers a more efficient way to write queries and model dynamic processes. Conviva’s Time-State Technology represents event stream data as timelines, using a set of timeline operators to compute stateful metrics in real time. By handling the complex data and analysis, our stateful analytics capabilities allow companies to focus on their primary business problem—that is, delivering the best possible user experiences.

Core Principles of Stateful Analytics

Built upon the principles of real-time insights and proactive issue resolution, stateful analytics is a transformative approach to ingesting, activating, and operationalizing data at scale. It’s a powerful tool for digital products to enhance performance and user experience. Stateful analytics provides a level of context and insight that enables dynamic optimization, capturing everything a user does or experiences from the moment they open an app to the moment they exit it. Real-time insights allow organizations to adapt and respond to changing conditions.

If we use video streaming providers as an example, stateful analytics enables them to track the quality of the streaming experience, promptly identify and address issues such as buffering or playback errors, and deliver a seamless and consistent experience. Buffering often occurs because of insufficient bandwidth or network congestion, both of which stateful analytics can detect. That enables platforms to take action to mitigate buffering instances. Playback errors can be the result of server issues, software bugs, or compatibility issues. Users often experience them as freezes, stutters, or interruptions during video playback. Stateful analytics can pinpoint the root cause of the issue, making a prompt diagnosis and resolution possible. Being able to anticipate—and solve—quality issues before they impact users is a powerful tool for platforms.

Stateful Analytics Solve Business Problems

The many applications of stateful metrics within digital services underscore their significance as a driving force behind performance optimization. Specifically, stateful analytics can be used for:

• Efficient Resource Utilization: Ensuring efficient bandwidth allocation and reducing operational costs (by focusing only on areas that directly impact user satisfaction and engagement).
• Customer Service: Because of the detailed data available, operations and customer service teams can solve problems faster—and reduce the total number of calls by preemptively solving issues.
• User Retention Strategies: Analyzing user behaviors and satisfaction over time to enhance retention and user engagement.

Digital platforms can use stateful metrics to understand performance. Maintaining optimal performance during peak usage times, anticipating fluctuations in user demand, and scaling efficiently all involve complexity and agility. Through continuous analysis of real-time data streams and the context provided by historical trends, platforms can navigate these complexities with precision and confidence.

By understanding how users interact with specific features and functionalities, along with any issues that they encounter (such as slow login time, video playback failure, etc.), operations teams can prioritize specific enhancements and updates based on actual usage patterns. Real-time observations capture ongoing user interactions and system metrics as they occur. Anticipating resource needs based on historical patterns enables video streaming services to allocate their resources most effectively. Using accurate and relevant data to make decisions about resource allocation ensures that platforms can handle fluctuations in user demand, maintain optimal performance during peak times, and scale to accommodate growing user bases.

Conviva’s Pioneering Approach to Stateful Analytics

Conviva’s innovative approach to implementing stateful analytics empowers organizations to monitor, analyze, and optimize performance with unparalleled depth and precision. In today’s data-driven landscape, stateful analytics provides a crucial, comprehensive view of systems, applications, and processes.

Whereas observability tools provide performance measurements of system components, quality of experience takes in all user data and models events across critical user flows with business context. This eliminates blind spots and brings data to life in a way that represents the true customer experience, precisely aligned to business workflows. Conviva takes a radically different approach in redefining observability, focusing on experience-centric operations (ECO). By doing this, it makes the quality of experience that users and customers observe in the real world the ‘first-class citizen’ of observability.

Traditional monitoring and logging tools don’t fully capture a user’s journey. Conviva’s time-state technology allows organizations to capture all of the necessary technical and performance data, while also having the capability to monitor and optimize each individual user experience in real time. Conviva’s technology delivers stateful insights at internet scale. That means our platform can process, analyze, and manage data streams from millions or even billions of users, devices, and interactions—and do so in real time, computing as many as 12 billion stateful metrics per minute. 

Digital platforms cater to wide audience bases, and their services must be able to accommodate fluctuations in demand as well as regional preferences. Platforms also need to be able to handle traffic surges without experiencing performance degradations or service disruptions, or they risk losing customers. 

Internet-scale capabilities enable platforms to capture and analyze vast amounts of data and to do so at a granular level. Knowledge of each user interaction in real time is the kind of insight platforms need to successfully personalize content recommendations, optimize streaming quality, and meet the needs and expectations of each user. Delivering stateful insights at internet scale gives platforms the ability to identify trends, patterns, and anomalies across huge data sets, empowering them to make data-driven decisions that drive engagement, retention, and growth.

User-generated data, such as viewing history, device data, or content interactions, can be transformed into meaningful insights that have tangible business implications. These insights can also be tracked over time, helping to identify changes in user behaviors and informing business processes, improvements to user experiences, and more. Trending data over time provides context that snapshots of data, capturing individual moments in time, cannot. 

By aggregating identified events into dynamic metrics that mirror user interactions on a platform, Conviva’s approach offers a comprehensive view of system behavior. It promptly standardizes, processes, and evaluates data, swiftly surfacing essential data so that organizations can monitor patterns and simultaneously assess real-time user experience and long-term user behavior. 

Stateful Analytics Across Industries

Stateful analytics provide value across a diverse range of industries, enabling many kinds of organizations to optimize their processes and make better-informed decisions. The combination of real-time and historical data gives companies the insights they need to drive innovation, improve efficiency, and deliver better experiences to their customers. Beyond streaming, stateful analytics can be used in:

• E-commerce: By analyzing historical transaction data alongside real-time user interactions, e-commerce platforms can optimize inventory management to meet consumer demand effectively. Stateful analytics also play a crucial role in enhancing the overall shopping experience, thanks to seamless login, cart, and checkout flows.

• Finance: With knowledge of historical transaction data and real-time market trends, financial institutions can identify anomalies, detect potential fraud, and make data-driven decisions to mitigate risks and enhance customer satisfaction. Stateful analytics are instrumental for fraud detection, risk management, and customer relationship management.

• Healthcare: By integrating present patient data with historical medical records, healthcare providers can identify early warning signs, predict disease progression, and personalize treatment plans to improve patient outcomes and reduce healthcare costs. Stateful analytics aid in patient monitoring, disease prediction, and treatment optimization.

• IoT: Analyzing real-time sensor data and historical performance metrics enables owners and operators to predict equipment failures, optimize production schedules, and ensure product quality, minimizing downtime and maximizing operational efficiency. Stateful analytics can be used for predictive maintenance, supply chain optimization, inventory management, and quality control.

• Transportation and Logistics: Combining real-time GPS data with historical traffic patterns and customer preferences helps logistics companies optimize delivery routes, allocate resources efficiently, and provide accurate delivery estimates to customers. Stateful analytics facilitate route optimization, fleet management, and delivery tracking.