Understanding how users interact with websites and apps is the key to improving digital experiences.
Unfortunately, collecting and acting on that data has always come with some degree of delay—a lag between when something happens on a customer’s screen and when a business can do something about it. By the time a team sees that a user struggled to complete a task, clicked in frustration, or backed out of a form, the session is over and the chance to help a frustrated user in the moment is long gone.
The best time to help a user is in the moment
Many personalization strategies are designed around the assumption that users will return. Delayed actions, like showing tailored content the next time someone logs in or sending a follow-up email, can be effective if you know who the user is and you expect them to come back.
That approach doesn’t work when the majority of your traffic is anonymous, which has become increasingly common, especially in industries like retail, where users often arrive from social media platforms. When you don’t know who the visitor is or whether you’ll ever see them again, your only chance to make a good impression is to help them in the moment.
Even when users are known, there are still time-sensitive situations that can’t wait. If someone is trying to complete a purchase, resolve a service issue, submit an important form, or understand a policy that will impact their decision, a few seconds of friction can derail the entire journey and even lose you a customer for life.
Closing the window between user friction and response
Low-latency streaming gives you access to behavioral intelligence about your users within seconds of it happening. Instead of waiting hours or more to see real user data, you’re alerted to friction signals while the session is still in progress and while there’s still time to do something about it.
Fullstory makes this possible by continuously capturing behavioral data and processing it in near real time. Behind the scenes, you configure the patterns you want to watch for—a sequence of actions, a single event, or a combination of signals—and Fullstory constantly matches those conditions against all incoming user traffic.
When a configured pattern is detected, the system can automatically dispatch an event. That event might trigger a workflow, send a signal to another platform like Adobe or Braze, or surface session context through an API. And this is all possible without any custom code.
Built for action, not just analysis
Low-latency streaming powers some of Fullstory’s most impactful capabilities, including Streams, which dispatch events to other systems in near real time, and the Session Summary API, which returns up-to-the-second behavioral context for use in downstream tools like AI assistants.
What sets Fullstory apart is its unique combination of full data capture and a configuration-driven workflow. You don’t require engineering support or manual instrumentation to start leveraging streaming. Teams simply define what they care about—such as an event, a sequence, or a friction pattern—and Fullstory automatically tracks and responds to those signals. This enables real-time awareness without the complexity traditionally associated with real-time data processing.
Use cases: How teams power better digital experiences with low-latency streaming
Improving customer support
Organizations utilize low-latency streaming to provide differentiated support experiences. For example, in regulated industries like responsible gaming, certain in-session behaviors can trigger workflows that escalate the session to the appropriate response team. Other organizations identify VIP users and route them to dedicated support teams when their sessions meet specific behavioral criteria.
Detecting and responding to risk
In addition to support, low-latency streaming plays a critical role in fraud detection workflows. When risky behavior is detected based on predefined patterns, that information is dispatched to downstream systems quickly enough to enable real-time mitigation. Timing is crucial in these scenarios, and streaming ensures the signals are surfaced before the session concludes.
Powering AI-driven engagement
Streaming is also employed to trigger AI processes. When a session matches defined conditions, an external AI system can use Fullstory’s Session Context and Summary API to pull the recent context from the session. This context is pivotal for generating timely next steps or messaging. For example, some internal sales processes leverage this feature to guide outreach based on live product engagement, enhancing the personalization of customer interactions.
Low-latency and warehouse exports: different tools, different jobs
Fullstory supports both warehouse data exports (delivered hourly) and low-latency streaming. Each serves a different purpose.
Warehouse exports are designed for bulk analytics and modeling. They include all captured session data and are well-suited to training personalization models or analyzing aggregate behavior patterns.
Low-latency streaming, on the other hand, is designed to react to individual user behaviors in near real time. It enables triggering lightweight, tailored experiences—like emails, personalized offers, or proactive support—based on specific user actions during the session.
→ Ready to harness the power of your data? Discover how Fullstory Anywhere: Warehouse can help you understand customer journeys, accelerate cross-functional analyses, and fuel AI models.
Start experiencing the benefits of low-latency streaming
Fullstory’s low-latency streaming moves behavior data from retrospective analysis into active engagement. With Streams and the Session Summary API in place, organizations no longer have to infer intent or wait for post-session insights; they can respond while the user is still on their site.
For digital teams looking to proactively support current users, personalize in-session experiences, or feed real-time AI agents with context, low-latency streaming is increasingly foundational.
→ Request a demo to see how Fullstory can help you transform digital experiences in real time.
