Using Student Success Analytics and Data to Prevent Attrition Before It Starts

Some students stop showing up long before they officially leave. The signs are there—an assignment missed, a form left incomplete, a shift in tone during advising—but without the right tools, those signs go unnoticed.

In a time when student persistence is anything but guaranteed, intuition isn’t enough. Institutions need real insight. That’s where enrollment analytics and predictive tools come in. With a strategic approach, data informs timely, student-centered support that goes far beyond traditional reporting.

Many early signs of disengagement never show up in a spreadsheet. Students might quit checking email, skip a registration deadline, or stop engaging in other ways before withdrawing academically. These patterns often fall through the cracks unless there’s a system in place to detect and interpret them.

Predictive analytics shifts the focus from reflection to anticipation, allowing teams to identify risk early and support students before small setbacks escalate.

Tools like Liaison Othot, a predictive enrollment modeling platform, make it possible to identify which students may need outreach long before they ask for help. By using artificial intelligence and machine learning, Othot delivers both predictive and prescriptive analytics that span the entire student lifecycle, from recruitment and financial aid to retention and persistence. Institutions can identify at-risk students early, receive real-time insights, and act on recommendations that align support strategies with what students actually need. Instead of relying on assumptions, teams make decisions grounded in data.

Data only makes a difference when it leads to something meaningful. When insights are shared with the right people—and paired with practical tools—institutions can turn analytics into a persistent, real-time support strategy.

Here are a few ways higher education data is being used to drive impact on campuses:

• Advisor check-ins triggered by engagement scores | When a student’s digital activity slows or they miss check-ins, the platform flags the risk, prompting a personalized outreach from their advisor.
• Financial aid reminders based on incomplete forms | A missed FAFSA deadline cues a thoughtful reminder that prioritizes care, making students feel supported rather than monitored.
• Registration prompts tailored to past behavior | If a student has historically registered late or dropped courses mid-term, targeted messages can help them plan ahead before trouble arises.
• Retargeting campaigns for disengaged students | Students who stop engaging with messaging mid-campaign can receive revised content based on what’s known about their preferences or concerns.

Each example represents a shift from reaction to prevention and from assumptions to insight.

Data must be accessible to affect change, and that means building tools that bring it out of silos. In a connected ecosystem, dashboards can be viewed and filtered by academic advisors, financial aid counselors, student affairs teams, and enrollment leaders. Everyone sees what matters, when it matters.

This visibility is essential to avoiding duplicated efforts and to delivering support that feels coordinated. CRM reporting can show which campaigns led to engagement, which messages resonated by student type, and where gaps still exist.

Visualizations also help leadership allocate resources more effectively, so high-need cohorts get more focused attention without adding friction to day-to-day workflows.

Technology is important, but adopting a data-first approach also requires the right mindset. Data must be treated as a tool for support and connection, not control.

Creating a culture of data-driven support starts with demystifying analytics. That can include celebrating small wins by showing team members and other institutional stakeholders how data-guided outreach helped a student re-enroll or stay on track. It can also mean inviting staff to participate in future student success initiatives.

Staff should be able to analyze and interpret data. When advisors, enrollment managers, and counselors collaborate on what the numbers mean and what to do next, the result is a smarter, more adaptive student success model.

To understand whether analytics are making a difference, institutions need to track the right metrics. Key measures of effective student success analytics include:

• Yield and persistence by risk segment | Are students flagged as high-risk persisting at higher rates due to targeted outreach?
• Response rates to personalized campaigns | Are students engaging with messages tied to their actual behavior and needs?
• Longitudinal tracking by cohort | Are students who received proactive interventions persisting over time, and how does that trend compare across terms?

When analytics are paired with action, the outcomes speak for themselves: Students stay longer, advisors work more strategically, and institutions gain clarity on how to sustain both.

Students rarely disengage without warning—they give signals. But without the right insights, those signals fade into the background. Analytics make it possible to catch them early, interpret them wisely, and respond with the kind of care that keeps students moving forward.

By embedding predictive tools and smart reporting into campus workflows, institutions can shift from a static strategy to a living one that adapts to student needs and reinforces a culture of trust.

Explore how Liaison’s analytics tools and student success frameworks can help your institution act on insight, build stronger connections, and improve persistence across every stage of the student journey. Contact us today.