Use Cases

Education

Student Retention Risk Intelligence and Intervention Management

Surface retention risk earlier and connect each signal to an owned intervention

Model6 helps institutions identify students who may be at risk of disengagement, stop-out, or withdrawal using approved historical and current data. It can surface contributing signals and support prioritization across student success and advising teams.

Agent6 can route the identified case, assign ownership, track outreach and referrals, and record follow-up through closure. The result is a clearer operating path from risk visibility to human-led action.

Why This Use Case Matters

Student risk develops across several weak signals

Reduced LMS activity, missed registration steps, incomplete assignments, advising no-shows, financial friction, and unanswered outreach may appear in different systems. Each signal can look minor until the combined pattern becomes serious.

Alert volume can outgrow advisor capacity

Broad rules and disconnected notifications can create more cases than teams can meaningfully review. Advisors need a prioritized view with enough context to decide where attention is most urgent.

Support fails when ownership and closure are unclear

A risk flag has little value when no team owns the next action, referrals disappear between offices, or leaders cannot see whether the student responded, received support, or still needs follow-up.

Why This Use Case Matters

Every early student-risk signal creates an opportunity for timely support. Explore how Bay6 AI helps institutions identify possible disengagement earlier, prioritize advisor attention, and coordinate measurable, human-led interventions.

Many institutions can see pieces of student risk. The operating problem is turning those signals into consistent, timely, and measurable support across advising, student success, financial services, academic support, and other teams that influence persistence.

Risk signals are distributed across SIS, LMS, CRM, advising, attendance, and support systems.
Teams may rely on lagging reports or manually assembled lists.
Advisors can receive alerts without a clear explanation or next action.
High caseloads make consistent prioritization difficult.
Referrals and follow-up can lose ownership across departments.
Leadership often sees retention outcomes after the intervention window has narrowed.

Model6 can analyze approved student data to estimate disengagement or withdrawal risk for a defined population and time horizon. It can provide risk bands and contributing signals that help authorized teams prioritize review.

Agent6 can convert the approved signal into an owned case, route it to the right team, create tasks, record outreach, manage referrals, and track status. Connect6 can support approved student-facing responses, appointment guidance, and resource navigation where appropriate.

Operating principle: The model supports prioritization. Advisors and student success professionals interpret the context, select the intervention, and remain accountable for the student-facing decision.

How the Retention and Intervention Workflow Works

The workflow links institution-specific risk intelligence to an owned, human-led intervention process.

Define the retention outcome

Set the student population, prediction window, outcome, permitted data, review owner, and intervention boundary.

Assess data readiness

Evaluate approved SIS, LMS, CRM, advising, registration, financial, and support data for quality and relevance.

Generate risk signals

Model6 produces risk bands and contributing indicators for eligible students.

Prioritize human review

Teams review the context and identify the students who require attention first.

Create and route the intervention

Agent6 assigns ownership, prepares context, creates tasks, and connects referrals or outreach steps.

Track response and outcome

Teams monitor contact, appointments, referrals, engagement movement, case status, and model performance.

What Model6 and Agent6 Help Retention and Advising Teams Do

The use case combines predictive insight with operational follow-through.

Retention risk scoring

Estimate risk for a defined student population using approved institutional data.

Contributing-signal visibility

Show the factors that may explain why a student requires review.

Priority and caseload views

Organize cases by risk, program, cohort, advisor, intervention need, or ownership.

Owned intervention cases

Create tasks, assign responsibility, and maintain case status.

Referral and outreach coordination

Connect advising, support resources, appointments, and cross-functional handoffs.

Outcome and model monitoring

Measure intervention completion, engagement movement, retention outcomes, drift, and segment performance.

What Institutions Can Improve

The objective is earlier, more consistent intervention with clear ownership and measurable follow-through.

Timing of student-risk identification
Advisor prioritization and caseload focus
Intervention ownership and task completion
Referral handoff and follow-up continuity
Student response, appointment, and engagement recovery
Visibility into model quality, case outcomes, and retention patterns

The strongest outcome is: A retention signal becomes valuable when the institution can review it, assign it, act on it, and confirm what happened next.

Pilot Measurement Areas

Use a defined baseline and measure the workflow before expanding scope.

Measurement Area	What to Track
Data readiness	Coverage, completeness, timeliness, consistency, and permitted use of selected data sources.
Model performance	Calibration, precision, recall, false positives, false negatives, stability, and segment performance.
Priority usefulness	Advisor agreement, context quality, review acceptance, and override reasons.
Intervention execution	Cases assigned, students contacted, appointments scheduled, referrals completed, and tasks closed.
Student movement	Response, registration, LMS activity, appointment attendance, support use, and other approved indicators.
Governance	Access, model versions, drift, bias review, exceptions, decisions, and audit records.

From Late Risk Detection to Coordinated Intervention

Each signal is visible in a different system and reviewed separately.
The student appears in broad reports after the risk becomes more visible.
Advisors reconstruct context and decide ownership manually.
Leadership sees final retention outcomes but limited evidence of intervention completion.

Without Bay6 AI

Model6 evaluates the combined pattern within the institution-defined retention model.
The student is surfaced earlier in a prioritized advisor review queue with contributing signals.
Agent6 prepares the available context, assigns the approved owner, and initiates the configured next step.
The workflow links prediction, review, outreach, referral, closure, and outcome for measurement.

With Bay6 AI

FAQs

Have questions? Browse our FAQs to learn more about how Bay6 AI works and how it supports your organization’s goals.

What is student retention risk intelligence?

It uses approved historical and current data to estimate which students may be at risk of disengagement, stop-out, or withdrawal within a defined period.

How does intervention planning differ from a retention dashboard?

The workflow connects risk signals to ownership, tasks, outreach, referrals, follow-up, and closure rather than stopping at reporting.

What data can be used?

Depending on institutional approval, the model may use SIS, LMS, CRM, advising, registration, attendance, academic progress, financial, communication, and prior outcome data.

Does Model6 make decisions about students?

No. Model6 supports prioritization. Authorized advisors and student success teams interpret the context and select the appropriate action.

How should an institution start?

Begin with one defined student population, a clear outcome, a limited set of approved data, named intervention owners, and measurable review and follow-up criteria.

Responsible AI and Institutional Oversight

Retention intelligence must support fair, human-led student services. It should not become an automated decision system for academic status, eligibility, access, discipline, or financial support.

Define the model purpose, population, prediction window, permitted use, and intervention boundaries.
Use approved data with clear access, privacy, retention, and quality controls.
Monitor false positives, false negatives, drift, bias, and performance across approved student segments.
Keep outreach, referrals, academic guidance, and sensitive decisions with authorized staff.
Track whether interventions are timely, appropriate, completed, and useful to students.

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Extend the workflow through counselor task creation, follow-up coordination, system updates, document requests, and enrollment operations handoffs.

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Connect Retention Risk to an Owned Intervention Workflow

Assess one student population, the available data, and the current intervention process to determine whether Model6 and Agent6 can create earlier, more measurable support.