80%
Lower Error Rates
e:cue benchmark
4 wks
To First Retention Insight
e:cue benchmark
3+
Data Sources Unified Per Student
LMS · SIS · CRM
6 wks
Earlier Signal Detection
vs. manual review
The Challenge
Retention problems are invisible until it's too late.
By the time a student withdraws, the signal was there weeks earlier — a pattern of missed logins, declining assignment completion, a financial aid flag, an unanswered outreach touchpoint.
The problem is that these signals live in separate systems and no one is connecting them. e:cue connects your LMS, SIS, CRM, and support data into a unified student lifecycle model — and trains Cue to surface the patterns that predict disengagement.
What You Get
From data chaos to decisive action.
Re-Engagement
Find the stop-outs worth reaching
Not every lapsed learner is winnable, and not every winnable learner should return to the same program. e:cue identifies who's genuinely re-enrollable.
Attrition Drivers
Identify the patterns behind the churn
Individual saves don't scale. e:cue isolates what's actually driving drop-off, whether it's a course, a cohort, a modality, an advisor, or an acquisition channel. Now, the fix is structural and the next cohort doesn't repeat it.
Intervention Impact
Deploy plays that actually improve persistence
Retention programs consume staff hours and rarely get measured. e:cue tests them against outcomes: which interventions move persistence, which are ritual.
Persistence Forecasting
Put revenue on the retention number
Project term-to-term persistence and completion by cohort with the revenue attached — so retention arrives at the leadership meeting as a business number, not a student-services one.
Retention Risk
Know which students need attention — this week
Attendance, LMS activity, assessment patterns, advisor contact — the signals exist, scattered across systems that don't talk. e:cue joins them and flags the students at risk while outreach can still change the outcome.
How It Works
How e:cue gets you to insight.
01 We Integrate
We connect your student lifecycle data.
Typically: LMS engagement data, SIS academic records, CRM outreach history, and historical enrollment and withdrawal records. The more historical data you have, the more precise Cue's risk models become.
02 We Customize
We build student risk models.
Our data scientists build disengagement risk, withdrawal probability, and persistence signal models — trained on your institutional patterns, not industry averages.
03 Cue Analyzes, You Act
Surface risk before it becomes attrition.
Which students need outreach this week? Which programs have the highest withdrawal risk? Cue answers all of them — in plain language, in Slack or the web.
How it Works
How e:cue gets you to insight.
01 We Integrate
02 We Customize
03 Cue Analyzes, You Act
We connect your student lifecycle data.
Typically: LMS engagement data, SIS academic records, CRM outreach history, and historical enrollment and withdrawal records. The more historical data you have, the more precise Cue's risk models become.
Testimonials
How one leader gained clarity from their retention data.
e:cue has been transformational for our projections. We’re able to plan more effectively, optimize our resources, and confidently scale. It’s been a game-changer for how we approach growth.
James Dressing
Chief Executive Officer
Frequently Asked Questions
Clarity before you act.
We don't fully own our student data. Can you still help?
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It depends on what you have rights to and can access. We work with the data your organization controls — typically LMS engagement, enrollment funnel, and first-party campaign data. Many EdTech clients have built significant retention analytics capability from first-party data alone. In an ideal scenario, our ability to improve prediction on retention is often a catalyst for deeper partnership in data exchanges between partners.
What data does Cue need for student retention analytics?
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Typically: LMS engagement data (login activity, assignment completion), SIS academic records, CRM outreach history, unstructured notes, and historical enrollment and withdrawal records. The more historical data you have, the more precise Cue's risk models become.