Predictive analytics for everyone
Enrollment forecasting software predicts headcount, tuition revenue, and course demand from historical enrollment data. Clema builds enrollment forecasts, retention models, and graduation predictions for your IR team when you describe what you need in plain English. No data science background required.
Predict Fall 2027 enrollment by program, using the last five years of admissions data
Based on 5-year trends, I project 2,847 new enrollments (+3.2%). Programs with strongest growth:
Two programs show risk of decline. Confidence interval: ±4.1%
What you can predict
Describe the question. Clema builds the model and explains the answer.
Enrollment forecasting
“Project next fall’s enrollment by program using five years of admissions data.”
Model headcount, credit hours, or FTE by program, campus, or student type. See confidence intervals and the variables driving the forecast.
Retention risk
“Which current first-year students are most at risk of not returning?”
Identify at-risk students using the factors your institution actually tracks: GPA, credit completion, financial holds, engagement signals. Get a list, not just a number.
Graduation predictions
“What is our projected six-year graduation rate for the 2021 cohort?”
Forecast graduation rates by cohort, demographic, and program. Compare your projection to the IPEDS benchmark before the number is final.
Financial aid optimization
“What scholarship amount maximizes yield for out-of-state admits with a 3.5+ GPA?”
Model the relationship between aid packages and enrollment decisions. Find the point where additional aid stops moving the needle.
How it works
Describe your question
Type what you want to predict in plain English. Clema identifies the target variable, the features, and the timeframe.
Clema builds the model
The system selects an appropriate method, trains on your historical data, and validates the results. You see accuracy metrics explained in plain language, not just R-squared values.
Review and act
Get forecasts with confidence intervals, ranked risk factors, and plain-English interpretation. Export to your board deck or feed the results into your student success platform.
Why Clema predictive analytics
Built for IR analysts, not statisticians. If you can describe the question, you can build the model.
Plain English in, forecasts out
Describe what you want to predict. Clema picks the method, trains the model, and explains the output in words your provost can read.
No data science team required
Built for IR analysts, not statisticians. If you can describe the question, you can build the model. The statistics happen underneath.
Your data, your warehouse
Models train on your institutional data in your warehouse. Nothing leaves your environment. Peer comparisons use aggregated public data only.
Confidence intervals, not false precision
Every forecast comes with an honest range. Clema tells you how confident the model is and what would change the prediction.
Explainable results
Every prediction lists the top factors driving it. For retention risk: "GPA below 2.0 and fewer than 12 credits completed are the strongest signals." No black boxes.
Connects to your stack
Feed predictions into your student success platform, your CRM, or your reporting pipeline. Export as CSV, push via API, or view in Clema analytics.
Get started today
Try Clema predictive analytics free, or move to a paid plan as your team grows.
Free plan
Explore Clema predictive analytics with no commitment
No credit card required
- Free forever, no credit card
- 20 AI conversations a month
- Full access to Clema predictive analytics data
- Formatted, cited responses
Paid plans
For growing teams and whole institutions
Monthly or annual, no implementation fee
- Pro300 chats, 3 seats$99/mo
- Max1,000 chats, 10 seats$299/mo
- EnterpriseSSO, integrations, SLACustom
Frequently
asked questions
No. You describe what you want to predict in plain English. Clema selects the modeling approach, runs the analysis, and explains the results in plain language. If you want to see the technical details, they are available, but they are not required.
It depends on the question. For time series forecasting like enrollment trends, it uses methods like ARIMA and Prophet. For classification like retention risk, it uses gradient boosting and logistic regression. You do not need to choose; Clema selects based on the data and the question.
That depends on your data. Clema shows you accuracy metrics in plain language before you use any forecast. If the model is not confident enough, it tells you and explains what additional data would improve it.
Yes. Clema starts with what is available in your connected data sources. You can add, remove, or weight features by telling it what to include or exclude.
Yes. All modeling happens within your data environment. Student-level data never leaves your warehouse. Clema connects to your data; it does not copy it to a third-party cloud.
Forecasting your campus can understand
Plain English predictions. Honest confidence intervals. No data science degree required.