Student purchase prediction for European EdTech
European online education market. We built a purchase propensity model that
predicts buying intent and helps match students with the right trial lesson.
97%
Accuracy Rate
1
Dedicated Engineering Team
1 Month
To Completion
2
Data Sources Used
AI & Analytics
About The Company
Manual recommendations were time-consuming and inefficient. The opportunity was to use learning management system data and student profile data to build a machine learning model that could score purchase likelihood and support personalized recommendations.
The Challenge
Manual recommendations did not scale
Matching students with trial lessons manually took too much time and limited how fast the team could personalize offers.
Purchase intent was hard to predict
The team needed a clearer way to understand which students were most likely to buy after a trial lesson.
Student data needed structure
LMS behavior and profile data had to be prepared before it could be used for reliable purchase prediction.
Personalization had to drive conversion
The model needed to support better recommendations, stronger engagement, and higher conversion to paid subscriptions.
The Approach
Data Discovery
The project started with reviewing available LMS data, student profiles, trial lesson history, and conversion patterns.
- LMS behavior mapped
- Conversion patterns analyzed
- Model requirements defined
Data Preparation
The available student and lesson data was cleaned and structured into a dataset ready for machine learning.
- Student profile data cleaned
- Trial lesson records structured
- Training dataset prepared
Model Development
A machine learning model was built to estimate the likelihood of a student purchasing after a trial lesson.
- Student behavior features processed
- Scoring logic trained
- Model outputs tested
Recommendation Logic
The model scores were connected to recommendation logic so the school could prioritize students and personalize trial lesson offers.
- Trial lesson recommendations mapped
- High-propensity users identified
- Personalization logic prepared
Validation & Delivery
The final model was validated, documented, and prepared for use in student engagement and conversion workflows.
- Prediction quality reviewed
- Technical documentation prepared
- Final model delivered
The Results
97%
Purchase prediction accuracy achieved
1 m
From data scoping to validated delivery
1
Dedicated ML team from start to finish
2
Core data sources used for scoring logic
Still using manual signals to decide who is ready to buy?
Turn student behavior into conversion-ready predictions.
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