Predicting the future is tricky business, especially when it comes to school enrolments. Yet for schools and Multi-Academy Trusts (MATs), having a clear picture of incoming student numbers isn't just helpful – it's essential.
Why? Because accurate enrolment forecasts touch nearly every aspect of a school's operations and long-term success. From budget planning and staffing to resource allocation and facilities management. They help schools avoid the pitfalls of overcrowded classrooms or empty seats, ensure appropriate teacher-to-student ratios, and guide long-term strategic planning. For MATs, these predictions can shape decisions about growth and resource distribution across multiple institutions.
In our previous blog we looked at utilising admissions data to boost enrolment rates; diving deep into the admissions funnel, various analytics techniques, and the challenges of managing enrolment data across multiple sources. If you missed it, use the link below to catch up!
Now, we're taking the next step: learning how to predict those enrolments effectively. In this blog, we will guide you through a step-by-step, robust methodology for building a predictive model, highlighting common pitfalls to avoid and sharing best practices to ensure accuracy.
Let’s begin!
Why build a predictive model to forecast student enrolments?
With educational institutions facing fiercer competition than ever before, the pressure to convert inquiries & leads into enrolled students has increased.
The main challenge arises with potential students reaching out through various channels - from social media to education fairs – this makes it difficult to determine which leads are most promising. Each inquiry represents a unique individual with their own background, preferences, and goals, also making it harder for institutions to know where to focus their efforts.
This often leads to a scattered approach, with schools trying to engage every lead equally, even those unlikely to enrol. It's not just inefficient; it's exhausting for admissions teams and can dilute the quality of interaction with truly interested candidates.
Predictive modelling gives schools a clearer picture of the drivers behind enrolments decisions, allowing for a more personalised and effective approach to recruitment. Rather than reducing applicants to mere numbers, these models help schools better understand and respond to individual needs and preferences. With these insights, schools can have more meaningful conversations with prospective students, focus their energy where it counts, and constantly improve how they connect with their future pupils. In the end, it's about creating a win-win situation: students find the right fit for their education, and schools build a diverse, engaged student body.
Common data challenges schools face with enrolment predictions
Before we dive into the methodology for building enrolment prediction models, it's crucial to address the foundation of any good forecast: the data itself. The quality and integrity of your data can make or break the accuracy of your predictions. Even the most sophisticated model won't yield reliable results if it's fed with flawed or incomplete information. With that in mind, let's explore 6 common data pitfalls that schools often encounter when trying to forecast enrolments, and how you can sidestep these issues:
1. Data quality & completeness:
One of the biggest hurdles schools faces is ensuring their data is accurate, complete, and current. When your dataset has gaps, outdated information, or plain errors, it's like trying to build a house on shaky ground. Your predictive model might end up making skewed predictions, leading you down the wrong path when it comes to enrolment strategies.
2. Data integration:
Schools often pull data from various sources - think student information systems, CRM tools, and even spreadsheets. Each of these might store information differently, using unique formats or structures. Trying to bring all this diverse data together can be like challenging. If not done carefully, you might lose crucial information or introduce errors that can throw off your entire prediction model.
3. Data inconsistencies:
It's rare to find a perfect dataset. You'll often encounter missing values, conflicting information, or incomplete entries, especially in personal data fields like gender or birth dates. This isn't just a minor inconvenience - it's a real challenge that requires significant data cleaning and pre-processing. Without this crucial step, your model might be working with faulty inputs, leading to unreliable predictions.
4. Outliers and imbalances:
Outliers and imbalanced data are more common than you might think. In enrolment data, it's common to find that only a small percentage of inquiries actually result in enrolments. This imbalance presents a real challenge for predictive modelling. When most of your data represents non-enrolments, your model might become really good at predicting who won't enrol but struggle to accurately identify the fewer cases that do lead to enrolment. On the other hand, ignoring these outliers and imbalances is like turning a blind eye to important nuances in your data, potentially leading to a model that misses the mark on accuracy.
5. Data volume & storage:
Managing vast amounts of historical data can overwhelm systems. Schools need robust infrastructure and efficient processing techniques to handle this information overload effectively, or risk missing out on valuable insights buried in the data.
6. Data bias:
Unchecked biases can lead to skewed predictions, potentially disadvantaging certain groups. Identifying and addressing these hidden biases is complex but essential for fair and accurate enrolment forecasting.
We've been in your shoes. We know these challenges aren't just bullet points – they're real obstacles that can make or break your enrolment strategy. If you're nodding along, recognising these hurdles in your own institution, let's talk. We're not here to sell you a one-size-fits-all solution. Instead, we want to understand your unique data landscape, your specific goals, and the challenges your schools is facing.
Step-by-step methodology to build a predictive model
Step 1: Data collection:
Gather a comprehensive dataset containing relevant attributes for enrolment prediction, including Visitor ID, Visitor Stage, Visit Date and Time, Enquiry Source, and Geo Location. This dataset forms the foundation for subsequent analysis, enabling the identification of patterns and trends that will inform the predictive model for enrolment. Ensure the data is accurate, complete, and representative of the target population to maximise the model's effectiveness.
Step 2: Pre-processing:
Prepare the dataset for analysis by addressing data quality issues and formatting. Begin by identifying and handling null values, missing data, outliers, and class imbalances. Encode categorical variables and normalise continuous numerical variables to ensure compatibility with machine learning algorithms. Apply appropriate imputation techniques for missing values where possible or remove incomplete records if necessary. Eliminate outliers to improve model performance, particularly for algorithms sensitive to extreme values. This pre-processing stage is crucial for creating a clean, consistent dataset that will yield more accurate and reliable predictive models.
Step 3: Exploratory Data Analysis (EDA):
Conduct a thorough examination of the dataset using statistical and visual techniques to uncover patterns, trends, and relationships among variables. This process involves generating summary statistics, creating visualisations such as histograms, scatter plots, and correlation matrices, and identifying key features that may influence enrolment. EDA helps deepen understanding of the data structure, guides hypothesis formation, reveals potential issues or opportunities in the dataset, and informs the selection of appropriate modelling techniques.
Step 4: Feature Selection:
The goal is to identify the most relevant attributes that significantly influence student enrolment predictions. Key features may include:
Enquiry source (e.g., website, social media, referral)
Campus visit timing and frequency
Demographic information (e.g., age, location, educational background)
Interaction history with the institution
By identifying the most influential predictors, we can focus on factors that truly impact enrolment decisions while minimising noise from less relevant variables. Also, not only does this improve the model’s performance but also enhances its interpretability, making it easier for stakeholders to understand and trust the results.
Before we dive into building the model, it's crucial to recognise that the foundation of any successful predictive analysis lies in thorough preparation and high-quality data. The steps we've discussed so far – from understanding your enrolment landscape to careful feature selection – are just the beginning of the journey.
Are you sure your data is primed for predictive success?
If you’re not a 100%, let's connect. Our team can conduct a thorough audit of your data ecosystem, providing you with actionable insights and a clear roadmap for building a robust enrolment prediction model.
Step 5: Building Model:
5.1 Data splitting:
Divide the pre-processed dataset into training and testing sets. The training set is used to build and tune the predictive model, while the testing set serves as a proxy for new, unseen data to evaluate the model's generalisation capabilities. This separation helps prevent overfitting, where a model performs well on training data but fails to generalise to new instances.
5.2 Machine Learning Algorithms:
Logical Regression:
Logistic regression is a powerful statistical model well-suited for predicting student enrolments. It excels in binary classification scenarios, where the outcome is either enrolment or non-enrolment. This model calculates the probability of a student enrolling by fitting historical enrolment data to a logistic function. This function transforms a combination of relevant factors—such as academic performance, financial aid offered, or campus visit attendance—into a probability between 0 and 1.
The formula for logistic regression in the context of enrolment prediction is:
P(enrolment) = 1 / (1 + e^-(β₀ + β₁x₁ + β₂x₂ + ... + βₙxₙ))
Where:
· P(enrolment) is the probability that a student will enrol.
· e is the base of the natural logarithm (approximately 2.718). ·
β₀ is the model's baseline enrolment rate. ·
β₁, β₂, ..., βₙ are coefficients corresponding to each predictive factor (e.g., test scores, financial aid amount, distance from home).
· x₁, x₂, ..., xₙ are the values of these factors for each student.
6. Model Evaluation:
Once the model is trained on logistic regression algorithms using sequential feature selection, the next step is to evaluate the model. The models will be evaluated by various evaluation metrics such as recall, precision, F1 score, accuracy etc. Hyperparameter optimisation should be conducted to ensure optimal accuracy.
· Recall: Recall measures the model’s ability to correctly identify all relevant positive cases.
· Precision: Precision indicates the proportion of correctly identified positive predictions out of all positive predictions made.
· Accuracy: Accuracy is the proportion of correctly predicted observations (both positive and negative) out of all observations.
· F1-Score: F1-score is the harmonic mean of precision and recall, providing a balance between the two metrics.
Now that we've built and evaluated the model, you might be wondering, "What's next?" The journey doesn't end with a set of performance metrics – it's about translating these insights into tangible improvements in your enrolment strategy.
This is where many institutions find themselves at a crossroads. You have a powerful predictive tool, but leveraging it effectively requires expertise in both data science and educational administration. That's where we come in.
Book a free discovery call with us to have a quick chat about we can help ensure your investment in predictive analytics translates into measurable improvements in your enrolment outcomes
An Ei Square solution to predicting student enrolments
An UK based school approached us to develop a predictive model to forecast their student enrolments. This model aimed to help the school optimise their recruitment strategies and resource allocation.
We conducted a thorough analysis of historical data, including visitor information, application details, and enrolment outcomes. Using this data, we built a predictive model to identify key factors influencing enrolment decisions.
Here are some key findings from the project and Ei Sqaure’s recommendations:
Enrolment Rate and Qualified Leads
Only 7% of visitors ultimately enrol. 72% of visitors do not proceed past the initial stage.
Recommendation: Focus on applications meeting qualified lead criteria rather than evaluating all applications.
Redefined enrolment rate as the number of enrolments divided by the number of qualified leads, rather than the total number of visitors.
Enrolment Rate= (Qualified Leads)/(Enrolled Students)
Timing of Visits
Weekday visitors show a higher likelihood of enrolment compared to weekend visitors.
Recommendation: Prioritise applications from weekday visitors.
Marketing Channel Effectiveness
Paid campaigns attract many visitors but have significantly lower enrolment rates compared to websites, referrals, and other online portals.
Recommendation: Shift focus from paid campaigns to offering better incentives, such as discounts and high-quality services, to attract and convert visitors more effectively.
Our machine learning model demonstrated remarkable effectiveness in predicting student enrolments, achieving an accuracy of 85% and a precision of 80%
If your school or MAT is looking to revamp its enrolment prediction and recruitment strategies, we're here to help. Our team of experts can develop a customised predictive model tailored to your school’s unique needs and data. Get in touch with us today to explore how we can enhance your enrolment process and drive better results for your institution.