Evaluating Prospect Acceptance Probability
Often it is important to evaluate how likely it is for a prospect to accept an offer for a product or service given the prospect's own characteristics (demographic, socio-economic, etc.) and the offer's features. For this purpose, it is necessary to build predictive models that evaluate the probability of accepting an offer as the features of this offer (price, product features) are varied.
Wide Range of Applications
Applicable to Various Situations
These models are applicable to a wide variety of situations: from evaluating college applicants, to crafting successful loyalty programs, and selling subscription services.
AI-Driven Models
A variety of techniques can be used to build these models. We leverage cutting-edge AI technologies to deliver the most accurate predictions.
Natural Language Processing (NLP)
Analyze text data and customer sentiment
Convolutional Neural Networks (CNN)
Deep learning for pattern recognition
Machine Learning Models
We employ a comprehensive suite of machine learning techniques to build robust predictive models with interpretable results.
Random Forest (RF)
Ensemble learning for high accuracy
CART Models
Classification and Regression Trees
XGBoost
Gradient boosting for optimal performance
Neural Networks (NN)
Deep learning architectures
Support Vector Machines (SVM)
Powerful classification algorithms
SHAP Analysis
Model interpretability and explainability
Case Study
Random Forest Predictive Model for Education Enrollment
The Challenge
Using Random Forests with 500 binary classification trees, we built a predictive model for a company engaged in education. The model estimated the probability of an applicant for a particular program offered by the company to enroll in the program.
The Solution
The outcome of this analysis was a continuously running scoring model that calculated a probability score for each new lead as this lead was entered into the CDP used by the company.
The scoring model was used by the company to segment the leads into different tiers depending on the likelihood of enrolling in the program. It increased significantly the efficiency and the productivity of the enrollment officers.
Model Performance
Ready to Leverage Predictive Analytics?
Let's discuss how our AI and machine learning models can optimize your marketing performance.
