The AIRA dashboard uses machine learning (ML) and artificial intelligence (AI) algorithms in the background. The AIRA dashboard models are named as per their functionality, for instance, if a model predicts when a customer is likely to make a transaction, it is titled ‘Transaction prediction’.
The propensity models of AIRA dashboard are described in the following table -
| Transaction prediction | The Transaction prediction predicts customers who all are likely to purchase in the future time period - 30 days. | The Transaction prediction model uses a binary classifier composing a primary ensemble of Light Gradient Boosting Machine (LightGBM), and Random Forest. |
| Customer Churn Prediction | The Customer Churn Prediction predicts customers who have a high chance of churning. | The Customer Churn Prediction model uses a binary classifier composing a primary ensemble of Light Gradient Boosting Machine (LightGBM), and Random Forest. |
| Campaign Response Prediction | The Campaign Response Prediction is very specific to a certain campaign period and given a campaign who would respond. This also takes the previous response rate of the person into account. | The Campaign Response Prediction model uses a binary classifier composing a primary ensemble of Light Gradient Boosting Machine (LightGBM), and Random Forest. |
| CLTV Prediction | The CLTV Prediction predicts the customer lifetime value for a year and then extrapolates for the next 3 years. | The CLTV Prediction model uses a combination of regression and bucketing based classification algorithms. |
| Product Affinity | The Product Affinity ranks the product in the order of choice of the customer. | The Product Affinity model uses the Proximity Model and FIR score Model. |