Google Analytics leverages AI, ML, and Big Data to understand better how consumers are interacting with brands. Further, it makes use of behavior AI and performance AI algorithms to provide with intelligent recommendations, enabling continuous improvement.
Similarly, Dasceq leverages AI/ML techniques such as predictive behavior models, Attribution Models, Multivariate Models, Deep Learning, Random Forest, Survival Models, Consumer Personas, and Voice NLP Algorithms to determine who will pay, when they will pay, the amount they are most likely to pay and how to influence them to pay.
Our 2iTM models identify deep consumer patterns to provide best recommendations and understand their persona to be able to maximize influence across channels. These AI/ML models continuously learn to optimize profits.
With data ingestion API, the platform intakes data in formats of CSV, XML, and JSON for loan application, client, loan, voice, payment, and third party data,
The data fed into the platform can be structured/unstructured, trained/untrained, which is why it is redacted and cleansed through, for more usable data for the AI/ML models.
Leveraging models and process APIs for a multi-model integration, our 2iTM models analyze, sift, and score the provided data by developing, validating, and testing models.
Recommendations using AI/ML tools on contact, payment, and treatment strategy are sent across as suggested actions to B2B apps, dashboards, and management.