Increasing revenue and customer satisfaction by implementing behavioral scoring
Telecommunication industry use case
Prepaid vs. Postpaid
- Fast and easy approval process enabled by the fully automated scoring system.
- Higher customer satisfaction with the hassle-free onboarding
- Revenue increase as more customers moved to a postpaid plan
- Reduced operational cost for driving customers to opt for postpaid
And Solutions' Credit Scoring Solution
User profiling and demographic data collection through direct questionnaires to customers via multiple mediums.
Device information such as manufacturing date, price, phone calls, contact information, application data, etc., can serve as an additional and accurate data stream.
Third-party data sources
Along with the data from credit bureaus, banks, and other financial institutions, third-party data such as telephone data, utility bill data is also collected to have a better and more accurate look into the customers’ behavioral tendencies.
Collected data is sanitized and stored with the desired structure for efficient data operations.
Tagging the raw data with one or more contextual attributes for use in model training.
Feature selection and extraction
Identifying, manifesting, and ranking dataset features that make the most sense of data based on accuracy and sensitivity.
Our scoring solution uses multiple AI/ML algorithms such as Random Forest, Artificial Neural Network, KNN, etc., based on the business objectives, goals, and nature. It then creates an ensemble model consolidating results from the utilized models.
Self-learning and updating
The active learning model works continuously and is fed with new data sets for supervised/unsupervised learning to make the system more intelligent and accurate.
Our base gamification schema with a loyalty system helps businesses access and identify desirable customers by generating key features that are highly suitable for modeling.