The IntelliML module allows for building and deploying Bots. It provides support for strong workflow and integration capabilities which allow the deployment of production ready bots. Deploying bots that help in automating tasks that are mission-critical and cannot be left to human errors, or tasks that rely on individual judgement that induces errors because of the emotional state of an individual or tasks that are repetitive and time consuming that takes thousands of man-hours and yet do not yield results. These are just some of the cases fit for RPA using Bots deployed on our platform.

Churn Management Predictor helps to predict if a customer is potentially going to drop out. The model will predict the likelihood of customers dropping out. The model can be run periodically on the entire customer dataset to produce a list of potential dropouts. This list can be used for various remedial actions to assist with customer retention. The Conversion Model (described above) can be used to retain a customer by predicting suitable product alternatives.

Conversion Predictor helps by suggesting the best product or service alternative to a customer based on Demographic Segmentation, behavourial profile, usage profile, other financial data available and financial needs analysis.

This Bot identifies the best time to call a potential customer based on identified parameters, it works with the campaign manager to create an outbound call list that enhances the chance of having an effective communication with the potential customer that will lead to a sale or efficient service call.

New Sale Prediction Model allows for prediction of “best fit” products or services for a particular customer based on their fitment in a specific demographic segmentation. The customer segmentation is based on unsupervised learning model. It leverages various behavourial attribute-relevant predictions that then drive the sales process.

This Bot helps in tracking and predicting a successful lead-to-cash-cycle. Based on various customer interactions, request and response time/frequencies or artifact exchanges, the bot will help in the continuous monitoring of leads to an effective sale.

This model uses Natural Language Processing (NLP) and Machine Learning Algorithms to categorize emails that can be used to create leads, service requests or tickets etc. The model relies on historical data and emails to identify key issues raised in an email and further classify the email to a particular service request category and sub-category. This model helps in improving operational efficiency by reducing human interventions.

This model is based on NLP and ML algorithms and helps in identifying critical issues highlighted in emails. This helps in identifying potential “time bombs” and addresses such issues without letting them blow up, thus avoiding significant cost and reputational damages.