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Customer-Centric Predictive Modeling 

Since the early stages of customers acquisition and throughout the course of service delivery, data reveals much about the needs and aspirations from the relation between the customer & the service provider. Customers constantly voice out those needs and aspirations through signals and advanced customer analytics is among the most powerful enablers to companies for translating those signals into useful insights to guide customer relationship management. Machine learning have been at the center of these enablers and Cognitro has been working with banks, retailers and telecom operators to leverage AI and predictive analytics customers to build targeted campaigns to customer customers, maximize share of wallet, create new products to match customers needs as well as predict customers attrition and win-back strategy. 

Customer-Centric Machine Learning Models

Channel Mix Optimization
What are the best mix of channels to reach customers to either 1) offer specific products 2) address service issues and 3) discuss balance due and payments plans?.......
Customer Value Segmentation
How many groups sharply differentiates usage profiles and suggests what the true “services basket” in each group? and how to best describe and contextualize these groups ?
Cross-sell, Up-sell & Next-Best-Offer
How to best bundle products for the right customers? what products can best be offered based on purchasing history? and what's the buying propensity for each?
Customer Loyalty & Retention
How to measure and track customer loyalty? how to detect declining customer relation and potential churn? and how what measures can you implement to retain and win-back customers?

Relevant Experience

Case Study:Customer Behavioral Segmentation & Needs Analysis

See how we’ve helped an Asia-based telecom operator better understand their customers, needs and motivations and improve their customer strategy.



Customer call centers are usually the first line of interaction for companies with customers. But call centers can become inundated with calls from dissatisfied customers seeking better customer attention and/or service quality. If went unnoticed, dissatisfied customers can cost the company to loos many customers to competition and face the potential of declining market share. To alleviate this risk, Cognitro has developed TonAlyze.AI, an AI-driven tool that can detect up to 5 different emotions from human voices including anger, happy, disappointed, anxious and stressed. The tool can be deployed in-real time to monitor when calls on hold in order to prioritize the call. It can also scan recorded calls to tag calls based on the overall sentiments derived from the call.


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