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Telecom

FutureAnalytica

AI Solutions for Telecom

Within the telecom industry data science applications are widely used to streamline the operations, to maximize profits, to build effective marketing and business strategies, to visualize data, to perform data transfer and for many other cases.

01

Retention, cross-selling and Upselling

Drive optimal strategies for customer retention and win back. Manage campaign output for cross-selling and upselling.

02

Customer Segmentation

By dividing a customer base into specific groups, marketers can use predictive analytics to make forward-looking decisions to tailor content to unique audiences.

03

Lifetime value prediction

Customer lifetime value is a discounted value of all the future profits and revenues generated by the customer. The CLV model is concentrated on customer purchasing behavior, activity, services utilized, and average customer value.

04

Network management and optimization

Network management and optimization gives an opportunity to define the score points in operations to identify the root causes of these complications. Looking into historical data and predicting possible future problems or, on the contrary, beneficial scenarios is a great benefit for the telecom providers.

05

Recomendation Engine

The recommendation engine is a set of smart algorithms depicting customers behavior and making a prediction about possible future needs of the product or service. Most popular approaches here are collaborative filtering and content-based filtering.

06

Attrition Management

Uncover issues that drive both dissatisfaction and churn With AI powered Preemptive client engagement. Firms can identify clients at high risk of attrition by learning from examples of clients that have closed or moved accounts in the past.

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