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Telcommunication Applications

Telecommunications and Artificial Intelligence

In the age of information explosion, big data has brought challenges. Faced with the continuous and intense competition from OTT service providers, traditional telecommunications service providers have been forced to undergo enterprise transformation.
Armed with big data, Sparkflows provides relief to the Telecommunication sector by empowering it  with plans and management to understand better customer behavior and increase profit.

Customer Experience Management(Customer 360)

      Decrease Customer service calls

      Decrease in support calls

 

      Increase customer retention and satisfaction

 

      Actively engage influencers

 

      Identify opportunities and build brand loyalty

Network Optimization
and Analytics

      Supports better capacity planning and              traffic management

 

      Effective service assurance to

      deliver a better customer experience. 

         

      A customer experience that retains                    subscribers and increases

      revenue

 

Recovery of payments

Identify customers most likely to have difficulty in payment

 

Take remedial steps for payment recovery

 

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AI Use Cases for Telecommunication
Self-service Data Science and Analytics for Enterprise

The new challenges faced by the Telecom Industry are huge volumes of predictive models needed and the briskness with which they ought to be updated. The competitive production in this sector has forced them to improve efficiency without any compromise on accuracy.

Sparflows Use Cases in Telecom

    

           Fraud Detection

           Predictive Analysis

           Customer Segmentation

           Customer Churn Prevention

           Lifetime Value Prediction

           Product Development

           Recommendation Engines

           Customer Sentiment Analysis

           Real time Analytics

            Price Optimization

              

Customer Segmentation

Telecom customers having similar traits are grouped together in multiple segments to facilitate in-depth analysis of their behaviour. Customer Segments are further used to design targeted marketing strategies based on customer value drivers. Four segmentation schemes can be considered: Customer Value Segmentation, Customer Behaviour Segmentation, Customer Life cycle Segmentation and Customer Migration Segmentation.​

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Read datasets related to Services used by a customer, customer income group, customer’s geographical location, call duration & number of calls

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Perform pre-processing and data cleaning

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Perform classification of customers using Machine Learning algorithms to create multiple segments

Fine tune the model and identify segment based preferences & value drivers. Use them for targeted marketing

Recommendations

Telecom companies tend to improve their customer lifetime value by recommending new products to customers. Two important strategies for recommendation are Upsell and Cross-sell. Upsell is a strategy to sell more expensive plan or service than what customer already has. Cross-sell is a strategy to sell a plan/service that other similar customers are using. Sparkflows enhances recommendation process by facilitating creation of customer segments and implementing machine learning algorithms to identify Upsell and Cross-sell opportunities. ​

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Read datasets related to customer details, services booked by a customer, location, income group, service usage data.​

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Perform pre-processing and data cleaning​

Perform classification of customer using Machine Learning algorithms to create multiple segments​

Implement Machine Learning algorithms such as Frequent Pattern Group to generate recommendation as per their segment​

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Offer recommended plan and services to customer.

Sentiment Analysis

To know feedback on new products, to know customer satisfaction and to assuage any negativity, Telecom companies processes posts on social media and various websites to get the pulse of customer sentiments. Sparkflows can help to process datasets on social media and websites and derive an insight into the satisfaction level of services offered. This information can be used to take preventive steps to avoid churn and to provide better services.​

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Read datasets related to posts on social media, websites, locations,
customer details

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Perform pre-processing and data cleaning​

Use Machine Learning algorithms to identify whether a post is positive or negative​

Take corrective action to improve services and to avoid churn

Customer Churn Prevention

Telecom companies always thrive to attract new customers and at same time try to avoid churn to maximize profit. Customer would churn due to numerous reason; some of the prominent ones are better plan offered by competition, change in demographic location, below par services offered by service provider and so on. Sparkflows can help to identify such customers using machine learning algorithm so that preventive steps can be taken to avoid it.​

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Read datasets such as services booked by a customer, customer details, location info and others

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Implement supervised machine
learning algorithms to identify
customers who are likely to churn​

Fine tune models ​

Take corrective action to improve services and to avoid churn