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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
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Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
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Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
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Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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8 Ways to Improve Accuracy of Machine Learning Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Science Roles in Telecom industry

Trupti
Trupti Last Updated : 16 Mar, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

Introduction

As a Data Scientist, I have worked with one of India’s largest telecom providers. Having worked closely with them, a wide range of infrastructure they need to connect with, infrastructure-related data, maintenance of those systems on regular basis for interrupted services, storing humongous customer data as per the government’s rules and regulations, and maintaining customer satisfaction at the same time is the goliath task that Telecom Operator goes through.

 

Big Data and Cloud Platform

In the early years, telecommunications data storage was hampered by a variety of problems such as unwieldy numbers, a lack of computing power, prohibitive costs. But with the new technologies, the dimension of problems has changed.

The areas of use of Technology are:

· Cloud Platform enabling Data storage expenses to drop every day. (Azure, AWS)

· Computer processing power is increasing exponentially (Quantum Computing)

· Analytics software and tools are cheap and sometimes free (Knime, Python)

In earlier days, the data stores were expensive, and data was stored in siloed – separated and often incompatible – data stores. This was creating barriers to make use of an enormous volume and variety of information. Business Intelligence (BI) vendors like IBM, Oracle, SAS, Tibco, and QlikTech are breaking down these walls between data storage and this provides a lot of jobs for telecom data scientists.

 

Data Scientist roles in Telecom Sector

1. Network Optimization

 

Network Optimization data scientist telecom

When a network is down, underutilized, overtaxed, or nearing maximum capacity, the costs add up

  • In the past, telecom companies have handled this problem by putting caption data and developing tiered pricing models.
  • But now, using real-time and predictive analytics, companies analyze subscriber behavior and create individual network usage policies.
  • When the network goes down, every department (sales, marketing, customer service) can observe the effects, locate the customers affected, andimmediately implement efforts to address the issue.
  • When a customer suddenly abandons a shopping cart, customer service representatives can soothe concerns in a subsequent call, text, oremail.
  • The advanced AI/ML techniques can alarm the network operator in the nearby time of failure of the tower by observing the tower parameters using itspast failures.
  • Building360-degree profile of Network using CDRs, Alarms, Network Manuals, TemIP, etc. gives a better overview of the network health.

Not only does this make happy customers, but it also improves efficiencies and maximizes revenue streams.

Telecoms also have the option to combine their knowledge of network performance with internal data (e.g., customer usage or marketing initiatives) and external data (e.g., seasonal trends) to redirect resources (e.g., offers or capital investments) towards network hotspots.

 

2. Customer Personalization

Customer Personalization

Like all the industries, Telecom has much more scope to personalize the services such as value-added services, data packs, apps to recommend based on following the behavioral patterns of customers. Sophisticated 360-degree profiles of customers assembled from all below help to build personalized recommendations for customers.

Customer Behaviour

  • voice, SMS, and data usage patterns
  • video choices
  • customer care history
  • social media activity
  • past purchase patterns
  • website visits, duration, browsing, and search patterns.

Customer Demographics

  • age, address, and gender
  • type and number of devices used.
  • service usage
  • geographic location

This allows telecom companies to offer personalized services or products at every step of the purchasing process. Businesses can tailor messages to appear on the right channels (e.g., mobile, web, call center, in-store), in the right areas, and in the right words and images.

Customer Segmentation, Sentiment analysis, Recommendation engines for more apt products for the customers are the illustrative areas where Data scientists can help for improvements.

 

3. Customer Retention

 

Customer Retention data scientist telecom

Due to customer dissatisfaction in any of the areas such as poor connection/network quality, poor services, high cost of services, call drops, competitors, less personalization, customer churn. This means they jump from network to network in search of bargains. This is one of the biggest challenges confronting a telecom company. It is far more costly to acquire new customers than to cater to existing ones.

To prevent churn, data scientists are employing both real-time and predictive analytics to:

  • Combinevariables (e.g., calls made, minutes used, number of texts sent, average bill amount, the average return per user i.e.ARPU) to predict the likelihoodof change.
  • Know when a customer visits a competitor’s website changes his/her SIM or swaps devices.
  • Use sentiment analysis of social media to detect changes in opinion.
  • Target specific customer segments with personalized promotions based on historical behavior.
  • React to retains customers as soon as the change is noted.

Predictive models, clustering would be the ways to predict the prospective churners.

 

Implemented Solution Approach

Using big data and python, I have developed the solution to find the upcoming network failure before it takes place. The critical success factor defined were:

· Identify and prioritize the cells with call drop issues based on rules provided by the operator.

· Based on rules specified, provide relevant indicative information to network engineers that might have caused the issue in the particular cell.

· Provide a 360-degree view of network KPIs to the network engineer.

· Build a knowledge management database that can capture the actions taken to resolve the problem and

· Update the CRs as good and bad, based on effectiveness in resolving the network issue

As a huge data was getting created, the database used was Hadoop -Big Insights.

Data transformation scripts were in spark.

And the neural network was the ML technique used to find out the system parameters when historically alarms (the indication of network failure) in the system got generated.

This information was fed as a threshold and once in the real scenario the parameters start approaching the threshold, the internal alert for those cell sites get generated for the Network engineer to focus on as preventive analytics.

Once the network engineer identifies the problem and solves it, it gets documented in the knowledge repository for future reference.

And when exactly a similar situation occurs, network the engineer will not get notification of internal alert but also steps to solve which is build using knowledge repository.

 

Conclusion

The reduction in process time, dropped call rate, the volume of (transient) issues handled by engineer, mean time to solve the problem, cost, people and increase in Revenue, customers, customer satisfaction, efficiency, and productivity of network engineers are the main area of any industry which Data scientists would be of help.

Various data generation sources under Telecom sectors are booming areas for Data Scientists to innovate, explore, value add, and help the provider to provide data-driven AI/ML solutions by preventive analytics, process improvements, optimizations, predictive analytics.

 

blogathondata scientist telecom
Trupti
Trupti

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