This document discusses various computational intelligence methods for predicting customer churn in telecommunication companies. It begins by introducing the problem of high customer churn rates in a competitive telecom market. It then discusses approaches like basic classifiers, data preprocessing techniques, and ensembles of classifiers. The document evaluates several specific techniques - multilayer perceptrons, genetic programming, self-organizing maps, and negative correlation learning. It concludes by discussing future work areas and published research applying these methods to improve churn prediction.
The importance of this type of research in the telecom market is to help companies make more profit.
It has become known that predicting churn is one of the most important sources of income to Telecom companies.
Hence, this research aimed to build a system that predicts the churn of customers i telecom company.
These prediction models need to achieve high AUC values. To test and train the model, the sample data is divided into 70% for training and 30% for testing.
Slides from the presentation of this NYC meetup : http://www.meetup.com/Data-Modeling/events/224554990/
I talked about how to model churn before even thinking about the machine learning model.
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
Customer churn prediction for telecom data set.Kuldeep Mahani
Customer churn prediction and relevant recommendations as per DSN telecom data analysis. Random forest and logistic regression were applied to predict customer churn.
The importance of this type of research in the telecom market is to help companies make more profit.
It has become known that predicting churn is one of the most important sources of income to Telecom companies.
Hence, this research aimed to build a system that predicts the churn of customers i telecom company.
These prediction models need to achieve high AUC values. To test and train the model, the sample data is divided into 70% for training and 30% for testing.
Slides from the presentation of this NYC meetup : http://www.meetup.com/Data-Modeling/events/224554990/
I talked about how to model churn before even thinking about the machine learning model.
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
Customer churn prediction for telecom data set.Kuldeep Mahani
Customer churn prediction and relevant recommendations as per DSN telecom data analysis. Random forest and logistic regression were applied to predict customer churn.
Telecommunication Analysis (3 use-cases) with IBM watson analyticssheetal sharma
The purpose of this study is, with the help of Watson Analytics examine why customers are not used the connection of Bits Telecom Company, which factors are influence the churn. Also see the cross selling and up-selling, also focus on profitability and investment and find out the way for better results.
I have done this analysis using SAS on a dataset with 5000 records. I have used CART and Logistic regression to build a predictive model to identify customers which are likely to shift to competitors network.
BigData Republic teamed up with VodafoneZiggo and hosted an meetup on churn prediction.
Telecom companies like VodafoneZiggo have long benefited from the fine art/science of predicting churn. Currently, in the booming age of subscription based business models (e.g. Netflix, Spotify, HelloFresh), the importance of predicting churn has become widespread. During this event, VodafoneZiggo shared some of its wisdom with the public, after which BDR Data Scientist Tom de Ruijter presented an overview of the modeling tools at hand, both classical, as well as novel approaches. Finally, the participants engaged in a hands-on session showcasing the implementation of different approaches.
PART 1 — Churn Prediction in Practice by Florian Maas
At VodafoneZiggo we are incredibly excited about Advanced Analytics and the enormous potential for progress and innovation. In our state of the art open source platform we store the tremendous amount of data that is generated every single second in our mobile and fixed networks. This means that we have a vast body of rich information, which if unlocked, can lead to something very special. As a company with a primarily subscription-based service model, churn plays a vital role in the daily business. Not only is the churn rate a good indicator of customer (dis)satisfaction, it is also one out of two factors that determines the steady-state level of active customers. During this talk, we will show how data science provides added value in the process of churn prevention at VodafoneZiggo. We will talk about the data and the modeling approach we use, and the pitfalls and shortcomings that we have encountered while building the model. We will also briefly discuss potential improvements to the current approach, which brings us to talk #2.
PART 2 — The Churn Prediction Toolbox by Tom de Ruijter
The second talk will show you the fine intricacies of predicting churn through different approaches. We’ll start off with an overview of different modeling strategies for describing the problem of churn, both in terms of a classification problem as well as a regression problem. Secondly, Tom will give you insights in how you evaluate a churn model in a way such that business stakeholders know how to act upon the model results. Finally, we’ll work towards the hands-on session demonstrating different model approaches for churn prediction, ranging from classical time series prediction to recurrent neural networks.
Customer churn occurs when customers or subscribers stop doing business with a company or service.
Also known as customer attrition, customer churn is a critical metric because it is much less expensive to retain existing customers than it is to acquire new customers – earning business from new customer’s means working leads all the way through the sales funnel, utilizing your marketing and sales resources throughout the process.
Introducing Customer Churn Prevention Powerpoint Presentation Slides. Discuss various ways through which a company can manage customer churn with this PPT slide deck. Showcase methods and ways by which a company can prevent the customer from reducing their purchase of products and services. Our readily available PPT slide deck helps to present the types of customer churn, methods to handle customer attrition, the impact of successful implementation of churn management, dashboard, churn propensity model, etc. Take the assistance of customer churn management PPT slideshow to depict several ways by which a firm can experience customer churn such as when customers stop spending, churn due to product quality, etc. Showcase four stages of customer churn management which allow the company to handle customer attrition. Present how the firm can prevent customer churn by using customer churn analysis PPT infographics. You can easily highlight information about the various marketing campaigns in order to retain its customer from churning. Provide ways to prevent churn through predictive analysis by incorporating our professionally designed customer churn prediction PPT presentation. https://bit.ly/3p6AR7S
Churn in the Telecommunications Industryskewdlogix
Strategic Business Analysis Capstone Project Telecommunications Churn Management
Churn is a significant problem that costs telecommunications companies billions of dollars through lost revenue. Now that the market is more mature, the only way for a company to grow is to take their competitors customers. This issue
combined with the greater choice that consumers have gained means that any adverse touch point with a consumer can result in a lost customer.
Customer churn has become a big issue in many banks because it costs a lot more to acquire a new customer than retaining existing ones. With the use of a customer churn prediction model possible churners in a bank can be identified, and as a result the bank can take some action to prevent them from leaving. In order to set up such a model in a bank in Iceland few things have to be considered. How a churner in a bank is defined, and which variables and methods to use. We propose that a churner for that Icelandic bank should be defined as a customer who has not been active for the last three months based on the bank definition of an active customer. Behavioral and demographic variables should be used as an input for the model, and either decision tree or logistic regression used as a technique.
Telecommunication Analysis (3 use-cases) with IBM watson analyticssheetal sharma
The purpose of this study is, with the help of Watson Analytics examine why customers are not used the connection of Bits Telecom Company, which factors are influence the churn. Also see the cross selling and up-selling, also focus on profitability and investment and find out the way for better results.
I have done this analysis using SAS on a dataset with 5000 records. I have used CART and Logistic regression to build a predictive model to identify customers which are likely to shift to competitors network.
BigData Republic teamed up with VodafoneZiggo and hosted an meetup on churn prediction.
Telecom companies like VodafoneZiggo have long benefited from the fine art/science of predicting churn. Currently, in the booming age of subscription based business models (e.g. Netflix, Spotify, HelloFresh), the importance of predicting churn has become widespread. During this event, VodafoneZiggo shared some of its wisdom with the public, after which BDR Data Scientist Tom de Ruijter presented an overview of the modeling tools at hand, both classical, as well as novel approaches. Finally, the participants engaged in a hands-on session showcasing the implementation of different approaches.
PART 1 — Churn Prediction in Practice by Florian Maas
At VodafoneZiggo we are incredibly excited about Advanced Analytics and the enormous potential for progress and innovation. In our state of the art open source platform we store the tremendous amount of data that is generated every single second in our mobile and fixed networks. This means that we have a vast body of rich information, which if unlocked, can lead to something very special. As a company with a primarily subscription-based service model, churn plays a vital role in the daily business. Not only is the churn rate a good indicator of customer (dis)satisfaction, it is also one out of two factors that determines the steady-state level of active customers. During this talk, we will show how data science provides added value in the process of churn prevention at VodafoneZiggo. We will talk about the data and the modeling approach we use, and the pitfalls and shortcomings that we have encountered while building the model. We will also briefly discuss potential improvements to the current approach, which brings us to talk #2.
PART 2 — The Churn Prediction Toolbox by Tom de Ruijter
The second talk will show you the fine intricacies of predicting churn through different approaches. We’ll start off with an overview of different modeling strategies for describing the problem of churn, both in terms of a classification problem as well as a regression problem. Secondly, Tom will give you insights in how you evaluate a churn model in a way such that business stakeholders know how to act upon the model results. Finally, we’ll work towards the hands-on session demonstrating different model approaches for churn prediction, ranging from classical time series prediction to recurrent neural networks.
Customer churn occurs when customers or subscribers stop doing business with a company or service.
Also known as customer attrition, customer churn is a critical metric because it is much less expensive to retain existing customers than it is to acquire new customers – earning business from new customer’s means working leads all the way through the sales funnel, utilizing your marketing and sales resources throughout the process.
Introducing Customer Churn Prevention Powerpoint Presentation Slides. Discuss various ways through which a company can manage customer churn with this PPT slide deck. Showcase methods and ways by which a company can prevent the customer from reducing their purchase of products and services. Our readily available PPT slide deck helps to present the types of customer churn, methods to handle customer attrition, the impact of successful implementation of churn management, dashboard, churn propensity model, etc. Take the assistance of customer churn management PPT slideshow to depict several ways by which a firm can experience customer churn such as when customers stop spending, churn due to product quality, etc. Showcase four stages of customer churn management which allow the company to handle customer attrition. Present how the firm can prevent customer churn by using customer churn analysis PPT infographics. You can easily highlight information about the various marketing campaigns in order to retain its customer from churning. Provide ways to prevent churn through predictive analysis by incorporating our professionally designed customer churn prediction PPT presentation. https://bit.ly/3p6AR7S
Churn in the Telecommunications Industryskewdlogix
Strategic Business Analysis Capstone Project Telecommunications Churn Management
Churn is a significant problem that costs telecommunications companies billions of dollars through lost revenue. Now that the market is more mature, the only way for a company to grow is to take their competitors customers. This issue
combined with the greater choice that consumers have gained means that any adverse touch point with a consumer can result in a lost customer.
Customer churn has become a big issue in many banks because it costs a lot more to acquire a new customer than retaining existing ones. With the use of a customer churn prediction model possible churners in a bank can be identified, and as a result the bank can take some action to prevent them from leaving. In order to set up such a model in a bank in Iceland few things have to be considered. How a churner in a bank is defined, and which variables and methods to use. We propose that a churner for that Icelandic bank should be defined as a customer who has not been active for the last three months based on the bank definition of an active customer. Behavioral and demographic variables should be used as an input for the model, and either decision tree or logistic regression used as a technique.
You did it: you’ve got some customers, you’ve pitched, you’ve networked, you’ve grown… but now what?
There’s a whole lot to understand between pitching to an Angel Group and the final handshake. We’ll walk you through the post pitching inquiry process, due diligence rounds and how they differ from group to group: from high level overviews, to in-depth analyses of every number, customer and flaw, our Angel experts will let you in on what they expect, hope for and get turned off by so you can be prepared.www.thecapitalnetwork.org
Morgan Lewis TCN presentation - seed and venture financing in three actsThe Capital Network
Term sheets got you tearing your hair out?
Come to Understanding Angel & Venture Term Sheets: A Play In 3 Acts where you will experience an in-depth discussion hosted by some of Boston’s most experienced attorneys. Through re-enactment of negotiations, you’ll understand how all parties: entrepreneurs, engineers and investors understand this process from their own perspectives.
High growth investments are complex: but rest assured this workshop will cut through it all to help you understand what it means for you.
Breaking the data barrier: Lessons from analytically advanced Finance organiz...Spencer Lin
The majority of enterprises recognize that data and analytics are transforming their businesses. But what about the Chief Financial Officer (CFO) and Finance? How are data and analytics changing their professions? To find out, the IBM Center for Applied Insights asked over 1,000 organizations across five industries about their Finance departments’ approach to data and analytics, cloud and engagement. Findings show that enterprises with analytically advanced Finance organizations reported better outcomes when it came to decision making, growth and agility.
EFQM European Foundation Of Quality Management - Radar ModelShashank Varun
The RADAR logic is a dynamic assessment framework and powerful management tool that provides a structured approach to questioning the performance of an organisation. At the highest level, RADAR logic states that an organisation needs to: Determine the Results it is aiming to achieve as part of its strategy.
Customer churn classification using machine learning techniquesSindhujanDhayalan
Advanced data mining project on classifying customer churn by
using machine learning algorithms such as random forest,
C5.0, Decision tree, KNN, ANN, and SVM. CRISP-DM approach was followed for developing the project. Accuracy rate, Error rate, Precision, Recall, F1 and ROC curve was generated using R programming and the efficient model was found comparing these values.
Data Mining on Customer Churn ClassificationKaushik Rajan
Implemented multiple classifiers to classify if a customer will leave or stay with the company based on multiple independent variables.
Tools used:
> RStudio for Exploratory data analysis, Data Pre-processing and building the models
> Tableau and RStudio for Visualization
> LATEX for documentation
Machine learning models used:
> Random Forest
> C5.0
> Decision tree
> Neural Network
> K-Nearest Neighbour
> Naive Bayes
> Support Vector Machine
Methodology: CRISP-DM
Pinnacle digital advisors -How U.S.Telecoms Can More Effectively Convert Data...sangeetk072
Pinnacle Digital Products ,Pinnacle digital advisors,,Pinnacle digital is the leading provider of next generation network and customer analytics solutions
http://pinnacledigital.in/index.html
INTEGRATION OF MACHINE LEARNING TECHNIQUES TO EVALUATE DYNAMIC CUSTOMER SEGME...IJDKP
The telecommunications industry is highly competitive, which means that the mobile providers need a
business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal
level of cost in marketing activities. Machine learning applications can be used to provide guidance on
marketing strategies. Furthermore, data mining techniques can be used in the process of customer
segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive
Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling
according to their billing and socio-demographic aspects. Results have been experimentally implemented.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
CHURN ANALYSIS AND PLAN RECOMMENDATION FOR TELECOM OPERATORSJournal For Research
With increasing number of mobile operators, user is entitled with unlimited freedom to switch from one mobile operator to another if he is not satisfied with service or pricing. This trend is not good for operators as they lose their revenue because of customer switch. To solve it, operators are looking for machine learning tools which can predict well in advance which customer may churn, so that they can predict any alternative plans to satisfy and retain them. In this paper, we design a hybrid machine learning classifier to predict if the customer will churn based on the CDR parameters and we also propose a rule engine to suggest best plans.
Predicting churn with filter-based techniques and deep learningIJECEIAES
Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.
Previous studies have predicted customer churn in the mobile indutry especially the postpaid customer
segment of the market. However, only few studies have been published on the prepaid segment that could
be used and operationalised within the marketing team that are responsible for the management of incident
of prepaid churn. This is the first identifiable literature where customer dormancy is predicted along the
customer value segmentation. In this article, we use a popular data mining technique to predict when a
customer will go dormant or stop performing revenue generating events in a prepaid predominant market.
Our study is unique as we considered ~1,451 attributes derived from CDR and SIM registration database
(previous studies only considered maximum of ~1,381 potential variables). We built 3 different models for
Very High, High and Low value segments. We applied our models on the prepaid base and the output was
later compared with the actual dormant customers. Very High segment has the highest accuracy and lift
while Low segment has the least at the same threshold. We show that once the problem of prepaid churn is
well defined, it can be predicted. We recommend a value segmentation dormancy prediction with decision
tree for prepaid segment with a certain threshold. Our study shows that this approach can be easily
adopted and operationalised by the campaign management team responsible for the management of
prepaid churn in a mobile industry.
An efficient enhanced k-means clustering algorithm for best offer prediction...IJECEIAES
Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm. Our results showed that our proposed clustering model accuracy was over 90%, while the traditional k-means accuracy was under 70%.
Customer segmentation for a mobile telecommunications company based on servic...Shohin Aheleroff
Competition between the mobile operators is becoming more based on subscriber’s behavior. In order to improve mobile operator’s competitiveness and customer value, several data mining technologies can be used.Most telecommunications carriers cluster their mobile customers by billing system data. This paper discusses how to cluster mobile customers based on their call detail records and analyze their consumer behaviors.
Varsha Shanbhag - Data Science in Telecom.pptxVarshaShanbhag2
In the realm of global telecommunications, the conventional sounds of dial tones have yielded to a data-driven revolution. Data science has become pivotal, fundamentally reshaping the operational landscape for telecom giants worldwide. From Tokyo's adoption of AI-driven churn prevention to Chile's pioneering predictive maintenance endeavours, data now serves as the driving force propelling the industry toward unprecedented levels of personalization and efficiency.
In this transformative era, algorithms have displaced copper as the cornerstone for securing customer loyalty, ensuring network resilience, and establishing market dominance!
This presentation explores the dynamic landscape of data science applications in the telecom sector, showcasing its pivotal role in navigating the complexities of our digitally driven present world.
[Big] Data For Marketers: Targeting the Right MarketPanji Winata
big data analytics for marketers: customer 360 degree profile, customer segmentation using RFM, Smartphone Adoption Prediction for Telecommunication Service, Event Trigger for Preventing Customer Churn in Telecommunication Service
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Innovative Methods in Media and Communication Research by Sebastian Kubitschk...
Churn prediction
1. Computational IntelligenceComputational Intelligence
methods for churn predictionmethods for churn prediction
in telecommunicationin telecommunication
companiescompanies
Hossam Faris, PhD
Associate Professor
Business Information Technology Department
King Abdullah II School for Information Technology
The University of Jordan
hossam.faris@ju.edu.jo
7ossam@gmail.com
2. IntroductionIntroduction
The market is very dynamic and highly
competitive.
It is very easy for customers to switch
from one service provider to another for
a better price rates or service quality.
Telecommunication companies suffer a
loss of 20-40% of their customers every
year!
3. IntroductionIntroduction
• Companies are aware that attracting new
customers is much more costly than
keeping current customers.
• Companies in the telecommunication
market realize that customers are the
most important asset for them.
4. What isWhat is customer churncustomer churn ??
In business, “customer churn” is a term
commonly refers to customers who stop
using some services or terminate their
contract and subscription with a company
to switch to another competitor.
Customer churn has many reasons and
factors. Such reasons include quality and
cost of services.
5. Churn management and predictionChurn management and prediction
The goal of churn management is to keep
current customers as long as the company is
alive in the market.
Revenue comes from the creation and
maintaining long-term relationships with the
customers.
A better churn management can help
Customer Relationship Management (CRM) in
decision making and establishing effective
customer retention campaigns.
6. The targetThe target
• We need to identify (predict) those
customers who are probably will leave.
• Specific marketing campaigns could be
designed to target the most risky
customer segments.
• Special discounts and subscriptions could
be offered.
7. From where to start ?From where to start ?
Detecting a churn by observation is almost
impossible.
Traditional surveys based on running
questionnaires or interviews suffer from a
high cost, limited access to customer
population and data self-reporting
Telecom companies realize that their
existing customer database is the key.
Service providers started to invest more in
data mining techniques that can aid in having
an efficient churn prediction models
9. Customer related featuresCustomer related features
Feature name Description
3G The subscriber is provided with 3G service (Yes, No)
Total Consumption (con) Total monthly fees (calling +SMS) in (JD)
Calling fees Total monthly calling fees (JD)
Local SMS fees Monthly local SMS fees(JD)
Int. calling fees Monthly fees for international calling (JD)
Local SMS count Number of monthly local SMS
Int. SMS count Number of monthly international SMS
Int. MOU Total of international outgoing calls in minutes
Total MOU Total minutes of use for all outgoing calls
On net MOU Minutes of use for on-net-outgoing calls
Churn Churning customer status (Yes, No)
10. Research linesResearch lines
The state-of-art basic classifiers approaches:
create or modify the algorithms that exist for
churn prediction.
Data level approaches: add a preprocessing step
where the data distribution is rebalanced in order
to decrease the effect of the skewed class
distribution in the learning process.
Ensembles of classifiers each ensemble is a group
of classifiers trained independently then all their
predictions are combines. Ensemble classifier
proofed to have better generalization and
outperform single classifiers.
16. Identifying important variables inIdentifying important variables in
MLPMLP
During the evolutionary cycle of GP,
input features that help GP in improving
the fitness value of the generated
individuals will survive while the weak the
features will be excluded and disappear
from the remaining generations.
18. 2.Data level approaches2.Data level approaches
This approach is performed on two stages:
Cleaning the data : A clustering method is
used to identify different behavior patterns
of customers. Small and unrepresentative
data are treated as outliers and noise. So
they are eliminated.
Modeling: A classification technique is
applied to develop the final prediction
model.
23. 3.Ensembles of classifiers3.Ensembles of classifiers
• NCL is an ensemble
learning technique that
encourages diversity
explicitly among
ensemble members
through their negative
correlation
• Negative correlation
Learning based on MLP
networks
25. Future workFuture work
Investigating the application of cost-
sensitive methods in churn prediction.
It is very interesting to study the most
influencing factors that affect customer
churn in different regions.
26. Published researchPublished research
• Faris, Hossam, Bashar Al-Shboul, and Nazeeh Ghatasheh. "A
genetic programming based framework for churn prediction in
telecommunication industry." Computational Collective Intelligence.
Technologies and Applications. Springer International Publishing,
(2014).
• Rodan, Ali, Faris, Hossam and others. "A support vector machine
approach for churn prediction in telecom industry." International
Information Institute (Tokyo). Information17.8 (2014): 3961.
• Faris, Hossam. "Neighborhood cleaning rules and particle swarm
optimization for predicting customer churn behavior in telecom
industry."International Journal of Advanced Science and Technology 68
(2014): 11-22.
• Rodan, A., Fayyoumi, A., Faris, H., Alsakran, J., & Al-Kadi, O.
“Negative Correlation Learning for Customer Churn Prediction: A
Comparison Study”. The Scientific World Journal, (2015).
