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.
Prepaid customer segmentation in telecommunications: An overview of common pr...Exacaster
The document discusses common practices for segmenting prepaid telecommunications customers. It describes segmenting customers to understand their behavior and needs, prioritize marketing resources, track changes over time, and focus predictive models. Key segmentation approaches include clustering customers based on usage data to identify distinct groups, separating customers by their lifecycle stage or spending amount to focus efforts, and tracking how acquisition cohorts and customer migration between value segments evolve over time. Segmentation allows tailoring offers, service, and communication to different customer subgroups.
Telco digital transfomation view by Exacaster 2019 Jolita Bernotiene
The document discusses the telecom industry's transition to digital services in response to increasing data usage, new technologies like 5G and AI, and competition from digital players. It notes that telecoms must transform digitally to become smart digital pipes with diverse service portfolios and customer-centric strategies to capture value from data and partners. This includes adopting data-driven operations, omnichannel experiences, and open ecosystems to better meet evolving customer expectations of low-cost, seamless connectivity and services. Exacaster helps telecoms accelerate this digital transformation.
Market Segmentation Strategies in Telecoms Industry Wystan Robertson
Market segmentation involves dividing a heterogeneous market into homogeneous subgroups with distinct needs, characteristics, or behaviors. The presentation discusses various bases for segmenting markets, including demographics, benefits sought, usage rates, and psychographics. It provides examples of segmenting the telecommunications market in Mabaruma, Guyana based on usage rates. Lower usage customers were identified as Classes B and C and a loyalty scheme was developed to increase their average revenue per user through targeted promotions. The scheme resulted in increased prepaid ARPU, brand loyalty, and handset upgrades for 56% of subscribers.
This document provides a case study of how Emarsys helped Airbnb launch in the Southeast Asia market. Airbnb needed help defining an omnichannel customer experience and marketing strategy. Emarsys developed a strategy using CRM ads to reach relevant audiences, product recommendations to increase cross-selling, and omnichannel marketing automation to maximize reach across devices and touchpoints. Tactics included retention-focused ad campaigns, lookalike audiences, personalized recommendations in emails and apps, and abandoned cart campaigns. The evaluation measured metrics like revenue, ROAS, funnels, CTRs, and increased conversions from one-time purchases to show the strategy's success.
TELCOs face challenges in retaining their large prepaid customer bases. They must break down customer retention into specific challenges and build tailored solutions. Key challenges include having limited signals to identify churn risk, needing to identify risky users earlier, and ensuring retention efforts actually impact those users. An effective strategy maps individual customer activity patterns over time to determine churn risk level and identify irregular behavior indicating higher risk. Targeted retention campaigns can then focus on specific at-risk segments.
LEGO has embraced change by combining business intelligence (BI) with a flexible information system. The database plays a key role in SAP's three-tier architecture by storing data on LEGO's products, operations, supply chain, and employees. Distributed databases improve performance and flexibility by allowing data to be stored across multiple computers and accessed worldwide. SAP's business software includes BI features like supply chain management, product lifecycle management, and enterprise resource planning. While distributed databases provide advantages like data access from various locations, they also involve additional complexity and overhead.
This document discusses how to model customer churn through machine learning. It defines churn as customers leaving or stopping usage. There are two types of churn - for subscription models where leaving can be clearly defined, and non-subscription models where leaving must be approximated. The document recommends predicting churn through classification models to identify potential churners, using customer behavioral and profile features over time. It also discusses evaluating models on validation data and using models to predict future churn and inform retention offers.
Prepaid customer segmentation in telecommunications: An overview of common pr...Exacaster
The document discusses common practices for segmenting prepaid telecommunications customers. It describes segmenting customers to understand their behavior and needs, prioritize marketing resources, track changes over time, and focus predictive models. Key segmentation approaches include clustering customers based on usage data to identify distinct groups, separating customers by their lifecycle stage or spending amount to focus efforts, and tracking how acquisition cohorts and customer migration between value segments evolve over time. Segmentation allows tailoring offers, service, and communication to different customer subgroups.
Telco digital transfomation view by Exacaster 2019 Jolita Bernotiene
The document discusses the telecom industry's transition to digital services in response to increasing data usage, new technologies like 5G and AI, and competition from digital players. It notes that telecoms must transform digitally to become smart digital pipes with diverse service portfolios and customer-centric strategies to capture value from data and partners. This includes adopting data-driven operations, omnichannel experiences, and open ecosystems to better meet evolving customer expectations of low-cost, seamless connectivity and services. Exacaster helps telecoms accelerate this digital transformation.
Market Segmentation Strategies in Telecoms Industry Wystan Robertson
Market segmentation involves dividing a heterogeneous market into homogeneous subgroups with distinct needs, characteristics, or behaviors. The presentation discusses various bases for segmenting markets, including demographics, benefits sought, usage rates, and psychographics. It provides examples of segmenting the telecommunications market in Mabaruma, Guyana based on usage rates. Lower usage customers were identified as Classes B and C and a loyalty scheme was developed to increase their average revenue per user through targeted promotions. The scheme resulted in increased prepaid ARPU, brand loyalty, and handset upgrades for 56% of subscribers.
This document provides a case study of how Emarsys helped Airbnb launch in the Southeast Asia market. Airbnb needed help defining an omnichannel customer experience and marketing strategy. Emarsys developed a strategy using CRM ads to reach relevant audiences, product recommendations to increase cross-selling, and omnichannel marketing automation to maximize reach across devices and touchpoints. Tactics included retention-focused ad campaigns, lookalike audiences, personalized recommendations in emails and apps, and abandoned cart campaigns. The evaluation measured metrics like revenue, ROAS, funnels, CTRs, and increased conversions from one-time purchases to show the strategy's success.
TELCOs face challenges in retaining their large prepaid customer bases. They must break down customer retention into specific challenges and build tailored solutions. Key challenges include having limited signals to identify churn risk, needing to identify risky users earlier, and ensuring retention efforts actually impact those users. An effective strategy maps individual customer activity patterns over time to determine churn risk level and identify irregular behavior indicating higher risk. Targeted retention campaigns can then focus on specific at-risk segments.
LEGO has embraced change by combining business intelligence (BI) with a flexible information system. The database plays a key role in SAP's three-tier architecture by storing data on LEGO's products, operations, supply chain, and employees. Distributed databases improve performance and flexibility by allowing data to be stored across multiple computers and accessed worldwide. SAP's business software includes BI features like supply chain management, product lifecycle management, and enterprise resource planning. While distributed databases provide advantages like data access from various locations, they also involve additional complexity and overhead.
This document discusses how to model customer churn through machine learning. It defines churn as customers leaving or stopping usage. There are two types of churn - for subscription models where leaving can be clearly defined, and non-subscription models where leaving must be approximated. The document recommends predicting churn through classification models to identify potential churners, using customer behavioral and profile features over time. It also discusses evaluating models on validation data and using models to predict future churn and inform retention offers.
1. eCRM provides companies with interactive and personalized communication across electronic and traditional channels while respecting customer preferences.
2. Building an effective eCRM solution requires defining business objectives, assessing current capabilities, and defining process changes needed to meet goals.
3. Key eCRM features include a focus on refining business processes, being data-driven to provide customized customer profiles and interactions, and measuring performance.
A major North American telecom sought to identify factors driving customer churn. We applied social network analysis over several billion call records. We found that customers with a cancellation in their frequent calling network churned at twice the monthly rate.
Many operators are still unable to match the customers who belong to the same household. Therefore, it’s hard for telecoms to identify the services shared within the same family such as wireless, pay-TV or music. Siloed understanding of subscribers leads to junk marketing campaigns followed by the negative customer experience. Customers simply do not convert!
1) Google is promoting search in India, where it has over 97% market share, because it recognizes that mobile usage is growing and will soon surpass desktop usage.
2) Google wants to integrate more services like Google Now into search to provide contextual information to users on their home screens based on location, time, and user history from Google services.
3) The ad aims to show users that Google can be a one-stop shop for all their information needs on mobile devices, replacing the need for individual weather and travel apps.
This document discusses customer segmentation using an unsupervised machine learning model. It analyzes transaction data from a brewery's 99,708 customers over 2 years to group them into meaningful marketing segments without prior customer information. A self-organizing map (SOM) model was used to iteratively cluster customers based on similarities. The optimal number of clusters was determined to be 3 based on cluster distances and averages. The 3 clusters identified were "Promising", "Explorers", and "High Value", with the latter found to be the best target segment for marketing due to its high sales percentage and visit frequency.
This document discusses customer segmentation and provides details on its various phases and processes. It is divided into the following key sections:
1. It outlines a three phase customer segmentation framework: customer segmentation, planning and execution, and institutionalization.
2. It then provides more details on the customer segmentation analytics process, including defining objectives, identifying relevant variables, data preparation, modeling, scoring, profiling segments, and identifying segment strategies.
3. Various statistical tools for segmentation like cluster analysis and CHAID are mentioned. Example attributes for segmenting banking customers and IT company customers are also listed.
1. An MVNO is a mobile operator that does not own wireless spectrum or network infrastructure and partners with traditional mobile network operators to offer mobile services using the host network operator's infrastructure.
2. An MVNE is a company that provides network infrastructure and services like provisioning and administration to enable MVNOs to offer services to their own customers without direct relationships with end users.
3. There are different types of MVNO business models ranging from lightest "branded reseller" to most complete "full-MVNO" with varying levels of investment and control over the mobile network.
This document discusses segmentation strategies for creating profitable customers. It begins by renewing the understanding of why segmentation is important for driving higher profitability through understanding customer needs. It then discusses profiting through segmentation by understanding customers and developing targeting strategies. It emphasizes investing in tools, skills, and systems to operationalize segmentation. Finally, it stresses the importance of demonstrating segmentation strategies through testing, measurement, and gaining organizational buy-in to make segmentation efforts successful.
Workshop : Segmenter sa base client - Bonnes pratiques, approches innovantes,...NP6
Ce support a pour but de vous présenter tous les avantages et les bonnes pratiques de la segmentation de données clients.
- Segmentation VS Typologie VS Scoring : quelles différences pour quels usages ?
- Les outils technologiques de la segmentation et de son activation
- La mise en place d’une segmentation : quelle méthodologie adopter pour mesurer les gains ?
- Les approches innovantes : quels sont les progrès récents de la recherche ?
Mercado Libre Publicidad - ¿Cómo llego a 1.000.000 de compradores con un míni...melidevelopers
El documento resume las principales tendencias de publicidad como native ads, behavioral targeting y publicidad móvil. También describe las soluciones de publicidad de Mercado Libre como anuncios nativos, anuncios de texto, patrocinios, banners y landing pages personalizadas, que permiten a las marcas llegar a audiencias específicas y aumentar las ventas en Mercado Libre.
This document provides an overview of customer segmentation techniques and applications for telecommunications. It defines customer segmentation as splitting a customer database into meaningful groups based on specific criteria. The goals are to gain customer insights, enable targeted marketing, and achieve competitive advantages. Various types of segmentation are described, including structural, categorical, and behavioral. Examples are given using dimensions like tenure, profitability, and risk. Effective customer metrics, technologies, infrastructure, and the segmentation lifecycle are also outlined.
Introduction Aux Conceptes Du MarketingImene Imentoo
Travail de recherche fournis par Guerdjoum Imene et Mesmoudi Saliha 3 eme année Marketing- Université Djilali Lyabes- faculté des Sciences Economiques et Sciences de Gestion- 2014/2015
Module: Marketing des Services
Ce travail est repartis en 7 parties:
Définition Du Marketing,
L’histoire Du Marketing En Quelques Dates,
L’importance Du Marketing (le Comptoir des Cotonniers),
Sur Quoi Porte Le Marketing?,
Qui Fait Du Marketing?,
Quels sont Les Concepts Du Marketing,
Les Démarche Marketing
LE YIELD MANAGEMENT HÔTELIER - PART 2
COMPRENDRE, METTRE EN PLACE ET ANALYSER UNE POLITIQUE TARIFAIRE
Ce Slideshare est un extrait d'une des formations en Yield hôtelier dispensée par e-axess.
cette présentation est concerne la gestion de la relation client, premièrement vous trouverez les modalités et les composantes de la GRC et deuxièmement le cycle de vie de client et l'intégration de GRC dans les entreprises et en fin la conclusion
Spencer's retail is the largest supermarket chain in India with 100 stores across 25 cities. The report analyzes Spencer's brand awareness and compares it to other retailers like Big Bazaar. It finds that while Spencer's is well-known, Big Bazaar has a better product range and greater customer acceptance. Most of Spencer's revenue comes from its FMCG, garments, gifts, and music departments. However, the report suggests that Spencer's should improve its product affordability and variety to better attract India's middle-income customers.
Online marketing is highly recommended for any kind of companies nowadays. In order to start the online marketing activity, one has to understand the basic 10c's for online marketers. Also they are supposed to know the environment around the organization to act according to the changes happening.
The document discusses customer segmentation and targeting youth customers in the Italian mobile market. It analyzes the youth segment based on their key characteristics like age, needs for self-expression, communication, and entertainment. It then outlines Vodafone Italy's youth proposition targeting these needs through offerings like ringtones, music, games, messaging promotions, and discounted call plans to increase engagement and market share among youth customers.
Carlson Marketing conducted a mobile segmentation study to understand different types of mobile users and how to target them. They identified personas based on usage frequency across voice, messaging, browsing and downloading. These ranged from basic voice users to "Mavericks" who are early adopters and power users. The study found increasing engagement with mobile functions correlates with greater technology adoption. It aims to help clients identify the right messages to attract each persona's response.
1. eCRM provides companies with interactive and personalized communication across electronic and traditional channels while respecting customer preferences.
2. Building an effective eCRM solution requires defining business objectives, assessing current capabilities, and defining process changes needed to meet goals.
3. Key eCRM features include a focus on refining business processes, being data-driven to provide customized customer profiles and interactions, and measuring performance.
A major North American telecom sought to identify factors driving customer churn. We applied social network analysis over several billion call records. We found that customers with a cancellation in their frequent calling network churned at twice the monthly rate.
Many operators are still unable to match the customers who belong to the same household. Therefore, it’s hard for telecoms to identify the services shared within the same family such as wireless, pay-TV or music. Siloed understanding of subscribers leads to junk marketing campaigns followed by the negative customer experience. Customers simply do not convert!
1) Google is promoting search in India, where it has over 97% market share, because it recognizes that mobile usage is growing and will soon surpass desktop usage.
2) Google wants to integrate more services like Google Now into search to provide contextual information to users on their home screens based on location, time, and user history from Google services.
3) The ad aims to show users that Google can be a one-stop shop for all their information needs on mobile devices, replacing the need for individual weather and travel apps.
This document discusses customer segmentation using an unsupervised machine learning model. It analyzes transaction data from a brewery's 99,708 customers over 2 years to group them into meaningful marketing segments without prior customer information. A self-organizing map (SOM) model was used to iteratively cluster customers based on similarities. The optimal number of clusters was determined to be 3 based on cluster distances and averages. The 3 clusters identified were "Promising", "Explorers", and "High Value", with the latter found to be the best target segment for marketing due to its high sales percentage and visit frequency.
This document discusses customer segmentation and provides details on its various phases and processes. It is divided into the following key sections:
1. It outlines a three phase customer segmentation framework: customer segmentation, planning and execution, and institutionalization.
2. It then provides more details on the customer segmentation analytics process, including defining objectives, identifying relevant variables, data preparation, modeling, scoring, profiling segments, and identifying segment strategies.
3. Various statistical tools for segmentation like cluster analysis and CHAID are mentioned. Example attributes for segmenting banking customers and IT company customers are also listed.
1. An MVNO is a mobile operator that does not own wireless spectrum or network infrastructure and partners with traditional mobile network operators to offer mobile services using the host network operator's infrastructure.
2. An MVNE is a company that provides network infrastructure and services like provisioning and administration to enable MVNOs to offer services to their own customers without direct relationships with end users.
3. There are different types of MVNO business models ranging from lightest "branded reseller" to most complete "full-MVNO" with varying levels of investment and control over the mobile network.
This document discusses segmentation strategies for creating profitable customers. It begins by renewing the understanding of why segmentation is important for driving higher profitability through understanding customer needs. It then discusses profiting through segmentation by understanding customers and developing targeting strategies. It emphasizes investing in tools, skills, and systems to operationalize segmentation. Finally, it stresses the importance of demonstrating segmentation strategies through testing, measurement, and gaining organizational buy-in to make segmentation efforts successful.
Workshop : Segmenter sa base client - Bonnes pratiques, approches innovantes,...NP6
Ce support a pour but de vous présenter tous les avantages et les bonnes pratiques de la segmentation de données clients.
- Segmentation VS Typologie VS Scoring : quelles différences pour quels usages ?
- Les outils technologiques de la segmentation et de son activation
- La mise en place d’une segmentation : quelle méthodologie adopter pour mesurer les gains ?
- Les approches innovantes : quels sont les progrès récents de la recherche ?
Mercado Libre Publicidad - ¿Cómo llego a 1.000.000 de compradores con un míni...melidevelopers
El documento resume las principales tendencias de publicidad como native ads, behavioral targeting y publicidad móvil. También describe las soluciones de publicidad de Mercado Libre como anuncios nativos, anuncios de texto, patrocinios, banners y landing pages personalizadas, que permiten a las marcas llegar a audiencias específicas y aumentar las ventas en Mercado Libre.
This document provides an overview of customer segmentation techniques and applications for telecommunications. It defines customer segmentation as splitting a customer database into meaningful groups based on specific criteria. The goals are to gain customer insights, enable targeted marketing, and achieve competitive advantages. Various types of segmentation are described, including structural, categorical, and behavioral. Examples are given using dimensions like tenure, profitability, and risk. Effective customer metrics, technologies, infrastructure, and the segmentation lifecycle are also outlined.
Introduction Aux Conceptes Du MarketingImene Imentoo
Travail de recherche fournis par Guerdjoum Imene et Mesmoudi Saliha 3 eme année Marketing- Université Djilali Lyabes- faculté des Sciences Economiques et Sciences de Gestion- 2014/2015
Module: Marketing des Services
Ce travail est repartis en 7 parties:
Définition Du Marketing,
L’histoire Du Marketing En Quelques Dates,
L’importance Du Marketing (le Comptoir des Cotonniers),
Sur Quoi Porte Le Marketing?,
Qui Fait Du Marketing?,
Quels sont Les Concepts Du Marketing,
Les Démarche Marketing
LE YIELD MANAGEMENT HÔTELIER - PART 2
COMPRENDRE, METTRE EN PLACE ET ANALYSER UNE POLITIQUE TARIFAIRE
Ce Slideshare est un extrait d'une des formations en Yield hôtelier dispensée par e-axess.
cette présentation est concerne la gestion de la relation client, premièrement vous trouverez les modalités et les composantes de la GRC et deuxièmement le cycle de vie de client et l'intégration de GRC dans les entreprises et en fin la conclusion
Spencer's retail is the largest supermarket chain in India with 100 stores across 25 cities. The report analyzes Spencer's brand awareness and compares it to other retailers like Big Bazaar. It finds that while Spencer's is well-known, Big Bazaar has a better product range and greater customer acceptance. Most of Spencer's revenue comes from its FMCG, garments, gifts, and music departments. However, the report suggests that Spencer's should improve its product affordability and variety to better attract India's middle-income customers.
Online marketing is highly recommended for any kind of companies nowadays. In order to start the online marketing activity, one has to understand the basic 10c's for online marketers. Also they are supposed to know the environment around the organization to act according to the changes happening.
The document discusses customer segmentation and targeting youth customers in the Italian mobile market. It analyzes the youth segment based on their key characteristics like age, needs for self-expression, communication, and entertainment. It then outlines Vodafone Italy's youth proposition targeting these needs through offerings like ringtones, music, games, messaging promotions, and discounted call plans to increase engagement and market share among youth customers.
Carlson Marketing conducted a mobile segmentation study to understand different types of mobile users and how to target them. They identified personas based on usage frequency across voice, messaging, browsing and downloading. These ranged from basic voice users to "Mavericks" who are early adopters and power users. The study found increasing engagement with mobile functions correlates with greater technology adoption. It aims to help clients identify the right messages to attract each persona's response.
The document discusses segmenting the internet market in Cameroon. It defines internet market segmentation and target market. Possible traditional segments include geographic, demographic, psychographic, and behavioral factors. Local realities suggest also considering gender, price, interests, location, religion, income, and household size. The market could be segmented into students/children, adults, professionals, and corporations. Further segmentation is proposed into four target groups: Home Riders, Generational Adventurers, Transit Masters, and Hot Sellers. Each group has different needs and motivations for internet usage.
Market segmentation involves dividing the total market into subgroups with similar needs and characteristics. It allows companies to target specific groups and focus their marketing efforts. The key benefits are increased marketing effectiveness, greater customer satisfaction, and the ability to design tailored marketing mixes. Some common bases for segmentation include demographics, behaviors, lifestyles, benefits sought, and geographic factors. Effective segmentation provides guidelines for resource allocation and helps organizations develop more focused strategies.
3.3.3.4 lab using wireshark to view network trafficAransues
Este documento proporciona instrucciones para usar Wireshark para capturar y analizar tráfico de red ICMP. Explica cómo descargar e instalar Wireshark, capturar tráfico ICMP local y remoto, y examinar las direcciones MAC y IP en las tramas capturadas. También describe las diferencias entre el tráfico local y remoto, como direcciones MAC diferentes para hosts remotos.
This document discusses the importance of using monitoring and evaluation (M&E) data to inform policy formulation, program planning, and improvement. It provides examples of how M&E data has been used in Nigeria to guide resource allocation and strengthen sub-national M&E systems. Barriers to data use include technical, organizational, individual, and political constraints. Ensuring data is effectively used requires understanding how it is produced and used, identifying and addressing barriers, and strengthening decision-making processes.
Leveraging Hadoop in your PostgreSQL EnvironmentJim Mlodgenski
This talk will begin with a discussion of the strengths of PostgreSQL and Hadoop. We will then lead into a high level overview of Hadoop and its community of projects like Hive, Flume and Sqoop. Finally, we will dig down into various use cases detailing how you can leverage Hadoop technologies for your PostgreSQL databases today. The use cases will range from using HDFS for simple database backups to using PostgreSQL and Foreign Data Wrappers to do low latency analytics on your Big Data.
Proper telephone etiquette is important to make a good first impression and maintain professionalism. Some key aspects of proper etiquette include answering calls promptly, using a friendly greeting, being courteous, listening attentively, speaking clearly, and ending calls politely. When mistakes happen, it is best to apologize, make things right, learn from the experience, and prevent recurrences.
If no one can find your information, it might be because you have too much online. To fix that, create your content with a lifecycle in mind. Here's everything you need to know about creating a lifecycle for your content. For more details, see the accompanying article at http://www.hilarymarsh.com/2014/02/19/content-lifecycle/
Thermo mechanical analysis (TMA) measures the relationship between a sample's length or volume and temperature. TMA instruments precisely measure both the temperature of a sample and very small movements of a probe in contact with the sample. TMA is mainly used to study polymers, characterizing polymers and assessing their mechanical properties. Some applications of TMA include measuring the thermal expansion of materials like aluminum, studying the effect of cross-linking and plasticizers on polymers, and determining the relationship between hardness and indentation.
This document provides an overview of traditional clothing in France. It describes some of the traditional clothing items worn by men, such as chemises (knee-length shirts), culottes or breech cloth (knee-length pants), hose (knee-high socks), and capotes or justacorps (heavy coats). It also outlines traditional clothing worn by women, such as jupons (short skirts), chemises (underskirts), mantelets (short waistcoats), and fichus (neck scarves). The document then discusses how the French Revolution caused reforms to clothing by ending social distinctions between classes and popularizing patriotic colors and symbols. After the revolution, clothing styles
Target Corporation is a major American retailer that offers discounted everyday and fashionable merchandise. It uses an integrated cost leadership and differentiation strategy to provide value to customers. Target differentiates itself from competitors like Walmart by marketing itself as "cheap chic" and bringing trends to stores faster. While Walmart is seen as a discount superstore, Target enforces a brand of quality products and high-end affordability. During the economic recession, Target emphasized value more through messages like "Fresh for Less" to address falling sales.
The document discusses stress, anxiety, and depression among the elderly and various coping strategies. It describes how stress can lead to anxiety and depression. Common stressors for the elderly are discussed such as loss, health issues, and social roles. The effects of stress on the body and mind are summarized. Various theories on aging and the impacts of stress are introduced. Strategies for coping with stress are provided, including social support, relaxation, exercise, and seeking help from mental health professionals when needed.
This document provides links to free resources for performance reviews, including phrases and samples. It discusses five areas that performance reviews may cover: attendance, attitude, communication, cooperation, and creativity/innovation. For each area, it provides positive and negative examples of phrases that could be used in a performance review. The overall document aims to provide managers with suggestions for wording to use when evaluating employees in common performance review categories.
Information technology can provide competitive advantages for organizations by improving efficiency and reducing costs. It allows organizations to better serve customers through mass customization, online services with reduced wait times, and improved communication. While hardware, software and IT infrastructure can be easily copied, an organization's managerial skills in effectively applying IT to business processes can be a source of sustained competitive advantage if they are difficult for competitors to replicate.
Export Credit Insurance (ECGC) provides insurance to exporters against payment risks from overseas buyers. Payments for exports face risks from events like war, economic crises, or buyer insolvency. ECGC was established by the Indian government to encourage exports by protecting exporters from these political and commercial risks. It offers various insurance covers to exporters and guarantees to banks to facilitate export financing. The insurance enables exporters to confidently expand overseas business without fear of losses from unpaid exports.
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.
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
This document discusses an approach for seamless real-time and batch processing of telecom transaction records. The approach allows telecom service providers to transition from offline batch processing of stored data to real-time analysis of streaming data. The approach is demonstrated through implementing a telecom revenue assurance solution using IBM Infosphere Streams, which can process stored call detail records (CDRs) offline and analyze streaming CDR data in real-time. This dual mode processing approach could benefit other domains like utilities, banking that deal with high volumes of transaction records.
Proposed ranking for point of sales using data mining for telecom operatorsijdms
This study helps telecom companies in making decisions that optimize its sales points to reduce costs, also
to identify profitable customers and churn ones. This study builds two research models; physical model for
continuous mining of database where ever it resides i.e., as we have On Line Analytic Processing (OLAP)
we must have On Line Data Mining (OLDM), and logical model using Technology Acceptance Model.
Previous Studies showed that using basic information of customers, call details and customer service
related data, a model can effectively achieve accurate prediction data.
This research gives a new definition and classification for telecommunication services from the data
mining point of view. Then this research proposed a formula for total rank a shop and each term of this
formula gives a sub rank. The proposed example shows that even a shop with lower numbers of population
and visitors, it still has higher rank.
This research suggested that telecom operators has to concentrate more on their e-shopping and epayment
as it is more cost effective and use data from shops for marketing issues. Some assumptions made
in this study need to be validated using surveys, also proposed ranking should be applied on live database.
EVALUTION OF CHURN PREDICTING PROCESS USING CUSTOMER BEHAVIOUR PATTERNIRJET Journal
This document summarizes research on predicting customer churn in the telecommunications industry. It first defines customer churn as the rate at which customers stop doing business with a company. It then reviews several past studies that have used techniques like decision trees, neural networks, and data mining to predict churn. The proposed research aims to develop a new churn prediction model using natural language processing (NLP) and machine learning approaches to improve accuracy. It will identify customer behavior patterns and evaluate factors that influence prediction accuracy. The model will be trained and tested on a telecommunications data set to calculate churn rates on both monthly and daily bases. This will help enhance customer service. Gaps in past research identified include issues with imbalanced data, high error rates, and
An efficient enhanced k-means clustering algorithm for best offer prediction...IJECEIAES
This document summarizes an article that proposes an enhanced k-means clustering algorithm to identify customers in a telecom company's dataset that are likely to upgrade to a higher-tier service package. The algorithm first performs customer profiling then applies k-means clustering to segment customers into homogeneous groups. It aims to more accurately identify potential customers for package upgrades compared to traditional k-means. The results showed the proposed approach achieved over 90% accuracy while traditional k-means was under 70%.
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
This document summarizes a research paper that predicts customer churn using logistic regression with regularization and optimization techniques. The paper applies these techniques to predict churn customers in the banking, e-commerce, and telecom sectors. It first discusses customer relationship management (CRM) and how data mining can be used for customer churn prediction. Then, it describes logistic regression and how the proposed method adds regularization and optimization to improve accuracy. The method is tested on datasets from the three sectors to classify customers as churners or non-churners. The paper finds that adding regularization and optimization to logistic regression enhances its performance in customer churn prediction.
IRJET - Customer Churn Analysis in Telecom IndustryIRJET Journal
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Customer segmentation for a mobile telecommunications company based on service usage
1. Customer Segmentation
For a Mobile Telecommunications Company
Based on Service Usage Behavior
By:
Shohin Aheleroff
Advisor:
Dr. Gholamian
Jun 2011
Abstract: 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. One of the most important data mining technologies
is customer clustering and segmentation. This targeting
practice has been proven manageable and effective for
mobile
telecommunications
industry.
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. Finally, the
subscribers categorized in four loyal groups and the
strategy to apply has been suggested in a specific life cycle.
I. INTRODUCTION
Mobile operator’s profits and ARPU (Average Revenue Per
User) are facing great challenges. Customer’s demand and
requirements of services has been changed. In order to
improve mobile operator’s competitiveness and customer
value, several data mining technologies can be used. One of
most important data mining technologies is customer
clustering analysis to categorize potential customers into
distinct groups for distinctive contact strategies. With the rapid
growing marketing business, data mining technology is imply
more important role in the demands of analyzing and utilizing
the large scale information gathered from customers especially
large amount call detailed record of mobile customers.
Information about customer’s behavior is required to segment
and personalize products and services along with business
strategy and planning.
But most of them segment customers only by personal
information such as age, gender and address from special
points, rather than from their actual behavior. Furthermore,
one of the key purposes of customer segmentation is customer
retention to increase the loyalty and avoid churning. This
paper focused on proposing a customer segmentation
framework based on actual customer behavior.
II. Research methodology
This study is designed to discover the patterns of use for
mobile services based call/event detailed records including
major service usage information and not personal information.
There are many clustering method, for example, fuzzy
clustering method, system clustering method, dynamic
clustering method and K-means clustering method. But the Kmeans method of cluster detection is most commonly used in
practice that the number of clusters is an input. Based on
business and infrastructure constrain generally operators are
comfortable to have as few as five unique segments, while
other require as many as twenty segments to satisfy their datadriven marketing needs. The decision of how many customer
segments a company should create is largely dictated by the
particular make-up of their customer base and the
organizations ability to develop and deliver unique segment
specific marketing treatments.
Fig.1 Data flow from Network Elements to IS downstream systems
III. Prepare the data for clustering
2. I found a set of valuable information to identify core needs of
subscribers based on their call detail record instead of their
personal information such as gender, address and income. A
Call Detail/Data Record contains at a minimum the following:
The number making the call (A number)
The number receiving the call (B number)
When the call started (date and time)
How long the call was (duration)
Call Type :
o
o
o
o
Voice call
SMS
Data (content)
GPRS/MMS
Balance before & after.
Location of mobile generator & terminator.
Incoming and outgoing Voice
Incoming and outgoing SMS
Different type of content
In addition to CDR we also should consider subscribers
interest to active or change any service by capturing their
action via analyzing Event Detail Records. By getting CDRs
from different sources, we would be able to make sure that
customers’ behavior captured and we can evaluate what they
are interested more and less. Any call or event from network
elements such as IN / CCN and MSC will pass through ODS
via mediation (Fig.1 shows the principle of data flow from
N.Es to IS downstream systems ) , so by accessing to the pull
of xDRs and defining proper characters ,we build our model of
customer segmentation based on their call/event detailed
record.
Considering the bellow steps (as illustrated in the Fig.2,) we
need enterprise hardware and software environment to deal
with huge amount of data (generated CDRs & EDRs) but we
also can consider sample of data to evaluate customer habits
and behaviors. Many segmentation algorithms and software
applications such as SAS and SPSS are already developed but
the most important is to follow bellow steps:
The number of data in GSM is a barrier to analysis
customer‘s behavior, so almost there is a limitation to
analysis the whole data.
CDRs & EDRs collected (by push or pull mechanism
into a data warehouse)
Different services selected:
o GPRS / MMS
o SMS
o Content e.g. RBT, Wallpaper, Java
application, Push mail, Music, Clip.
o Voice
Specific factors selected as the core items to monitor
customer‘s behavior.
Apply k-means as a well-known segmentation
algorithm.
Customer segmentation as per each recognized
factors to generate a matrix of segments.
Using Segmentation output for loyalty and customer
churn application.
Fig.2 Customer Segmentation Model
IV. CASE STUDY:
A MOBILE OPERATOR’S CUSTOMERS
CLUSTERING ANALYSIS
According to CDR (call detail records) analysis of a mobile
operator, located in the Middle East that has about 35 million
mobile subscribers, in a normal day and also holiday, the trend
shows that, the SMS usage is quite more than Call. As
Customer Segmentation is the process of splitting a customer
database into distinct, meaningful, and groups, the major
parameters such as call duration, balance, call type, tariff plan
and call time needs to be considered.
As mentioned earlier in many methods number k of clusters to
construct is an input user parameter. Running an algorithm
several times leads to a sequence of clustering systems.
Selection of the number of clusters (e.g. 10, 5, and 3) before
K-means implementation is required. However to achieve the
optimum number of clusters using the data histogram (Fig.3)
will help to make a decision as a practical solution.
The majority of subscribers use to have less call duration and
only few of them have calls up to 200 seconds.
Fig.3 Call Duration vs the number of Subscribers
3. By focusing on Call Duration and using K-means with the five
numbers of clusters (Table.1.), the center of selected clusters
areas after 19 number of iteration has been changed. The
minimum distance between initial centers is 165.000 (between
5th & 3rd segments) and the maximum is 200(between 2nd &
4th segments).The final center of clusters versus initial cluster
center has been improved as resulted in Table2.As we
expected the number of cases in each cluster is not close to
each other. According to call duration histogram, the people
who their call duration (15085 cases in 5th segment) is quite
short are more than the others.
In addition to call duration exercise, the same practice is
applicable on the other CDRs parameters such as balance
before / after, SMS, MMS, GPRS usage and other content
based parameter for customer segmentation purpose.
Furthermore, it’s highly suggested to have a matrix of major
parameters to come up with a unique plan instead of each
individual service segments.
Final Cluster Centers
Cluster
1
CALL_DURATION
2
3
4
5
421
139
59
257
10
Initial Cluster Centers
Cluster
1
CALL_DURATION
2
3
4
5
722
337
166
537
1
Distances between Final Cluster Centers
Cluster
1
1
2
3
Iteration
1
2
3
4
5
1
.000
19.596
28.955
43.135
15.805
2
51.500
27.633
12.061
30.882
.747
3
28.833
19.922
9.615
25.645
.756
4
53.381
16.630
7.859
20.455
.638
5
30.357
13.194
6.555
19.908
.565
6
22.833
11.932
5.573
19.376
.540
7
20.995
9.350
4.950
16.043
.562
8
14.232
8.933
4.130
13.544
.458
9
11.224
9.720
3.325
14.647
.344
10
14.287
8.156
3.379
12.287
.381
11
7.786
7.666
2.777
10.875
.324
12
5.498
7.140
2.288
6.635
.174
13
7.047
8.058
3.272
8.796
.350
14
11.129
7.275
2.209
10.422
.178
15
8.958
6.040
2.922
7.709
.342
16
6.050
5.457
1.844
7.140
.163
17
5.223
4.057
1.790
5.896
.230
18
.633
3.315
1.584
3.267
.211
Table.1 implementation of K-means (k=5) for call duration
282.059
3
Change in Cluster Centers
5
282.059 361.953 163.896 411.675
2
Iteration History
4
79.894
361.953
79.894
4
163.896
118.163 198.057
5
411.675
129.616 49.722
118.163 129.616
198.057 49.722
247.779
247.779
Table.2 Initial, Final and Distance between Cluster Centers
V. Customer type definition:
According to the definition of customer’s behaviors and due to
the behavior of subscribers it’s clearly shows where they are.
o Plain Loyal:
A customer that has always been Active (never went into
Dormancy or Churn status).
o Not Dependable:
A customer has reached the Churn status for the first time. He
may in the future either stay in Churn status or return to
Active (he will then be labeled ‘Loyal under Incentive’ from
now on until he reaches again and for good the Churn status).
o Fence Seated:
A customer has moved out of Active into Dormancy for the
first time. She/he may either fall into the Churn status, remain
Dormant, are become Active again.
o Loyal under Incentive:
A customer that has moved (once or several time) out of
Dormancy or Churn and back into Active status.
Based on the level of loyalty of customers during their life
cycle (Fig.3) we really need to keep the plain loyal motivated
and also provide proper motivation and package to improve
their loyalty.
4. o
o
o
o
Fig.3 Customer Life Cycle
In order to get a full picture of customer behavior in the
network and to realize their interest and also to predict their
behavior based on historical call detail record, we will analysis
mentioned scenarios in detail to identify
o
o
Which segment he/she falls into and what are the
characteristics of this segment including revenue value to
the business.
What is the risk of this customer to leave the network or
remain inactive
VI. Customers’ Behavioral Evolution
During their Life Cycle:
Based on six month historical data from enterprise data
warehouse, the statistics report of customers life cycle shows
(Fig.4) that more than 50 percent of plain loyal subscribers are
significantly decreasing while the other there type of
subscribers are not in the same level or even increasing such
as loyal under incentive subscribers.
I would like to highlight that due to behavior of subscribers
during the selected period of customers’ life cycle, we can
predict that both loyal plain and loyal under incentive
subscribers will reach to a single point. In this situation the
two various segments will merge to a unique group with
population of 40 present of total subscribers.
The volume of subscribers (Fig.4) shows that the operator is
quite a bit in a safe side at this stage; however the monitoring
of customers behavior illustrated that we will face high rate of
changes in near future.
All the subscribers are active in the network till their status
will be changed to dormant and churned status. As soon as
they become a dormant subscriber, the risk will be remained to
leave the network and churn. The highest challenge is related
to the fence seated group because they have potential capacity
to change their status into loyal incentive in a very optimistic
view or they will leave the network for ever (pessimistic).
Besides the total number of subscribers in each segment, we
need to more focus on the detailed information and track the
dynamic behavior of subscribers in each group and find a
proper answer for the following questions:
What happened to the ‘Fence Seated’ customers?
(7% of base)
What happened to the ‘Not Dependable’ customers?
(16% of base)
What happened to the ‘Loyal’ customers? (57% of
base)
Where do each ‘’Loyalty type’ sit and what strategy
to apply?
Distinguish between groups, is the key milestone to make
distinguish promotion and motivation for each individual
groups. By the same way, marketing managements can design
more suitable marketing strategy.
The behavior of Fence seated subscribers shows that all the
existing promotions and various tariff plan have not any
impact on this group, so as per detailed graph (Fig.5) after one
month only 41% of this subscribers remained in the same
situation while the 60% divided into two equal parts and joint
into “Not dependable” and “ Loyal under inventive” groups.
It’s so interesting that the after about four month the loyal
under incentive (66%) group members increased to double
compare to the Not dependable (33.5%) group members.
By focusing on not dependable subscribers which are 16% of
total subscribers, it’s illustrated that only 10% of these people
moved to loyal under incentive while the rate of movement
increased up to 37% after four month. I would like to highlight
that not dependable staff only moved to loyal under incentive
group and not to any other group. this is a good message to
keep continue and boos the existing marketing plan to increase
the number of loyal under incentive staff at the first step.
The goal is to make a plan to have specific motivation to move
three segments into the plain loyal group. The selected period
of subscribers’ life cycle is totally in a green status as more
than half out of total subscribers is plain total. There is a big
risk of churning to other operators because during the last six
month the trend of loyal subscribers is not fluctuated and
decreased to 49% that shows 8 % drop down to other groups.
Besides not dependable and fence seated groups, the two other
loyal groups have high rate of upward (Loyal under Incentive
from 19% to 33%) and downward (Plain Loyal from 57% to
49%) changes.
Fig.4 Customer Life Cycle
5. VII. Where do each ‘’Loyalty Type’ sit
What strategy to apply?
Any operator required to build strong, profitable customer
relationships with solutions that increase average revenue per
user, reduce subscriber churn and enhance brand loyalty.
In order to utilize the best criteria, two important parameters
selected to identify level of loyalty based on monthly recharge
and active on net (AON) for each segment of subscribers.
The propagation of subscribers and the location of each
loyalty type will lead the business to make an adequate plan,
so based on two mentioned items we mapped (Fig.6) four
loyalty groups into a matrix of duration (10 to 16 month) and
recharge (from 60$ to 100$) monthly basis. The margin for
AON is 13month and the recharge is 80$ monthly basis. It
means that if a subscriber is active more that 13 month on
network, then its ether plain or under incentive loyal
subscribers depend on the volume of recharge per month.
By the same way if they are less that 13 month active on
network then they are either fence seated or Not dependent ,
so it shows that we need development plan to keep them more
active on net by offering new packages as motivations.
As mentioned the boundary for monthly revenue is 80$, so it’s
quite important that even the subscribers who charge more
than specified range (80$) they are not loyal.
In addition to mentioned parameters to identify behaviors and
level of loyalty, we need to consider the service usage and also
the dependency or relation between services as part of cross
functional management to improve customer segmentation.
After we recognized the location of loyal subscribers, it’s time
to plan the right strategy for each specific segment.
The size and the status of each loyal group have been
illustrated in the Fig.6 based on their monthly recharge and
active time on the network. Both fence seated and not
dependable subscribers have less size than other two groups
and they stayed around 12 month on the network as an active
subscriber, while fence seated staff pied more than boundary.
On the other hand, two plain and under incentive loyal groups
are almost 15 month active on the net with different monthly
recharge payment. It’s clearly advised to motivate the loyal
under incentive subscribers to buy more vouchers (for
prepaid) or bill payment (postpaid) as the right strategy to
make then plain loyal as they are 33% of all subscribers.
As a general concern and risk, we might loss the entire 14% of
not dependable subscribers if they refuse to do payment.
By considering the size of each loyal group , the average
revenue per user and the location of group in the matrix
(Fig.6) , at least we would be able to initiated a strategic plan
because sometime it’s very costly to motivate this group than
put more effort and cost on the loyal under incentive or fence
seated groups to make them plain loyal on the network, so
depend on the constraints such as budget , priority and the
number of subscribers in each group, management will be able
to make a decision accordingly.
Fig.5 Detailed Behavior of Customer Life Cycle
6. XI. REFERENCES
[01] Ngai, E.W.T., Xiu, Li., Chau, D.C.K. (2008).
“Application of Data Mining Techniques in Customer
Relationship
Management.”
Expert
Systems
with
Applications, No.8:003-124.
[02] Kim, Su-Yeon. , Jung, Tae-Soo. , Suh, Eui-Ho., Hwang,
Hyun-Seok. (2006).“Customer segmentation and strategy
development based on customer lifetime value.” Expert
Systems with Applications, Vol.31:101–107.
Fig.6 Loyalty type subscribers based on their monthly recharge and active on
net duration every month
VIII. CONCLUSION
The mobile telecommunication marketplace is highly
competitive. The operators often need to design
distinguishable marketing strategy based on different behavior
of their mobile subscribers in order to improve their marketing
result and revenue. Call Detail Records describe customer
utilization behavior. They have more information to describe
customer behavior than billing system data. Clustering
analysis based on call detail records can give more
information than other clustering analysis for marketing
management. We suggested a customer life cycle model
considering the past contribution, potential value, and churn
probability at the same time. The model used for customer
segmentation. Three perspectives on customer value (current
value, potential value, and customer loyalty) assist marketing
managers in identifying customer’s segmentation with more
balanced viewpoints. After identification of subscriber’s
behavior and identification of loyal groups, it’s feasible and
possible to put mobile customer clusters in place and make an
applicable strategic plan for each group to achieve customer
satisfaction.
IX. ACKNOWLEDGMENT
The author would like to thank Dr. Gholamian for his support
in the direction of the thesis in Shiraz University. During
working on it, I got a lot of help from both supervisor and
colleagues within MTN Iran. My supervisor always tracks the
work to make sure there is no problem, and if there is, he
would give immediate help to solve the problem. And he also
gave me a good idea on how to write the thesis and what is the
process. Thanks to my colleagues, they gave me lots of
encouragement and help on both studies and work. Thanks to
my wife, she always let me know she love me which gave me
motivation. At last, I want to thank this program for the
opportunity to grow and share knowledge.
[03] So Young, Sohn. Kim, Yoonseong. (2008). “Searching
customer patterns of mobile service using clustering and
quantitative association rule.” Expert Systems with
Applications, Vol.34:1070–1077.
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