Prevent your customers from reducing their purchases of products and services by using Customer Attrition Management PowerPoint Presentation Slides. Discuss various ways through which the company can manage the customer churn by using customer churn management PPT slideshow. The presentation describes key statistics associated with customer churn, present customer situation in a firm, types of customer churn, methods to handle customer attrition, churn propensity model, the impact of successful implementation of churn management, dashboard, etc. Customer attrition PPT templates help to track the customer churn rate on monthly basis. Discuss four stages of customer churn management which allows the company to handle customer attrition. Take advantage of customer churn analysis PPT layouts and showcase how the firm can prevent customer churn. The company can handle the churn issue by detecting the churn signals and triggers from the customer. Depict information about the various marketing campaigns in order to retain its customer from churning. Provide ways to prevent churn through predictive analysis by downloading customer churn prediction PPT presentation. https://bit.ly/2R7GztG
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
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.
This document discusses predicting customer churn for mobile operators to prevent customer loss, increase satisfaction and optimize network usage. It provides details on churn rates in India, data collection methods used including oversampling, and analysis performed on factors like network coverage, call quality and customer support to develop a churn prediction model. The analysis was conducted by a team of 7 people using techniques like p-value analysis, affinity diagramming and tweaking oversampling through trial and error.
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.
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.
This document discusses using machine learning models to predict customer churn for a telco company and provide targeted recommendations to reduce churn. The data was explored and customers were segmented into 5 clusters based on usage. Decision tree and random forest models were used to determine why customers churn and which customers churn for each cluster. Targeted recommendations were then provided for different customer categories within each cluster, such as offering free international minutes, discounted calling rates, or prioritized customer service. The results showed the potential to avert $555,700 in annual revenue loss through reducing customer churn.
Customer churn prediction for telecom data set.Kuldeep Mahani
This document analyzes customer data from a telecom company to predict customer churn and develop retention programs. Both logistic regression and random forest models were tested, with random forest found to have slightly higher accuracy at 83% versus 79% for logistic regression. The top four factors influencing churn were identified as total spending in 2017, data consumption, off-net spending, and customer tenure. The document recommends introducing attractive SMS packages, new off-net calling schemes, and more flexible data schemes tailored to individual customer spending to help increase customer retention.
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
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.
This document discusses predicting customer churn for mobile operators to prevent customer loss, increase satisfaction and optimize network usage. It provides details on churn rates in India, data collection methods used including oversampling, and analysis performed on factors like network coverage, call quality and customer support to develop a churn prediction model. The analysis was conducted by a team of 7 people using techniques like p-value analysis, affinity diagramming and tweaking oversampling through trial and error.
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.
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.
This document discusses using machine learning models to predict customer churn for a telco company and provide targeted recommendations to reduce churn. The data was explored and customers were segmented into 5 clusters based on usage. Decision tree and random forest models were used to determine why customers churn and which customers churn for each cluster. Targeted recommendations were then provided for different customer categories within each cluster, such as offering free international minutes, discounted calling rates, or prioritized customer service. The results showed the potential to avert $555,700 in annual revenue loss through reducing customer churn.
Customer churn prediction for telecom data set.Kuldeep Mahani
This document analyzes customer data from a telecom company to predict customer churn and develop retention programs. Both logistic regression and random forest models were tested, with random forest found to have slightly higher accuracy at 83% versus 79% for logistic regression. The top four factors influencing churn were identified as total spending in 2017, data consumption, off-net spending, and customer tenure. The document recommends introducing attractive SMS packages, new off-net calling schemes, and more flexible data schemes tailored to individual customer spending to help increase customer retention.
Customer Relationship Management Model PowerPoint Presentation SlidesSlideTeam
Getting problem in managing customer retention? We have come up with professionally created customer relationship management model PowerPoint presentation slides. Organizations can participate to manage their relations with customers at all points during the customer lifecycle. Furthermore, in this pre-designed professionally created CRM framework PPT presentation we have included slides on customer initiatives, customer perceived value, emphases on client transaction, traction vs relationship marketing, economic retention, CRM model, lifecycle management, CRM stages and components, E-CRM process, current lead status, lead and deal acquisition, customer retention, CRM capabilities, bar charts and many more. Apart from this, with our relationship development PPT slides, you can also emphasize various other concepts like customer business relationship, sales force planning, consumer management, organizational planning, customer satisfaction, customer services, consumer technical support and business review. The best part is that you can also easily download our customer relationship management model PowerPoint templates. Get all the ingredients to fit exactly with our Customer Relationship Management Model PowerPoint Presentation Slides. It allows you to attempt the jigsaw.
This presentation provides insight into how to forecast and calculate customer lifetime value (CLV). Here a startup applied a scientific approach to maximise customer retention and minimise churn. The outputs of the analytics were built into the system and business processes driving the success of the company and helping it to win the customer service of the year award, and to achieve a successful exit through acquisition.
This document discusses calculating and analyzing customer lifetime value (CLV). It provides formulas for calculating CLV based on yearly gross contribution per customer, retention rate, discount rate, and retention costs. An example calculation is shown. The document also discusses how retention rate affects CLV and introduces the concepts of net promoter score (NPS) and customer likeability score to categorize customers. It proposes mapping customers on a "grid" based on these metrics to identify the most and least valuable relationships.
- The document describes a project to predict customer churn for a telecom company using classification algorithms. It analyzes a dataset of 3333 customers to identify variables that contribute to churn and builds models using KNN and C4.5.
- The C4.5 model achieved higher accuracy (94.9%) than KNN (87.1%) on the test data. Key variables for predicting churn were found to be day minutes, customer service calls, and international plan.
- The model can help the telecom company prevent churn by focusing retention efforts on at-risk customers identified through these important variables.
This document discusses predicting customer churn in the telecom industry. It outlines collecting customer data from Kaggle on services used, account details, and demographics. Exploratory data analysis finds recent and higher spending customers more likely to churn. Feature selection and encoding is done before imbalanced data handling with SMOTE tomek. Various classifiers are tested with random forest performing best with an AUC of 0.834. Partial dependence plots and SHAP values are used to explain the model. Finally, a web app is created and deployed on Github to predict churn probabilities and help telecom companies reduce customer churn.
The document discusses Amazon's business and technologies. It provides an overview of Amazon as an e-commerce and cloud computing company, discusses its use of Linux, Oracle databases, and secure payment systems. It also describes how Amazon utilizes robots in warehouses, Kiva robots to aid in picking and packing, and its plans for drone delivery via its Prime Air system to deliver packages using vertical take-off drones that can achieve speeds of 100kph and dodge birds.
This document discusses customer churn, which refers to the rate at which customers leave a company. It states that reducing churn by 5% can boost profits by 75% and that US companies lose $1.6 trillion per year to churn. Common causes of churn include poor customer service, price, functionality, and changing customer needs. The document promotes MECBot, a data analytics product that can detect, prevent, and reduce churn in real-time through capabilities like churn scoring, campaign management, and customer lifetime value maximization.
Customer Churn Management For Profit Maximization PowerPoint Presentation SlidesSlideTeam
The PowerPoint template allows firm in preventing its customers from reducing their purchase of products and services. It will help firm by providing various ways through which firm can manage their customer churn. It will cover details about churn propensity model. The template covers details about key statistics associated to customer churn. It covers details about present situation of customer attrition and customer churn rate on monthly basis. Customer churn is considered as critical issue which affect firms overall firm performance, due to which firm will incur heavy losses in terms of abandon purchases and lower revenues. It is to be noted that retaining customer is more profitable than acquiring new customers. The template will cover information regarding various types of customer churn such as when customer stops spending, churn due to product quality or complete customer account loss. The template will provide details about how firm will handle customer attrition by focusing on four stages of churn management by acquiring churned customers, delighting customers, preventing customer attrition, and saving customers through various campaigns. The template covers details about churn propensity model which will help preventing customer churn through predictive analytics by utilizing different statistical techniques such as machine learning. https://bit.ly/36qQZKg
Customer Churn prediction in ECommerce Sector.pdfvirajkhot5
Retaining customers is a challenging issue that is encountering most of organizations, particularly businesses operating in e-commerce sector. According to Wu et al., (2017), it is much more difficult to retain the existing customers as compared to attract new ones because existing customers provides high value in ecommerce; however, to attract new customers, companies need to invest a lot of money for making them as loyal customers. This study will develop a prediction model for E-commerce sector to correlate the key attributes leading to churn.
This document discusses predicting customer lifetime value (CLV) for an auto insurance company using various machine learning models. It outlines preprocessing the data, feature engineering, building models like linear regression, ridge regression, decision trees, random forests, and evaluating the models using R2. Random forest was found to have the best performance with an R2 of 0.913. The conclusions recommend focusing on male customers, specific coverage offers, and tailored sales strategies based on customer demographics to improve CLV predictions.
- Amazon was founded in 1994 by Jeff Bezos and launched online in 1995 as an online bookstore (Paragraph 1)
- It has since grown to be one of the largest online retailers, selling over 20 million products globally through its various websites (Paragraph 5)
- Key to Amazon's success has been its focus on customer value, strong logistics network, and long term vision (Paragraph 4)
The document discusses customer churn risk and how to develop predictive churn models. It defines risk as having two components: uncertainty and exposure to that uncertainty. When building a churn model, the key steps are: defining active vs churned customers, selecting relevant customer data, analyzing characteristics to identify predictors, developing a predictive score using methods like logistic regression, and evaluating the model's ability to identify customers likely to churn. The goal of a churn model is to provide insights for preventing churn, not just statistical precision.
Case study: Amazon improvement of customer serviceSulaman Muhammad
Amazon has improved its customer service by improving inventory management in several ways:
1. It increased the number of warehouses to reduce delivery times and opened warehouses near customers.
2. It offered a wide range of products to improve availability and purchase directly from publishers to shorten times.
3. It used location and manufacturing postponement to centralized inventory and delay customization. It also outsourced inventory of less popular products and used drop shipping to focus on e-commerce while maintaining selection and delivery speed.
4. Amazon automated warehouses using barcodes and conveyor belts to efficiently receive, track, pack and ship high volumes of orders.
UT had the highest difference in average international call minutes between churning and non-churning customers, indicating international call rates were a likely reason for churn. Several other states also showed differences in international minutes, suggesting international plan rates affected churn. The report analyzed call minute differences and built a logistic regression model to predict churn behavior from the telecom usage data.
RFM (Recency, Frequency, Monetary value) analysis measures a customer's recency of purchase, visit frequency, and typical monetary value. It helps marketers identify their best customers, customers at risk of churning, and potential customers. CleverTap's RFM dashboard visualizes customers on a 2D graph by recency and frequency scores. It segments customers into 10 groups including Champions, Potential Loyalists, New Customers, and At-Risk Customers to target personalized campaigns.
SaaS Metrics: The Secret to Subscription SuccessEd Shelley
This document discusses key metrics for Software as a Service (SaaS) businesses including Monthly Recurring Revenue (MRR), churn rate, customer lifetime value (LTV), and others. It defines each metric, how it is calculated, and why it is important to measure. The document advocates measuring these metrics to better understand customer acquisition, retention, and revenue over time in order to improve the business. It provides examples of how leading SaaS companies use these metrics to manage and grow their subscriptions.
How amazon Works. - Supply chain management.PankajWagh26
Amazon started in 1995 selling books from Jeff Bezos' garage. It now sells a huge variety of products online. Amazon uses a hybrid supply chain model, delivering some products itself and using other logistics firms to deliver marketplace products. It has a large data warehouse running on HP servers with Oracle software to store customer and product data. Amazon also prioritizes security, encrypting customer information and credit cards during checkout. It has been successful in expanding product offerings through partnerships with other retailers and sellers.
IRJET - Customer Churn Analysis in Telecom IndustryIRJET Journal
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
The document discusses various strategies for customer retention, including rewarding customer behavior, establishing loyalty programs, using transactional emails, surprising customers, and being active on social media. The goal of these strategies is to encourage customers to repeat purchases and refer others by adding value at every step of the customer journey. The most effective retention strategies stand for a clear value proposition, use social proof from other customers, position their product to solve customer needs, and focus on continually improving the customer experience through easy problem solving and gratitude.
Chapter 4 Creating Customer Value, Satisfaction, and LoyaltyNishant Agrawal
1. Conducting periodic customer satisfaction surveys to understand customer perceptions and gather feedback.
2. Monitoring customer loss or defection rates to identify potential issues.
3. Tracking the performance of competitors to benchmark satisfaction levels.
Creating Customer Value, Satisfaction and Loyalty / Marketing Management BY ...Choudhry Asad
The document discusses key concepts related to customer value, satisfaction, and loyalty. It covers traditional versus modern customer-oriented organizations, determining customer perceived value and its drivers, measuring customer satisfaction, defining loyalty, and frameworks for customer relationship management. Steps for customer value analysis, retention, and managing the customer base are also outlined.
Customer Relationship Management Model PowerPoint Presentation SlidesSlideTeam
Getting problem in managing customer retention? We have come up with professionally created customer relationship management model PowerPoint presentation slides. Organizations can participate to manage their relations with customers at all points during the customer lifecycle. Furthermore, in this pre-designed professionally created CRM framework PPT presentation we have included slides on customer initiatives, customer perceived value, emphases on client transaction, traction vs relationship marketing, economic retention, CRM model, lifecycle management, CRM stages and components, E-CRM process, current lead status, lead and deal acquisition, customer retention, CRM capabilities, bar charts and many more. Apart from this, with our relationship development PPT slides, you can also emphasize various other concepts like customer business relationship, sales force planning, consumer management, organizational planning, customer satisfaction, customer services, consumer technical support and business review. The best part is that you can also easily download our customer relationship management model PowerPoint templates. Get all the ingredients to fit exactly with our Customer Relationship Management Model PowerPoint Presentation Slides. It allows you to attempt the jigsaw.
This presentation provides insight into how to forecast and calculate customer lifetime value (CLV). Here a startup applied a scientific approach to maximise customer retention and minimise churn. The outputs of the analytics were built into the system and business processes driving the success of the company and helping it to win the customer service of the year award, and to achieve a successful exit through acquisition.
This document discusses calculating and analyzing customer lifetime value (CLV). It provides formulas for calculating CLV based on yearly gross contribution per customer, retention rate, discount rate, and retention costs. An example calculation is shown. The document also discusses how retention rate affects CLV and introduces the concepts of net promoter score (NPS) and customer likeability score to categorize customers. It proposes mapping customers on a "grid" based on these metrics to identify the most and least valuable relationships.
- The document describes a project to predict customer churn for a telecom company using classification algorithms. It analyzes a dataset of 3333 customers to identify variables that contribute to churn and builds models using KNN and C4.5.
- The C4.5 model achieved higher accuracy (94.9%) than KNN (87.1%) on the test data. Key variables for predicting churn were found to be day minutes, customer service calls, and international plan.
- The model can help the telecom company prevent churn by focusing retention efforts on at-risk customers identified through these important variables.
This document discusses predicting customer churn in the telecom industry. It outlines collecting customer data from Kaggle on services used, account details, and demographics. Exploratory data analysis finds recent and higher spending customers more likely to churn. Feature selection and encoding is done before imbalanced data handling with SMOTE tomek. Various classifiers are tested with random forest performing best with an AUC of 0.834. Partial dependence plots and SHAP values are used to explain the model. Finally, a web app is created and deployed on Github to predict churn probabilities and help telecom companies reduce customer churn.
The document discusses Amazon's business and technologies. It provides an overview of Amazon as an e-commerce and cloud computing company, discusses its use of Linux, Oracle databases, and secure payment systems. It also describes how Amazon utilizes robots in warehouses, Kiva robots to aid in picking and packing, and its plans for drone delivery via its Prime Air system to deliver packages using vertical take-off drones that can achieve speeds of 100kph and dodge birds.
This document discusses customer churn, which refers to the rate at which customers leave a company. It states that reducing churn by 5% can boost profits by 75% and that US companies lose $1.6 trillion per year to churn. Common causes of churn include poor customer service, price, functionality, and changing customer needs. The document promotes MECBot, a data analytics product that can detect, prevent, and reduce churn in real-time through capabilities like churn scoring, campaign management, and customer lifetime value maximization.
Customer Churn Management For Profit Maximization PowerPoint Presentation SlidesSlideTeam
The PowerPoint template allows firm in preventing its customers from reducing their purchase of products and services. It will help firm by providing various ways through which firm can manage their customer churn. It will cover details about churn propensity model. The template covers details about key statistics associated to customer churn. It covers details about present situation of customer attrition and customer churn rate on monthly basis. Customer churn is considered as critical issue which affect firms overall firm performance, due to which firm will incur heavy losses in terms of abandon purchases and lower revenues. It is to be noted that retaining customer is more profitable than acquiring new customers. The template will cover information regarding various types of customer churn such as when customer stops spending, churn due to product quality or complete customer account loss. The template will provide details about how firm will handle customer attrition by focusing on four stages of churn management by acquiring churned customers, delighting customers, preventing customer attrition, and saving customers through various campaigns. The template covers details about churn propensity model which will help preventing customer churn through predictive analytics by utilizing different statistical techniques such as machine learning. https://bit.ly/36qQZKg
Customer Churn prediction in ECommerce Sector.pdfvirajkhot5
Retaining customers is a challenging issue that is encountering most of organizations, particularly businesses operating in e-commerce sector. According to Wu et al., (2017), it is much more difficult to retain the existing customers as compared to attract new ones because existing customers provides high value in ecommerce; however, to attract new customers, companies need to invest a lot of money for making them as loyal customers. This study will develop a prediction model for E-commerce sector to correlate the key attributes leading to churn.
This document discusses predicting customer lifetime value (CLV) for an auto insurance company using various machine learning models. It outlines preprocessing the data, feature engineering, building models like linear regression, ridge regression, decision trees, random forests, and evaluating the models using R2. Random forest was found to have the best performance with an R2 of 0.913. The conclusions recommend focusing on male customers, specific coverage offers, and tailored sales strategies based on customer demographics to improve CLV predictions.
- Amazon was founded in 1994 by Jeff Bezos and launched online in 1995 as an online bookstore (Paragraph 1)
- It has since grown to be one of the largest online retailers, selling over 20 million products globally through its various websites (Paragraph 5)
- Key to Amazon's success has been its focus on customer value, strong logistics network, and long term vision (Paragraph 4)
The document discusses customer churn risk and how to develop predictive churn models. It defines risk as having two components: uncertainty and exposure to that uncertainty. When building a churn model, the key steps are: defining active vs churned customers, selecting relevant customer data, analyzing characteristics to identify predictors, developing a predictive score using methods like logistic regression, and evaluating the model's ability to identify customers likely to churn. The goal of a churn model is to provide insights for preventing churn, not just statistical precision.
Case study: Amazon improvement of customer serviceSulaman Muhammad
Amazon has improved its customer service by improving inventory management in several ways:
1. It increased the number of warehouses to reduce delivery times and opened warehouses near customers.
2. It offered a wide range of products to improve availability and purchase directly from publishers to shorten times.
3. It used location and manufacturing postponement to centralized inventory and delay customization. It also outsourced inventory of less popular products and used drop shipping to focus on e-commerce while maintaining selection and delivery speed.
4. Amazon automated warehouses using barcodes and conveyor belts to efficiently receive, track, pack and ship high volumes of orders.
UT had the highest difference in average international call minutes between churning and non-churning customers, indicating international call rates were a likely reason for churn. Several other states also showed differences in international minutes, suggesting international plan rates affected churn. The report analyzed call minute differences and built a logistic regression model to predict churn behavior from the telecom usage data.
RFM (Recency, Frequency, Monetary value) analysis measures a customer's recency of purchase, visit frequency, and typical monetary value. It helps marketers identify their best customers, customers at risk of churning, and potential customers. CleverTap's RFM dashboard visualizes customers on a 2D graph by recency and frequency scores. It segments customers into 10 groups including Champions, Potential Loyalists, New Customers, and At-Risk Customers to target personalized campaigns.
SaaS Metrics: The Secret to Subscription SuccessEd Shelley
This document discusses key metrics for Software as a Service (SaaS) businesses including Monthly Recurring Revenue (MRR), churn rate, customer lifetime value (LTV), and others. It defines each metric, how it is calculated, and why it is important to measure. The document advocates measuring these metrics to better understand customer acquisition, retention, and revenue over time in order to improve the business. It provides examples of how leading SaaS companies use these metrics to manage and grow their subscriptions.
How amazon Works. - Supply chain management.PankajWagh26
Amazon started in 1995 selling books from Jeff Bezos' garage. It now sells a huge variety of products online. Amazon uses a hybrid supply chain model, delivering some products itself and using other logistics firms to deliver marketplace products. It has a large data warehouse running on HP servers with Oracle software to store customer and product data. Amazon also prioritizes security, encrypting customer information and credit cards during checkout. It has been successful in expanding product offerings through partnerships with other retailers and sellers.
IRJET - Customer Churn Analysis in Telecom IndustryIRJET Journal
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
The document discusses various strategies for customer retention, including rewarding customer behavior, establishing loyalty programs, using transactional emails, surprising customers, and being active on social media. The goal of these strategies is to encourage customers to repeat purchases and refer others by adding value at every step of the customer journey. The most effective retention strategies stand for a clear value proposition, use social proof from other customers, position their product to solve customer needs, and focus on continually improving the customer experience through easy problem solving and gratitude.
Chapter 4 Creating Customer Value, Satisfaction, and LoyaltyNishant Agrawal
1. Conducting periodic customer satisfaction surveys to understand customer perceptions and gather feedback.
2. Monitoring customer loss or defection rates to identify potential issues.
3. Tracking the performance of competitors to benchmark satisfaction levels.
Creating Customer Value, Satisfaction and Loyalty / Marketing Management BY ...Choudhry Asad
The document discusses key concepts related to customer value, satisfaction, and loyalty. It covers traditional versus modern customer-oriented organizations, determining customer perceived value and its drivers, measuring customer satisfaction, defining loyalty, and frameworks for customer relationship management. Steps for customer value analysis, retention, and managing the customer base are also outlined.
1) Customer relationship management (CRM) is a strategy that places customers at the center of a business to foster mutually beneficial relationships. It requires understanding key CRM concepts and best practices.
2) Calculating a customer's lifetime value allows businesses to segment customers, focus on the most profitable, and tailor offerings. However, each customer desires unique relationship styles so one approach does not fit all.
3) Businesses can increase customer lifetime value by reducing defection rates through personalized service, strengthening relationships, and cross-selling/up-selling additional products. The goal is satisfying customers so they remain loyal and generate future profits.
CCustomer - Methodical and fact-based client managementErik Jonsson
This document describes CCustomer AB and their services for methodical and fact-based client management. CCustomer helps companies maximize revenue by analyzing customer data, understanding customer flows, measuring customer lifetime value, and implementing a four-step process for improvements. Their services include customer analysis, recruitment, and ensuring changes have the desired long-term effects. The goal is to focus marketing efforts on retaining existing customers and attracting the most profitable new customers.
- Customer satisfaction, value, and loyalty are important for companies to deliver in order to maximize lifetime customer value. Companies should understand customer needs and expectations, deliver high quality products and services, and monitor satisfaction over time.
- It is more profitable to attract and retain existing customers than acquire new ones. Companies should measure customer lifetime value and profitability to understand which customers to prioritize. Building loyalty through programs, interactions, and institutional ties can further increase customer value.
Companies that excel at customer acquisition will have:
1. An executive dedicated to managing customer acquisition.
2. A dedicated budget for customer acquisition activities.
3. An understanding of the economics and profitability of acquiring different types of customers.
4. A likelihood of employing CRM technologies to track customers.
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The document discusses building customer satisfaction through delivering value and high quality. It defines customer value and satisfaction, and how perceived value is determined by benefits versus costs. Specific strategies are provided, such as enhancing product attributes, improving quality, reducing prices, and focusing on customer service. Measuring satisfaction through surveys and monitoring complaints is also covered. The goal is to turn customers into loyal, long-term partners by continuously discovering ways to provide more value.
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What's the right way to measure churn? Which customers are most at risk? What can you do about it? Join this session to see how Zuora Insights helps you predict churn, measure churn rate, find subscriber segments most likely to churn, and trigger relevant and timely customer engagement that prevents churn.
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2. Agenda for Customer Attrition Management
2
01 • Company needs to prevent customers from reducing
their purchases of products and services
02
• Discussing about various ways through which
firm can manage the customer churn
03 • Discussing about churn
propensity model
04 • Add text here
3. Table of Content for Customer Attrition Management
3
04 Churn Propensity Model
05
Impact of Successful Implementation
of Churn Management
06 Dashboard
• Customer Purchase Tracking
o Problem Understanding
o Data Collection, Preparation and Pre-processing
o Modelling and Testing
• Various Customer Attributes to Look For
• Preventing Churn through Predictive Analytics
• Machine Learning Process
• Predicting Customer Churn
• Customer Journey Analytics
• Key Statistics Associated to Customer Churn
• Present Churn Situation in Firm
• Current Customers Attrition in Firm
• Customer Churn Rate on Monthly Basis
01 Introduction
02 Various Types of
Customer Churn
03
How to Handle Customer
Attrition?
o Segmentation on Basis of Customer Spending
o Acquisition Strategy for Churned Customer
o Delighting Customers to Improve Loyalty
o How to Prevent Customer Churn?
o Several Anti – Churn Campaigns
• Customer Treatment Framework
• Four Stages of Churn Management
4. Table of Content 1
4
• Key Statistics Associated to Customer Churn
• Present Churn Situation in Firm
• Current Customers Attrition in Firm
• Customer Churn Rate on Monthly Basis
Introduction
5. Key Statistics Associated to Customer Churn
5
Companies are losing $85
billion each year due to
customer churn and
abandoned purchases
Costs 5x more to acquire
new customers than keeping
existing ones
16x more to bring new
customers to the same level
of current ones
5% increase in customer
retention leads to 30% - 90%
increase in profits
90% of firm’s future
revenues derive from 30% of
existing customers
Probability of selling to new
customer – 5 – 15%
Probability of selling to
existing customer – 65 –
75%
03
02
01 05
04
Customer churn is a serious issue which affects firm’s overall performance. Due this, firms incur heavy losses in terms of abandon purchases and lower revenues. Retaining existing
customer is more profitable than acquiring new customers.
6. Present Churn Situation in Firm
6
0
40
80
120
160
200
2015 2016 2017 2018 2019 2020
Millions
Chumers Subscribers
0
20
40
60
2015 2016 2017 2018 2019 2020
Percentage
This slide depicts the current scenario of churners compared to customers over the years. It also provides information regarding annual churn rate of the customers.
Customers and Churners Annual Churn Rate of Customers
Key Takeaways
• There is increase in churners with increase in customer base from 2015 to 2020
• Add text here
Key Takeaways
• There is a rise in customer attrition rate in past years
• Add text here
7. Current Customers Attrition in Firm
7
Current Customers 10,000 14,375 18,531
Number of Churners -500 -719 -927
New Customers 5,000 5,000 5,000
New Churn -125 -125 -125
Total Customers 14,375 18,531 22,479
Adjusted Churn Rate 5.13% 5.13% 5.13%
Quarterly Churn Rate 5.17% 5.17% 5.13%
August September October
This slide provides information about current customers, number of churners with churn rate for last three months.
8. Customer Churn Rate on Monthly Basis
8
0%
2%
4%
6%
8%
10%
Customer Churn
Key Takeaways
• There is steady increase in customer churn from 2018 to 2020
• But in Dec 2019, firm has encountered the highest customer churn rate
• Reasons for high churn rate –
• Poor client experience
• Bad onboarding experience
• Lost value perception
• Add text here
Firm has tracked customer churn rate on monthly basis. It has been seen that firm has faced highest churn rate in last month.
01/07/2018 01/06/2020
10. Various Types of Customer Churn
10
This slide provides information about several ways in which firm can experience customer churn such as – when customer stops spending, churn due to product quality, etc. and the
reasons behind them.
Account Declining in Spending
• Decrease in overall spend with firm
• Purchasing in less volume and ordering less frequently
• Add text here
• Add text here
Product Churn
o Failure in meeting customer expectations
o Failure in providing value to customers as compared to
competitor’s offering
o Degrading in quality and shortening shelf life
• Add text here
• Products become unattractive for various reasons –
Complete Customer Account Loss
o Failure in delivering promises made
o Not finding product useful
o Bad customer service
• Reasons for customer leaving business entirely
• Add text here
• Add text here
11. Table of Content 3
11
How to Handle
Customer Attrition?
o Segmentation on Basis of Customer Spending
o Acquisition Strategy for Churned Customer
o Delighting Customers to Improve Loyalty
o How to Prevent Customer Churn?
o Several Anti – Churn Campaigns
• Customer Treatment Framework
• Four Stages of Churn Management
12. Four Stages of Churn Management
12
The firm is need of managing its customer churn and it will achieve this in four stages such as – acquiring churned customer, delighting customer, preventing customer attrition and
saving customers through various campaigns.
Acquire
Customer segmentation
Acquiring customers through
different strategies
Delight
Delighting customers in order to
increase customer loyalty as
compared to competitors
Prevent
Preventing customers from
attrition through analyzing various
churn signals and triggers
Retain
Focus on saving customers from
leaving the firm through several
campaigns
13. Segmentation on Basis of Customer Spending
13
From To
$0.00 $25.00 Low
$25.1 $50.00 Medium
$50.1 $75.00 High
$75.01 $100.00 Silver
$100.01 $150.00 Gold
#150.1 $1,000.00 Platinum
Customer
Spending Range
Customer
Spending Range
The firm will bifurcate its customers based on their spending pattern.
14. Acquisition Strategy for Churned Customer
14
0
2
4
6
Average lifetime indexed to deal seekers Average CLV indexed to deal seekers
0%
20%
40%
60%
80%
100%
Total market Acquisition market
Deal seekers
Tariff optimizers
Apathetics
Loyalists
Lifetime Customer Lifetime Value Availability in % of market
The firm is looking for acquiring its churned customer as it is costlier in acquiring new customers than retaining the existing ones. The customers will be categorized into segments such
as deal seekers, tariff optimizers, etc. and strategies will be implemented according to the categorization.
Deal Seekers Short term promotions Quarterly
Tariff Optimizers Add text here Add text here
Apathetics Add text here Add text here
Loyalists Increasing benefits over time-Providing free and easy to access to cloud storage Add text here
Category Frequency
Category
Customer Segment Mix of segment per universe
Altering mix of acquisition –
15. Delighting Customers to Improve Loyalty
15
Improved Customer Interactions
• Loyalty program
• Interactable and intuitive system
• Customized email responses
• Add text here
Enhanced customer engagement Add text here
Treating Customers
• Small gifts
• Thanking customers through
handwritten notes on invoices
• Add text here
Loyalty building Add text here
Follow Up
• Sale follow up
• Satisfaction/ complaint feedback
survey Add text here
Enhanced customer service experience Add text here
Parameter Comments
Strategies Benefit
The firm is looking for different parameters in order to delight its customers. This slide portrays various strategies that firm will implement to enhance customer loyalty and the benefits that
it will incur.
16. How to Prevent Customer Churn
16
Pending or unresolved incident No action taken on complaint call
Increased complaints
4 - 5 complaints related to
• Invoice
• Orders
• Product quality
End of commitment
Customer inquires about the commitment period duration
and status
Your text here
Communicating with competitors
Interacting with competitor’s tele sales department thrice in
last two months
Churn Signals
Preventive
Measures
Description
Churn
Triggers
Description
Preventive
Measures
This slide depicts information about how firm can prevent the customer churn. The firm can proactively handle the churn issue by detecting the churn signals and triggers from
customers.
17. Several Anti - Churn Campaigns
17
This slide portrays information about various marketing campaigns in order to retain its customers from churning.
Cross Sell and
upsell Campaigns
Building post sales opportunities through
• Additions or improvements based on previous purchases
• Add text here
Product and
Service Improvement
• Making customers aware about new features and optimizations
through email, webinar
• Add text here
• Tracking insights regarding losing
customers
• Add text here
Customer
Satisfaction Campaigns
After
Sales Engagement
• Requesting feedback and offering support
• Add text here
18. Customer Treatment Framework
18
The firm will target its customers by testing various strategies in terms of offers and will check their performance through feedback.
Testing
multiple strategies
Offer 1
Offer 2
Offer 3
Update modes
based on
response data
Feedback Loop
Tracking customer
responses on various offers
Offer 1
Offer 2
Offer 4
Update modes
based on
response data
Feedback Loop
Campaign refining on basis
of customer response
Offer 5
Offer 2
Offer 6
Likely Churner
19. Table of Content 4
19
Churn Propensity
Model
o Problem Understanding
o Data Collection, Preparation and Pre-processing
o Modelling and Testing
• Various Customer Attributes to Look For
• Preventing Churn through Predictive Analytics
• Machine Learning Process
• Predicting Customer Churn
• Customer Journey Analytics
20. Various Customer Attributes to Look For
20
Data Collection Through
Campaign History
• Customer information collected from
different email campaigns types –
promotions, discounts
• Add text here
Customer
Demographic Data
• Gender
• Age
• Income
• Education level
• Socio economic characteristics
• Add text here
Customer Behavior
Data
• Subscription date
• Customer spending per visit
• Subscription history
• Page views
• Brand preferences
• Add text here
The firm will explore various features from data. The available data is analyzed per user basis.
21. • Algorithms provide information
regrading shared behavioral
pattern of customers who
already abandon purchasing
• Check behavior of current
customers against the patterns
and alert about potential
churners
• Get informed about the
customer’s pain points and
choosing effective retention
actions
Preventing Churn through Predictive Analytics
21
Projecting the probability of future outcomes, events based on current and historical data. It utilizes different statistical techniques such as machine learning, pattern recognition through
data mining, etc.
Machine Learning
Churned Customer
History
Pattern
Recognition
Proactive re-
Engagement
Predictive
Model
Customers at risk
of Churn
Happy
Customers
Existing
Customers
22. Machine Learning Process – Problem Understanding
22
This graph/ chart is linked to excel and changes automatically based on data. Just left click on it and select “Edit Data”.
The customer churn prediction through machine learning is initiated through understanding the insights through two methods – classification and regression.
0%
10%
20%
30%
40%
50%
60%
70%
80%
-1 0 1 2 3 4 5 6 7
Churn
Profitability
# Customer Service Calls
Problem Understanding
Two types of problems to solve
Regression
• Probability estimate of churn per customer –
• Estimate about customers that are likely to get churn in a specific time
period
Classification
o Will customer churn or not
o Will customer renew its subscription
• Data scientists categorize customers in different class/category
(churner/non churner)
• Categorizing based on pricing plan downgrading
23. Machine Learning Process-Data Collection, Preparation and Preprocessing
23
Once the insights are identified, data collection sources are selected for further predictive modeling. After that, dataset is prepared with a set of attributes representing behavior patterns
related to customer engagement level for specific product/service.
Data Collection
Recognizing various sources of data for churn prediction –
• CRM systems –customer information from sales and customer support records
• Feedbacks on review and social media platforms
• Add text here
Data Preparation and Preprocessing
Recognizing various sources of data for churn prediction –
o Customer demographic features – education, level, income
o User behavior feature – lifecycle stage, active session length
o Support features – sent queries, customer satisfaction score
• Feature engineering – Predicting outcomes by selecting most relevant attributes depicting different
behavior patterns related to customer engagement level with product/service. Features can be –
24. Machine Learning Process – Modelling and Testing
24
Root node
Interior node Interior node
Leaf node Interior node Interior node
Leaf node Leaf node Leaf node
Leaf node Leaf node
Decision Tree
• Data sample splits into two or more homogeneous sets with most significant
differentiator in input variables
• Tree initiated from root node – best predictor
• Add text here
• Add text here
Decision Tree
Tree1
Voting
P1
P2
Pm
Treem
Tree2
Predicted Class
A
• High prediction accuracy with numerous decision trees
• Each tree classifies data instance based on attributes
• Forest selects classification with maximum votes
• Add text here
• Add text here
Random Forest
The firm can consider in between two churn prediction model – decision tree and random forest. These models are capable in detecting potential churners with desired level of accuracy.
25. Predicting Customer Churn
25
Customer 1 Medium 201.4 0.112 Yes Add text here
Customer 2 Low 153.2 .265 No Add text here
Customer 3 High 127.5 .741 Yes Add text here
Customer 4 Low 161.3 .878 Yes Add text here
Customer 5 Low 254.4 .281 No Add text here
Customer 6 Add text here Add text here Add text here Add text here Add text here
After deploying churn prediction model, the firm will become capable in predicting churn probability of various customers on basis of various attributes such as NPS, transaction usage, etc.
Customer Transaction Usage NPS Churn Probability Churn Prediction Comments
26. Customer Journey Analytics (1/2)
26
7K 21.7K 332 29K
Return in Store
7.1K 21.7K 313 29.2K
Coupon Spent
6.9K 21.5K 340 28.7K
Reward Sent
7.4K 22.1K 251 29.7K
Web Searched
7.6K 11K 11.3K 29.9K
Product Searched
Key Takeaways
• Marketing team will keep track of crucial customer touchpoints which will deliver more value,
affect future interactions and will prove to be fruitful in driving new and repeatable clients
• The analysis will help in uncovering crucial steps that will drive more value
• Add text here
• Add text here
The firm can also measure churn rate using customer journey analytics tools by mapping entire customer journey by analyzing various touchpoint encountered.
7.6K 11K 18.6K
Product Compared
7.6K 7.6K
Cart Abandoned
27. Customer Journey Analytics(2/2)
27
Key Takeaways
• Marketing team will keep track of crucial customer touchpoints
which will deliver more value, affect future interactions and will
prove to be fruitful in driving new and repeatable clients
• The analysis will help in uncovering crucial steps that will drive more
value
• Add text here
• Add text here
The firm can also measure churn rate using customer journey analytics tools by mapping entire customer journey by analyzing various touchpoint
encountered.
46.6
K
Signed Up for
Online Banking
11K
Downloaded
Mobile App
2.9K
Open Cd
46.6
K
Tie 1
581K
120-day Customer
Satisfaction
1.9K
Account
Closed
256K
Account
Closed 1
Call made to
customer Support
2.9K
22.3K
Enrolled in
Direct Deposited
1.6K
Apply for Loan
28. Table of Content 5
28
Impact of Successful
Implementation of Churn
Management
29. Impact of Successful Implementation of Churn Management
29
Benefit Description Annual $ Impact
Decreased churn
Revenues increased
Higher cross - selling
revenues
Customer retention rate
Revenues generated
Add text here
Add text here
$3.3M
$0.4M
65% increase
$4 - $5 M
Add text here
Add text here
Benefit Category
Reduce Churn and Drive
Upsell Revenue
Enhanced
Customer Engagement
Improved
Customer Retention
This slide portrays the overall impact of churn prevention and benefits associated to it.
31. Dashboard – Customer Purchase Tracking (1/2)
31
The firm is looking forward to track customer experience by tracking various touchpoints in order to handle issues such customer churn. KPIs mentioned in the dashboard are measured
regularly across the organization.
Repeat Purchase
Path to Purchase
0
20
40
60
Customer activity
0
0.2
0.4
0.6
0.8
Reward conversion per group
-0.03
0.17
0.37
0.57
0.77
Coupon conversion rate per group
Summary
Participants
11.6K
Total repeat sales after
coupon
8.35
K
Total repeat sales
after reward
29.2
K
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Landing page per group test
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
Product viewed per category
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Add cart/ product view
0
5000
10000
15000
Oct-19
Nov-19
Dec-19
Jan-20
Feb-20
Mar-20
Total cart value per month
Summary
Participants
29.8K
Add to cart by product
category
34.3
K
Sales Q1 2020
0
5000
10000
15000
Oct-19
Nov-19
Dec-19
Jan-20
Feb-20
Mar-20
Total cart value per month
0
0.2
0.4
0.6
0.8
1
Reward conversion per group
0
20
40
60
Female Music
New chart
-25
75
175
275
375
475
Sep-20 Oct-20 Nov-20 Dec-20
Checkout by month
Summary
Participants
9.1K
Total revenues by
category
14.5
K
32. Dashboard - Customer Purchase Tracking (2/2)
32
The firm will track down the customers activities in terms of revenues generated. Customers are bifurcated in segments such as existing, new, lost and active.
Existin…
New,
624
Active,…
Churned
, 1,669
Customers 6419 1.5% 624 14.8% 1869 49.9% 5174 12.3%
Revenue $425k 0.9% $31k 9.7% $139k 66.5% $317k 15.1%
Avg Bill $66.17 0.7% $50.33 18.4% $74.44 27.9% $61.27 27.6%
Existing New Lost Active
35. 30 60 90 Days Plan
35
Days
Plan
30
Days
60
Days
90
Days
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36. Weekly Timeline with Task Name
36
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
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37. Our Mission
37
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Mission
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Vision
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Goal
38. Designation
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Our Team
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39. Roadmap for Process Flow
39
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04
40. About Us
40
Creative
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Professional
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Talented
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41. Comparison
41
WhatsApp Users
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50%
Twitter Users
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65%
Facebook Users
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80%
42. Financial
42
Minimum
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Medium
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Maximum
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35
60
90
43. Timeline
43
START FINISH
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2016
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2017
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2020