This quick start guide provides an Enterprise Guide project that categorizes customers into a predefined number of ‘segments’ based on the score from the RFM analysis as well as:
Introduction to the RFM model
Data Requirements
SAS project configuration considerations
Model Description
Workflow Overview and Build
RFM stands for Recency, Frequency, and Monetary value, each corresponding to some key customer trait. These RFM metrics are important indicators of a customer’s behavior because frequency and monetary value affects a customer’s lifetime value, and recency affects retention, a measure of engagement.
RFM analysis helps marketers find answers to the following questions:
Who are your best customers?
Which of your customers could contribute to your churn rate?
Who has the potential to become valuable customers?
Which of your customers can be retained?
Which of your customers are most likely to respond to engagement campaigns?
Let's find out more with the help of Slideshare.
RFM Segmentation is the easiest and most frequently used form of database segmentation. It is based on three key metrics: Recency, Frequency and Monetary Value of customer activity. RFM is often used with transactional history in e-commerce, but can also work for Social Media interactions, online gaming or discussion boards. Based on calculated segments a marketer can prepare cross-sell, up-sell, retention and reactivation capampaigns. This deck provides a simple introduction to the RFM Segmentation methodology.
The Purpose is to optimize the lead scoring mechanism based on their fit,demographics,behaviors,buying tendency etc. By implementing explicit & Implicit lead scoring modelling with lead point system.
RFM stands for Recency, Frequency, and Monetary value, each corresponding to some key customer trait. These RFM metrics are important indicators of a customer’s behavior because frequency and monetary value affects a customer’s lifetime value, and recency affects retention, a measure of engagement.
RFM analysis helps marketers find answers to the following questions:
Who are your best customers?
Which of your customers could contribute to your churn rate?
Who has the potential to become valuable customers?
Which of your customers can be retained?
Which of your customers are most likely to respond to engagement campaigns?
Let's find out more with the help of Slideshare.
RFM Segmentation is the easiest and most frequently used form of database segmentation. It is based on three key metrics: Recency, Frequency and Monetary Value of customer activity. RFM is often used with transactional history in e-commerce, but can also work for Social Media interactions, online gaming or discussion boards. Based on calculated segments a marketer can prepare cross-sell, up-sell, retention and reactivation capampaigns. This deck provides a simple introduction to the RFM Segmentation methodology.
The Purpose is to optimize the lead scoring mechanism based on their fit,demographics,behaviors,buying tendency etc. By implementing explicit & Implicit lead scoring modelling with lead point system.
RFM Model
First, customers are divided into 5 equal sized groups (20% in each group)
Customers are then given an R, F, & M score
Using a score of 1 to 5, 20% of the most recent customers get an R score of 1.
The second most recent get an R score of 2 and this continues until all 5 groups receive a score.
The 5 groups are reorganized to repeat the procedure for the F & M scores.
(see spreadsheet – Supplier Rankings)
Customer churn has become a big issue in many banks because it costs a lot more to acquire a new customer than retaining existing ones. With the use of a customer churn prediction model possible churners in a bank can be identified, and as a result the bank can take some action to prevent them from leaving. In order to set up such a model in a bank in Iceland few things have to be considered. How a churner in a bank is defined, and which variables and methods to use. We propose that a churner for that Icelandic bank should be defined as a customer who has not been active for the last three months based on the bank definition of an active customer. Behavioral and demographic variables should be used as an input for the model, and either decision tree or logistic regression used as a technique.
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.
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.
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
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IE Business School
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Melody Ucros
Jina Kim
Andrea Blasioli
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Fergus Buckey
Alex Kyalo
Louis Rampignon
Data Source: http://archive.ics.uci.edu/ml/datasets/online+retail
A presentation from the 2014 National Postal Forum by Gary Seitz, Executive VP of C.TRAC, on Recency, Frequency, and Monetary (RFM) analysis as a simple tool to help mailers.
RFM Model
First, customers are divided into 5 equal sized groups (20% in each group)
Customers are then given an R, F, & M score
Using a score of 1 to 5, 20% of the most recent customers get an R score of 1.
The second most recent get an R score of 2 and this continues until all 5 groups receive a score.
The 5 groups are reorganized to repeat the procedure for the F & M scores.
(see spreadsheet – Supplier Rankings)
Customer churn has become a big issue in many banks because it costs a lot more to acquire a new customer than retaining existing ones. With the use of a customer churn prediction model possible churners in a bank can be identified, and as a result the bank can take some action to prevent them from leaving. In order to set up such a model in a bank in Iceland few things have to be considered. How a churner in a bank is defined, and which variables and methods to use. We propose that a churner for that Icelandic bank should be defined as a customer who has not been active for the last three months based on the bank definition of an active customer. Behavioral and demographic variables should be used as an input for the model, and either decision tree or logistic regression used as a technique.
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.
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.
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
Customer Segmentation for Retention StrategyMelody Ucros
IE Business School
Marketing Intelligence Project by Group F:
Melody Ucros
Jina Kim
Andrea Blasioli
Adedeji Rodemade
Fergus Buckey
Alex Kyalo
Louis Rampignon
Data Source: http://archive.ics.uci.edu/ml/datasets/online+retail
A presentation from the 2014 National Postal Forum by Gary Seitz, Executive VP of C.TRAC, on Recency, Frequency, and Monetary (RFM) analysis as a simple tool to help mailers.
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It consists of the interrelated component modules-FI (Financials) and CO (Controlling) with an
extensive set of sub-modules that cover every aspect of the financial and managerial accounting
for both external and internal reporting. Skilled FICO Technical and Functional Consultants are in
very high demand as enterprises world over have been moving to SAP as a single solution for their
business needs. As financial management forms the very basis for any business, SAP FICO has seen
a consistent increase in its implementation with a very high demand for FICO professionals. Garuda
Trainings has come out with a comprehensive online training course in SAP FICO to give our students
the much needed advantage in this highly competitive and sought after segment of the ERP industry.
Online SAP FICO Course Contents: FICO is an integration of two modules FI and CO and the below
curriculum has been segmented accordingly.
Contents:
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2) The FI module and its architecture
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4) Fixed Assets sub-module
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6) The Accounts Payable sub-module
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8) The CO module and its architecture, interaction with the FI and other SAP modules
9) Cost centres and profit centres accounting
10) Internal orders and profitability
11) Product costing and activity based costing
For More Info: http://garudatrainings.com/sap-fico-online-training/
Why Choose Us: Our online course in SAP FICO gives you a perfect flexibility of pursing the
course within your existing schedule as you can opt from weekend or weekday batches as per
your convenience. The training resources are prepared by experts with rich experience in SAP
implementations. All modules are led by trainers and are interactive with a recording ability for
future use and access. We offer a 24/7 access to training resources and technical support and give
you a perfect quality training course with an extra emphasis on practical exposure to real-time
implementation scenarios and live projects.
The Complete Advantage: We endeavour to give you a perfect career as a SAP FICO consultant and
our online training course also includes the advantage of placement assistance through our industry
network. To help our users easily clear the interview evaluation, we offer an extensive collection of
in-depth interview questionnaires along with tips of effectively writing resumes. Choose us to get
the perfect advantage in your career as an SAP FICO consultant.
Register For Free Demo:
www.Garudatrainings.com
Email Us:garudatrainings@gmail.com
Ph No:+1-508-841-6144
Sap Tips and Tricks Training for End userArghadip Kar
Sap Tips and Tricks Training for End user
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Understand preventive measures to avoid releasing Transports with erroneous code.
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Unleash the motivational power of incentive compensation.
CRMIT Solutions provides a complete set of services, from transition through go live, to post go-live. This also includes modelling, design, administration and analysis of incentive compensation programs ranging from hundreds to thousands of direct or indirect sales professionals.
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ESPL India - A offshore software solution provider company with more than 10 years of experience dealing with all types of Software Solution.
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bhawesh.jha@esplindia.com
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