Customer Life Time Value Analysis
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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.
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Predict Customer Lifetime Value Presentation
This document discusses customer lifetime value (CLV), which is the net present value of future cash flows from a customer. It notes that not all customers are equally profitable and that customer retention can be cheaper than acquisition. Common CLV modeling approaches discussed include RFM analysis, the Pareto/BG-NBD model, and random forest models using features like customer engagement levels. Case studies on Groupon and ASOS are provided that used random forests and neural networks to predict churn and CLV. While neural networks can improve predictions, random forests often provide good results at a lower cost.
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3. Companies can use CLV to inform customer acquisition and relationship management strategies, such as spending more to acquire or retain the most valuable customers.
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This document discusses customer lifetime value (CLV) and how to optimize marketing strategies around it. It covers:
1. Choosing the right key performance indicators (KPIs) to focus on like CPA, ROAS, profit, or CLV.
2. An overview of customer centricity and how CLV is the "Holy Grail" metric for understanding customer strategies and analytics.
3. How to take action and put the chosen KPI into practice through campaign reporting, optimization, and ensuring the right data integration and technology platforms.
4. A short introduction to marketing experiments for proving the effectiveness and value of marketing efforts through controlled experimentation.
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The document discusses customer lifetime value (CLV), including:
1. CLV is the predicted net profit attributed to the entire future relationship with a customer.
2. CLV is an important metric because the best customers account for the majority of sales, and retaining existing customers is often cheaper than acquiring new ones.
3. Companies can use CLV to inform customer acquisition and relationship management strategies, such as spending more to acquire or retain the most valuable customers.
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MIS637_Final_Project_Rahul_Bhatia
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- 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.
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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.
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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.
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The document presents a case study for a lead scoring model built to predict potential customer conversions for an education company. Data on past leads was analyzed to identify key variables impacting conversion rate. A logistic regression model was developed and evaluated on train and test data, achieving 78% accuracy. The model can assign a lead score between 0-100 to help the company prioritize hot leads most likely to convert.
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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.
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This presentation discusses customer lifetime value (CLV) modeling. CLV is defined as the net profit attributed to the entire future relationship with a customer. It is typically used in business-to-consumer contexts but can also apply to business-to-business. CLV must be greater than customer acquisition costs. The presentation outlines different levels of sophistication in CLV prediction models and lists key inputs like contribution margin, churn rate, retention cost, and period. It provides a simple e-commerce example and discusses how churn rate strongly impacts CLV for subscription businesses. Standard and more accurate CLV calculation formulas are also shown.
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The document discusses quality management and planning. It describes the Plan-Do-Study-Act cycle for continuous improvement. The seven tools of quality control are explained, including cause-and-effect diagrams, flowcharts, control charts, scatter diagrams, Pareto analysis, and histograms. Quality Function Deployment is discussed as a tool to translate customer requirements into technical specifications. The concepts of reliability, process management, ISO standards, and reasons for TQM failures are summarized. Statistical sampling, standard deviation, Six Sigma methodology, and how Six Sigma differs from traditional quality control approaches are briefly outlined.
Top Recurring Revenue Metrics
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quality and performance
Based on the additional data provided:
- The average range (R) is 0.45
- The average (x) is 8.034
- The sample size (n) is 8
= 1.864(0.45) = 0.838
LCLR = D3R = 0.136(0.45) = 0.061
All sample ranges fall within the control limits on the R-chart, so the process variability is in statistical control.
For the x-chart:
UCLx = x + A2R = 8.034 + 2.282(0.45) = 8.234
LCLx = x - A2R = 8.
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Six Sigma-s04.ppt
Motorola launched Six Sigma in the 1980s to focus on eliminating defects through reducing process variation. General Electric widely adopted it in the 1990s. Six Sigma utilizes a DMAIC process - Define, Measure, Analyze, Improve, Control - to systematically identify and remove sources of defects. It aims for near-perfect process performance, defined as 3.4 defects per million opportunities. Key metrics include Defects Per Unit, Defects Per Million Opportunities, and Process Capability Indices. Organizations structure teams led by Master Black Belts, Black Belts and Green Belts to deploy Six Sigma projects.
Six Sigma-s04.ppt
Motorola launched Six Sigma in the 1980s to focus on eliminating defects through reducing process variation. General Electric widely adopted it in the 1990s. Six Sigma utilizes a DMAIC process - Define, Measure, Analyze, Improve, Control - to systematically identify and remove sources of defects. It aims for near-perfect process performance, defined as 3.4 defects per million opportunities. Key metrics include Defects Per Unit, Defects Per Million Opportunities, and Process Capability Indices. Organizations structure teams led by Master Black Belts, Black Belts and Green Belts to deploy Six Sigma projects.
six sigma-s04.ppt
Motorola launched Six Sigma in the 1980s to reduce defects through statistical analysis of processes. General Electric widely adopted it in the 1990s. Six Sigma aims to reduce variation and defects through training employees in statistical process analysis and improvement methods. It uses the "DMAIC" structure of Define, Measure, Analyze, Improve, and Control to systematically solve problems and track processes. The goal is to reduce defects to 3.4 per million opportunities.
Six Sigma- Define & Measure_Sudhanshu.pdf
1) Six Sigma is a data-driven approach to process improvement that aims to reduce defects to 3.4 parts per million. It involves defining customer needs, measuring processes, analyzing data for improvement opportunities, and controlling processes.
2) Quality is defined as meeting or exceeding customer requirements. Total quality cost includes prevention, appraisal, internal failure, and external failure costs. Understanding customer needs through methods like the KANO model is important for defining critical quality characteristics.
3) Measurement and statistics are essential for understanding process capability. Descriptive statistics summarize data while inferential statistics allow estimating population parameters from samples. Tools like histograms, box plots, and statistical parameters
Cvm Dqg Final1
The document discusses customer value management (CVM) concepts and provides a case study of its application. It defines key CVM terms like value, price, and cost. It describes how CVM can be used as a strategic tool to drive business by focusing on delivering the highest value to customers relative to competitors. The case study details how AT&T shifted to a CVM approach in the 1990s to improve its competitive position and market share.
Transforming Procurement in a Financial Services Organisation Undergoing Sign...
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The document discusses the significant transformation of the procurement function at a large financial services organization to create a more effective and resilient operating model focused on supplier collaboration, risk management, and digital capabilities. Key changes included restructuring the organization, roles, processes, and systems to deliver improved value and savings while enhancing supplier governance and risk management. Metrics show the transformation is on track to deliver expected cost savings and improve employee engagement and satisfaction through changes to the operating model, technology landscape, and focus on value, risk, and digitalization.Similar to Customer Life Time Value Analysis (20)
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Customer Life Time Value Analysis
- 1. CUSTOMER LIFE TIME VALUE (CLV) PREDICTIONS Auto Insurance Company Made By Soumit kar 1
- 2. V Slide 1 Problem Statement and Business Overview Data Pre Processing and Feature Engineering Model Building R2 Evaluation Data Insights Conclusion and Recommendations AGENDA Made By Soumit Kar 1 2
- 3. Objective Company's Approach and Insight We are expected to create an analytical and modelling framework to predict the lifetime value of each customer based on the quantitative and qualitative features. • A major non-life insurance company wants to evaluate customer lifetime value based on each customer’s demographics and policy information including claim details. • The CLV is a profitability metric in terms of a value placed by the company on each customer and can be conceived in two dimensions: the customer`s present Value and potential future Value. Made By Soumit Kar 3 OVER VIEW
- 4. Made By Soumit Kar 4 BUSINESS STRATEGIES
- 5. Made By Soumit Kar 5 PROFIT PROFIT TIME More Efficient acquisition of new customers Better Cross / Up selling More Efficient Customer Retention Recovery of Potential Valuable Customers Faster Termination of Less Valuable Customer Acquisition Retention Termination CUSTOMER LIFE CYCLE
- 6. . Made By soumit Kar 6 PROFIT PROFIT TIME More Efficient acquisition of new customers Better Cross / Up selling More Efficient Customer Retention Recovery of Potential Valuable Customers Faster Termination of Less Valuable Customer Acquisition Retention Termination CLV HELPS TO ANSWER THIS…… Do the new customer has same profile as valuable customer? Do the Customer have high probability of cross- selling? Do the Customer have high potential? Do the Customer have low potential?
- 7. 7 1. Parameters Used ● Business centric parameter ● Customer centric Parameter ● Demographic Parameter 2. Combine sets used for Top 5 and Bottom 5 Customers. 3. Information and navigation button. 4. Histogram for all variables in Tooltips. Components Used in Dashboard
- 10. 10 Policy Age segment= Months Since Policy Inception > median, high else Low CLV segment = CLV > median, High else Low CS=High PAS=High CS = Low PAS= Low CS = High PAS=Low CS = Low PAS= High
- 11. DATA PRE-PROCESSING AND FEATURE ENGINEERING Data Pre- Processing Dataset was given with 9134 data points and 24 variables. There is no missing value Drop columns those add no significance. A new Features are been extracted based on the existing variables. New Features are: Present Value of Customer, Clubbing the Various Levels into one level. Applied one hot encoding for categorical columns See the correlation features. Using the VIF and statistical analysis some of the variables are been removed. Feature Engineering Variables Extraction Made By Soumit Kar . 11
- 12. FEATURE ENGINEERING . Made By Soumit Kar 12 Present Value of Customer = Monthly Premium Amount Months since policy Inception Total Amount Claim * - Bachelor College High School or Below Doctor Master
- 13. DEMO GRAPHS OF THE FINAL DATA FRAME . Made By Soumit Kar 13 Customer Centric Business Centric Customer Demo+ graphs Income Sales Channel Education Employment status Monthly Premium Value Marital Status No. of Policies Policy & Offers Location Vehicle Class Coverage Gender Initial Data Frame with 9134 data points and 24 feature variables Dataset Summary: Total Observations : 9134 Total variables : 24 Split : 70-30 Target Variable : Customer Life Time Value Scaling : Based on the model Evaluation Metric : R2
- 14. 14 Statistical Analysis Proceed with non parametric tests since the dependent variable is not normally distributed Shapiro–Wilk test Mann-Whitney U test Kruskal-Wallis H-test Assumption No Auto correlation = Durbin- Watson Test Normality of Residuals = Jarque Bera test Linearity of residuals = rainbow test Homoscedasticity Test = Goldfeld Test No Multi colinearity = VIF
- 15. MODEL BUILDING Made By Soumit Kar . 15
- 16. 16 Models R^2 Linear Regression 0.25484160264997946 Ridge Regression 0.2148084953609083 Decision Tree 0.840787974934538 Random Forest 0.9139544276201418 Hyperparameter Random Forest 0.9123540914308492 Hyperparameter Adaboost 0.8945253809639656
- 18. CONCLUSION AND RECOMMENDATIONS ● As the Customer who is using Four Door Car and Two Door car more inclined to coverages and even the Loss incurred by them is more. ● Focus on male customers and promote offer 3 and offer 4. ● Even Group 3 Can be Bought in to Group 4 by giving offers to them and even new attracting policies. ● Apply better sales strategy for Doctor’s and master’s students as they are busy ● Concluding that Random Forest Model is stable in order to predict the CLTV. . Made By Soumit Kar 18
- 19. THANK YOU Made By Soumit Kar . 19