This document discusses models for predicting customer lifetime value that incorporate regularity in customer purchase patterns. It introduces the Pareto/GGG and (M)BG/CNBD-k models, which extend existing models by allowing for heterogeneity in customer purchase regularity. A simulation study shows the Pareto/GGG model improves predictive accuracy when customers exhibit regular patterns. Empirical analysis of various datasets finds purchase regularity varies both across and within categories, and accounting for regularity leads to different predictions of next purchase timing. The document advocates leveraging regularity to improve predictions of customer lifetime value.
Prediction of customer propensity to churn - Telecom IndustryPranov Mishra
The aim of this project is to help a telecom company with insights on customer behavior that would be useful for retention of customers. The specific goals expected to be achieved are given below
1. Identification of the top variables driving likelihood of churn
2. Build a predictive model to identify customers who have highest probability to terminate services with the company.
3. Build a lift chart for optimization of efforts by targeting most of the potential churns with least contact efforts. Here with 30% of the total customer pool, the model accurately provides 33% of total potential churn candidates.
Models tried to arrive at the best are
1. Simple Models like Logistic Regression & Discriminant Analysis with different thresholds for classification
2. Random Forest after balancing the dataset using Synthetic Minority Oversampling Technique (SMOTE)
3. Ensemble of five individual models and predicting the output by averaging the individual output probabilities
4. Xgboost algorithm
In this project, I tried to explain what is Customer Lİfe Time Value and how to calculate it using Probabilistic Models like BG/NBD model and Gamma Gamma submodel.
Also I tried to explain the relation between these models and distributions like Poisson, Gamma, Geometric and Beta.
Today, photography has reached a level where basic digital cameras are being replaced by high end Digital SLR cameras. The experiment designed and executed will help the aspiring photographers and the general population that use these cameras, by giving them a brief idea & description about the various parameters that come under consideration while clicking photographs and also an overview of the outcomes.
Customer churn prediction for telecom data set.Kuldeep Mahani
Customer churn prediction and relevant recommendations as per DSN telecom data analysis. Random forest and logistic regression were applied to predict customer churn.
Prediction of customer propensity to churn - Telecom IndustryPranov Mishra
The aim of this project is to help a telecom company with insights on customer behavior that would be useful for retention of customers. The specific goals expected to be achieved are given below
1. Identification of the top variables driving likelihood of churn
2. Build a predictive model to identify customers who have highest probability to terminate services with the company.
3. Build a lift chart for optimization of efforts by targeting most of the potential churns with least contact efforts. Here with 30% of the total customer pool, the model accurately provides 33% of total potential churn candidates.
Models tried to arrive at the best are
1. Simple Models like Logistic Regression & Discriminant Analysis with different thresholds for classification
2. Random Forest after balancing the dataset using Synthetic Minority Oversampling Technique (SMOTE)
3. Ensemble of five individual models and predicting the output by averaging the individual output probabilities
4. Xgboost algorithm
In this project, I tried to explain what is Customer Lİfe Time Value and how to calculate it using Probabilistic Models like BG/NBD model and Gamma Gamma submodel.
Also I tried to explain the relation between these models and distributions like Poisson, Gamma, Geometric and Beta.
Today, photography has reached a level where basic digital cameras are being replaced by high end Digital SLR cameras. The experiment designed and executed will help the aspiring photographers and the general population that use these cameras, by giving them a brief idea & description about the various parameters that come under consideration while clicking photographs and also an overview of the outcomes.
Customer churn prediction for telecom data set.Kuldeep Mahani
Customer churn prediction and relevant recommendations as per DSN telecom data analysis. Random forest and logistic regression were applied to predict customer churn.
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
Feature engineering--the underdog of machine learning. This deck provides an overview of feature generation methods for text, image, audio, feature cleaning and transformation methods, how well they work and why.
Omni-Channel Retailing: A Strategy for Retailers to Thrive in the Covid-19 Pa...Ken Kwong-Kay Wong
The COVID-19 pandemic is rewriting the rules of retail. A growing number of shoppers now rely on same-day delivery, curbside pickup, in-home/in-car delivery, and AI-powered drive-thru to get their goods. To accommodate such a paradigm shift, retailers must undertake significant changes in their business models to become digitally enabled and data-driven.
Omni-Channel Retailing is written to help retailers and retail students understand the importance of delivering a seamless, cohesive, and contextual customer experience throughout the shopping journey. This book addresses today's retailers' challenges and gives new ideas for implementation. Relevant activities and discussion topics are included to help readers master the concepts.
Scalable Detection of Concept Drifts on Data Streams with Parallel Adaptive W...Jonas Traub
Paper: Scalable Detection of Concept Drifts on Data Streams
with Parallel Adaptive Windowing
Abstract: Machine learning techniques for data stream analysis suffer from concept drifts such as changed user preferences, varying weather conditions, or economic changes. These concept drifts cause wrong predictions and lead to incorrect business decisions. Concept drift detection methods such as adaptive windowing (Adwin) allow for adapting to concept drifts on the fly.
In this paper, we examine Adwin in detail and point out its throughput bottlenecks. We then introduce several parallelization alternatives to address these bottlenecks. Our optimizations lead
to a speedup of two orders of magnitude over the original Adwin implementation. Thus, we explore parallel adaptive windowing to provide scalable concept detection for high-velocity data streams with millions of tuples per second.
The report show research findings of existing sales and distribution practices of a few leading food companies and discusses ways to improve the sales through revision in sales and distribution policies
Beyond Churn Prediction : An Introduction to uplift modelingPierre Gutierrez
These slides are from a talk I at the papis conference in Boston in 2016. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
I tried to bridge the gap between causal inference theory and uplift theory, especially concerning how to properly cross validate the results. The notation used is the one from uplift modelling.
Uplift Modelling as a Tool for Making Causal Inferences at Shopify - Mojan HamedRising Media Ltd.
For many businesses, it is not enough to model the probability of an outcome but rather, “given a predictive model, what can we do to change the probability of this outcome?” The goal of this talk is to present how uplift modelling is used to make causal inferences that guide acquisition strategy at Shopify. Mojan will walk through a case study focused on the statistics and experimental design behind uplift modelling, in addition to the learnings gained from bringing this model to production. The python implementation of this presentation will be made available to attendees.
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
Feature engineering--the underdog of machine learning. This deck provides an overview of feature generation methods for text, image, audio, feature cleaning and transformation methods, how well they work and why.
Omni-Channel Retailing: A Strategy for Retailers to Thrive in the Covid-19 Pa...Ken Kwong-Kay Wong
The COVID-19 pandemic is rewriting the rules of retail. A growing number of shoppers now rely on same-day delivery, curbside pickup, in-home/in-car delivery, and AI-powered drive-thru to get their goods. To accommodate such a paradigm shift, retailers must undertake significant changes in their business models to become digitally enabled and data-driven.
Omni-Channel Retailing is written to help retailers and retail students understand the importance of delivering a seamless, cohesive, and contextual customer experience throughout the shopping journey. This book addresses today's retailers' challenges and gives new ideas for implementation. Relevant activities and discussion topics are included to help readers master the concepts.
Scalable Detection of Concept Drifts on Data Streams with Parallel Adaptive W...Jonas Traub
Paper: Scalable Detection of Concept Drifts on Data Streams
with Parallel Adaptive Windowing
Abstract: Machine learning techniques for data stream analysis suffer from concept drifts such as changed user preferences, varying weather conditions, or economic changes. These concept drifts cause wrong predictions and lead to incorrect business decisions. Concept drift detection methods such as adaptive windowing (Adwin) allow for adapting to concept drifts on the fly.
In this paper, we examine Adwin in detail and point out its throughput bottlenecks. We then introduce several parallelization alternatives to address these bottlenecks. Our optimizations lead
to a speedup of two orders of magnitude over the original Adwin implementation. Thus, we explore parallel adaptive windowing to provide scalable concept detection for high-velocity data streams with millions of tuples per second.
The report show research findings of existing sales and distribution practices of a few leading food companies and discusses ways to improve the sales through revision in sales and distribution policies
Beyond Churn Prediction : An Introduction to uplift modelingPierre Gutierrez
These slides are from a talk I at the papis conference in Boston in 2016. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
I tried to bridge the gap between causal inference theory and uplift theory, especially concerning how to properly cross validate the results. The notation used is the one from uplift modelling.
Uplift Modelling as a Tool for Making Causal Inferences at Shopify - Mojan HamedRising Media Ltd.
For many businesses, it is not enough to model the probability of an outcome but rather, “given a predictive model, what can we do to change the probability of this outcome?” The goal of this talk is to present how uplift modelling is used to make causal inferences that guide acquisition strategy at Shopify. Mojan will walk through a case study focused on the statistics and experimental design behind uplift modelling, in addition to the learnings gained from bringing this model to production. The python implementation of this presentation will be made available to attendees.
As part of my master thesis "Stochastic Models of Noncontractual Consumer Relationships" I participated in a contest organized by the DMEF to forecast Consumer Lifetime Value. My submitted model finished second (out of 25 entries). These slides concisely summarize my approach and also the final model.
Stochastic Models of Noncontractual Consumer RelationshipsMOSTLY AI
Master thesis, which introduces a newly derived stochastic prediction model for customer lifetime values, that is able to incorporate regularities within the transaction timings of the customer base.
Incorporating Regularity into Models of Noncontractual Customer-Firm Relation...MOSTLY AI
Presentation of a newly derived stochastic prediction model for customer lifetime values, which is able to incorporate regularity within the transaction patterns.
A.I. (artificial intelligence) platforms are popping up all the time, and many of them can and should be used to help grow your brand, increase your sales and decrease your marketing costs.In this presentation:We will review some of the best AI platforms that are available for you to use.We will interact with some of the platforms in real-time, so attendees can see how they work.We will also look at some current brands that are using AI to help them create marketing messages, saving them time and money in the process. Lastly, we will discuss the pros and cons of using AI in marketing & branding and have a lively conversation that includes comments from the audience.
Key Takeaways:
Attendees will learn about LLM platforms, like ChatGPT, and how they work, with preset examples and real time interactions with the platform. Attendees will learn about other AI platforms that are creating graphic design elements at the push of a button...pre-set examples and real-time interactions.Attendees will discuss the pros & cons of AI in marketing + branding and share their perspectives with one another. Attendees will learn about the cost savings and the time savings associated with using AI, should they choose to.
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
As the call for for skilled experts continues to develop, investing in quality education and education from a reputable https://www.safalta.com/online-digital-marketing/best-digital-marketing-institute-in-noida Digital advertising institute in Noida can lead to a a success career on this eve
In this presentation, Danny Leibrandt explains the impact of AI on SEO and what Google has been doing about it. Learn how to take your SEO game to the next level and win over Google with his new strategy anyone can use. Get actionable steps to rank your name, your business, and your clients on Google - the right way.
Key Takeaways:
1. Real content is king
2. Find ways to show EEAT
3. Repurpose across all platforms
Mastering Local SEO for Service Businesses in the AI Era is tailored specifically for local service providers like plumbers, dentists, and others seeking to dominate their local search landscape. This session delves into leveraging AI advancements to enhance your online visibility and search rankings through the Content Factory model, designed for creating high-impact, SEO-driven content. Discover the Dollar-a-Day advertising strategy, a cost-effective approach to boost your local SEO efforts and attract more customers with minimal investment. Gain practical insights on optimizing your online presence to meet the specific needs of local service seekers, ensuring your business not only appears but stands out in local searches. This concise, action-oriented workshop is your roadmap to navigating the complexities of digital marketing in the AI age, driving more leads, conversions, and ultimately, success for your local service business.
Key Takeaways:
Embrace AI for Local SEO: Learn to harness the power of AI technologies to optimize your website and content for local search. Understand the pivotal role AI plays in analyzing search trends and consumer behavior, enabling you to tailor your SEO strategies to meet the specific demands of your target local audience. Leverage the Content Factory Model: Discover the step-by-step process of creating SEO-optimized content at scale. This approach ensures a steady stream of high-quality content that engages local customers and boosts your search rankings. Get an action guide on implementing this model, complete with templates and scheduling strategies to maintain a consistent online presence. Maximize ROI with Dollar-a-Day Advertising: Dive into the cost-effective Dollar-a-Day advertising strategy that amplifies your visibility in local searches without breaking the bank. Learn how to strategically allocate your budget across platforms to target potential local customers effectively. The session includes an action guide on setting up, monitoring, and optimizing your ad campaigns to ensure maximum impact with minimal investment.
Mastering Local SEO for Service Businesses in the AI Era is tailored specifically for local service providers like plumbers, dentists, and others seeking to dominate their local search landscape. This session delves into leveraging AI advancements to enhance your online visibility and search rankings through the Content Factory model, designed for creating high-impact, SEO-driven content. Discover the Dollar-a-Day advertising strategy, a cost-effective approach to boost your local SEO efforts and attract more customers with minimal investment. Gain practical insights on optimizing your online presence to meet the specific needs of local service seekers, ensuring your business not only appears but stands out in local searches. This concise, action-oriented workshop is your roadmap to navigating the complexities of digital marketing in the AI age, driving more leads, conversions, and ultimately, success for your local service business.
Key Takeaways:
Embrace AI for Local SEO: Learn to harness the power of AI technologies to optimize your website and content for local search. Understand the pivotal role AI plays in analyzing search trends and consumer behavior, enabling you to tailor your SEO strategies to meet the specific demands of your target local audience. Leverage the Content Factory Model: Discover the step-by-step process of creating SEO-optimized content at scale. This approach ensures a steady stream of high-quality content that engages local customers and boosts your search rankings. Get an action guide on implementing this model, complete with templates and scheduling strategies to maintain a consistent online presence. Maximize ROI with Dollar-a-Day Advertising: Dive into the cost-effective Dollar-a-Day advertising strategy that amplifies your visibility in local searches without breaking the bank. Learn how to strategically allocate your budget across platforms to target potential local customers effectively. The session includes an action guide on setting up, monitoring, and optimizing your ad campaigns to ensure maximum impact with minimal investment.
Top 3 Ways to Align Sales and Marketing Teams for Rapid GrowthDemandbase
In this session, Demandbase’s Stephanie Quinn, Sr. Director of Integrated and Digital Marketing, Devin Rosenberg, Director of Sales, and Kevin Rooney, Senior Director of Sales Development will share how sales and marketing shapes their day-to-day and what key areas are needed for true alignment.
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
Monthly Social Media News Update May 2024Andy Lambert
TL;DR. These are the three themes that stood out to us over the course of last month.
1️⃣ Social media is becoming increasingly significant for brand discovery. Marketers are now understanding the impact of social and budgets are shifting accordingly.
2️⃣ Instagram’s new algorithm and latest guidance will help us maintain organic growth. Instagram continues to evolve, but Reels remains the most crucial tool for growth.
3️⃣ Collaboration will help us unlock growth. Who we work with will define how fast we grow. Meta continues to evolve their Creator Marketplace and now TikTok are beginning to push ‘collabs’ more too.
Financial curveballs sent many American families reeling in 2023. Household budgets were squeezed by rising interest rates, surging prices on everyday goods, and a stagnating housing market. Consumers were feeling strapped. That sentiment, however, appears to be waning. The question is, to what extent?
To take the pulse of consumers’ feelings about their financial well-being ahead of a highly anticipated election, ThinkNow conducted a nationally representative quantitative survey. The survey highlights consumers’ hopes and anxieties as we move into 2024. Let's unpack the key findings to gain insights about where we stand.
Mastering Multi-Touchpoint Content Strategy: Navigate Fragmented User JourneysSearch Engine Journal
Digital platforms are constantly multiplying, and with that, user engagement is becoming more intricate and fragmented.
So how do you effectively navigate distributing and tailoring your content across these various touchpoints?
Watch this webinar as we dive into the evolving landscape of content strategy tailored for today's fragmented user journeys. Understanding how to deliver your content to your users is more crucial than ever, and we’ll provide actionable tips for navigating these intricate challenges.
You’ll learn:
- How today’s users engage with content across various channels and devices.
- The latest methodologies for identifying and addressing content gaps to keep your content strategy proactive and relevant.
- What digital shelf space is and how your content strategy needs to pivot.
With Wayne Cichanski, we’ll explore innovative strategies to map out and meet the diverse needs of your audience, ensuring every piece of content resonates and connects, regardless of where or how it is consumed.
In this presentation, Danny Leibrandt explains the impact of AI on SEO and what Google has been doing about it. Learn how to take your SEO game to the next level and win over Google with his new strategy anyone can use. Get actionable steps to rank your name, your business, and your clients on Google - the right way.
Key Takeaways:
1. Real content is king
2. Find ways to show EEAT
3. Repurpose across all platforms
Most small businesses struggle to see marketing results. In this session, we will eliminate any confusion about what to do next, solving your marketing problems so your business can thrive. You’ll learn how to create a foundational marketing OS (operating system) based on neuroscience and backed by real-world results. You’ll be taught how to develop deep customer connections, and how to have your CRM dynamically segment and sell at any stage in the customer’s journey. By the end of the session, you’ll remove confusion and chaos and replace it with clarity and confidence for long-term marketing success.
Key Takeaways:
• Uncover the power of a foundational marketing system that dynamically communicates with prospects and customers on autopilot.
• Harness neuroscience and Tribal Alignment to transform your communication strategies, turning potential clients into fans and those fans into loyal customers.
• Discover the art of automated segmentation, pinpointing your most lucrative customers and identifying the optimal moments for successful conversions.
• Streamline your business with a content production plan that eliminates guesswork, wasted time, and money.
Come learn how YOU can Animate and Illuminate the World with Generative AI's Explosive Power. Come sit in the driver's seat and learn to harness this great technology.
Is AI-Generated Content the Future of Content Creation?Cut-the-SaaS
Discover the transformative power of AI in content creation with our presentation, "Is AI-Generated Content the Future of Content Creation?" by Puran Parsani, CEO & Editor of Cut-The-SaaS. Learn how AI-generated content is revolutionizing marketing, publishing, education, healthcare, and finance by offering unprecedented efficiency, creativity, and scalability.
Understanding
AI-Generated Content:
AI-generated content includes text, images, videos, and audio produced by AI without direct human involvement. This technology leverages large datasets to create contextually relevant and coherent material, streamlining content production.
Key Benefits:
Content Creation: Rapidly generate high-quality content for blogs, articles, and social media.
Brainstorming: AI simulates conversations to inspire creative ideas.
Research Assistance: Efficiently summarize and research information.
Market Insights:
The content marketing industry is projected to grow to $17.6 billion by 2032, with AI-generated content expected to dominate over 55% of the market.
Case Study: CNET’s AI Content Controversy:
CNET’s use of AI for news articles led to public scrutiny due to factual inaccuracies, highlighting the need for transparency and human oversight.
Benefits Across Industries:
Marketing: Personalize content at scale and optimize engagement with predictive analytics.
Publishing: Automate content creation for faster publication cycles.
Education: Efficiently generate educational materials.
Healthcare: Create accurate content for patients and professionals.
Finance: Produce timely financial content for decision-making.
Challenges and Ethical Considerations:
Transparency: Disclose AI use to maintain trust.
Bias: Address potential AI biases with diverse datasets.
SEO: Ensure AI content meets SEO standards.
Quality: Maintain high standards to prevent misinformation.
Conclusion:
AI-generated content offers significant benefits in efficiency, personalization, and scalability. However, ethical considerations and quality assurance are crucial for responsible use. Explore the future of content creation with us and see how AI is transforming various industries.
Connect with Us:
Follow Cut-The-SaaS on LinkedIn, Instagram, YouTube, Twitter, and Medium. Visit cut-the-saas.com for more insights and resources.
Is AI-Generated Content the Future of Content Creation?
PhD Seminar Riezlern 2016
1. Leveraging Regularity in Predicting Customer
Lifetime Value
Michael Platzer & Thomas Reutterer
Seminar Riezlern 2016
2. Warm Up
PAGE 2
Customer A
Customer B
1-Jan-16, 09:00 21-Jun-16, 10:28
1) Which customer would you prefer? The regular one, or the clumpy one?
2) Which type of customers are more prevalent? The regular ones, or the clumpy ones?
Two customers – Same Recency, Same Frequency
3. PAGE 3
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
4. PAGE 4
dead?
non-contractual setting
Customer purchases, until she stops purchasing.
However, dropout event is not observed.
Buy-Till-You-Die
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
alive!
5. Key Issues in the Management
of Customer Relationships
PAGE 5
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
?
?
Given: Purchase history of customer
cohort in non-contractual setting.
Example:
CD Sales
Broadening the context:
• purchase ≈ transaction ≈ event …
• customer relationship ≈ channel activity ≈
service activity …
Questions:
How valuable is that cohort?
How many purchases to expect?
Who will still be active?
Who will be most active?
When will next purchase take place?
6. PAGE 6
BTYD “Gold Standard”
Pareto/NBD
Schmittlein, Morrison and Colombo, 1987
Assumptions
1. Purchase process (while ‘alive’)
• Purchases follow Poisson process, i.e. exponentially-distributed
inter-transaction times, itti,j ~ Exponential(λi)
• λi are Gamma (r, α) distributed across customers
Pareto
NBD
(Ehrenberg 1959)
à parameter estimation of (r, α, s, β) via Maximum Likelihood
à closed-form solutions for key expressions P(alive), # of future purchases
à require only recency/frequency summary statistics (x, tx, T) per customer
2. Dropout (‘death’) process
• (Unobserved) customer’s lifetime is exponentially distributed,
lifetime τi ~ Exponential(μi)
• μi are Gamma (s, β) distributed across customers
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
3. λ and μ vary independently
7. PAGE 7
BTYD Models
• BG/NBD (Fader, Hardie, and Lee 2005)
Discrete time defection process (after any transaction) instead of continuous
• MBG/NBD (Batislam et al. 2007), CBG/NBD (Hoppe and Wagner 2007)
Customers can drop out at time zero (immediately after first purchase)
• PDO/NBD (Jerath et al. 2011)
Defection opportunities tied to calendar time (indep. of transaction timing)
• GG/NBD (Bemmaor and Glady 2012)
Flexible lifetime model, departing from exponential (Gamma-Gompertz)
• Pareto/NBD variant (Abe 2009)
Hierarchical Bayes extension of Pareto/NBD (dependencies of λi and μi)
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
à All modify dropout process, but not purchase process
8. PAGE 8
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
10. PAGE 10
next event?
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Regularity improves
Predictability
11. PAGE 11
next event?
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Regularity improves
Predictability
12. PAGE 12
next event?
Well, so what?
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Regularity improves
Predictability
13. PAGE 13
still alive?
A
B
Buy-Till-You-Die Setting
Customer A and B exhibit same Recency and Frequency,
yet we come to different assessments regarding P(alive).
Regularity improves
Predictability
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
14. PAGE 14
• Erlang-k Herniter (1971)
• Gamma Wheat & Morrison (1990)
• CNBD Chatfield and Goodhardt (1973)
Schmittlein and Morrison (1983)
Morrison and Schmittlein (1988)
• CNBD Models Gupta (1991)
Wu and Chen (2000)
Schweidel and Fader (2009)
Regularity in Purchase
Timings
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
15. PAGE 15
• RFMC Zhang, Bradlow and Small (2015)
Irregularity in
Purchase Timings
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
16. PAGE 16
Empirical Findings
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Data Sets
Grocery kwheat = 2.5
Donations kwheat = 2.2
Health Supplements kwheat = 2.1
Office Supply kwheat = 1.8
CD Sales kwheat = 1.0
Fashion & Accessoires kwheat = 0.6
Grocery Categories
Coffee pads kwheat = 3.1
Detergents kwheat = 2.8
Toilet Paper kwheat = 2.8
Cat food kwheat = 2.8
…
Light bulbs kwheat = 1.9
Cosmetics & perfumes kwheat = 1.6
Sparkling Wine kwheat = 1.6
17. PAGE 17
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
18. Pareto/GGG
Platzer and Reutterer, forthcoming
PAGE 18
Customer Level
• Purchase Process: While alive, customer purchases with Gamma
distributed waiting times; i.e. itti,j ~ Gamma(ki, ki λi)
• Dropout Process: Each customer remains alive for an exponentially
distributed lifetime with death rate μi; i.e. lifetime τi ~ Exponential(μi)
Heterogeneity across Customers
• λi ~ Gamma(r, α)
• μi ~ Gamma(s, β)
• ki ~ Gamma(t, γ)
• λi, μi, ki vary independently
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Pareto/GGG =
Pareto/NBD + Varying Regularity
19. Gamma Distributed
Interpurchase Times
PAGE 19
k=0.3 k=1
Exponential
k=8
Erlang-8
regularrandomclumpy
Coefficient of Variation = 1 / sqrt(k)
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
20. Pareto/GGG
Estimation via MCMC
Component-wise Slice Sampling
within Gibbs with Data Augmentation
SEITE 20
L Significantly Increased Computational Costs
(2mins for drawing 1’000 customers)
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
21. Pareto/GGG
Estimation via MCMC
Component-wise Slice Sampling
within Gibbs with Data Augmentation
SEITE 21
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
L Significantly Increased Computational Costs
(2mins for drawing 1’000 customers)
J but…
• Posterior Distributions instead of Point Estimates
• Also for Individual Level Parameters
• Direct Simulation of Key Metrics of Managerial Interest
• And only one additional summary statistic required
22. Simulation Study
Design
160 scenarios covering a wide range of parameter settings
(similar to simulation design from BG/BB paper)
• N = {1000, 4000}
• r = {0.25, 0.75}, α = {5, 15}
• s = {0.25, 0.75}, β = {5, 15}
• (t, γ) = {(1.6, 0.4), (5, 2.5), (6, 4), (8, 8), (17, 20)}
=> Total of 400’000 simulated customers
=> Total of 64 billion individual-level parameter draws (via slice sampling)
Compare individual-level forecast accuracy of Pareto/GGG vs. Pareto/NBD
in terms of mean absolute error (MAE). Study relative improvement in terms
in MAE.
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
23. Simulation Study
Regularity improves Predictability
Regularity improves Predictability
• bigger lift for bigger regularity
• even for mildly regular patterns
we see lift
• no lift for random and clumpy
customers
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
24. Simulation Study
Lift in Predictive Accuracy
by Segment
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
25. Simulation Study
Interplay of Recency,
Frequency and Regularity
Assumptions: mean(itt) = 6 weeks, mean(lifetime) = 52 weeks
A
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
26. Simulation Study
Interplay of Recency,
Frequency and Regularity
Same RF, but different P(alive)
for different k! Particularly when
customer is already “overdue”.
Regular customers are less
likely and clumpy customers are
more likely to be still alive,
when compared to the
randomly purchasing customer.
Assumptions: mean(itt) = 6 weeks, mean(lifetime) = 52 weeks
A
B
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
27. Simulation Study
Interplay of Recency,
Frequency and Regularity
Same RF, but different P(alive)
for different k! Particularly when
customer is already “overdue”.
Regular customers are less
likely and clumpy customers are
more likely to be still alive,
when compared to the
randomly purchasing customer.
Assumptions: mean(itt) = 6 weeks, mean(lifetime) = 52 weeks
A
B
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
28. Empirical Findings
regular
Poisson
clumpy
à regularity varies across but also within datasets
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
29. à improved predictive accuracy for datasets with regular patterns
median(k) rel. Lift in MAE
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Empirical Findings
30. à estimates for next transaction timings differ, when
regularity is taking into consideration
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Empirical Findings
31. PAGE 31
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
32. (M)BG/CNBD-k
Platzer and Reutterer, forthcoming
PAGE 32
Customer Level
• Purchase Process: While alive, customer purchases with Erlang-k
distributed waiting times; i.e. itti,j ~ Erlang-k(λi)
• Dropout Process: A customer drops out at a (re-)purchaseevent with
probability pi
Heterogeneity across Customers
• λi ~ Gamma(r, α)
• pi ~ Beta(a, b)
• λi, pi vary independently
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
BG/CNBD-k =
BG/NBD + Fixed Regularity
MBG/CNBD-k =
MBG/NBD + Fixed Regularity
33. (M)BG/CNBD-k
Platzer and Reutterer, forthcoming
PAGE 33
Closed-Form Expressions
• Likelihood à 100-1000x faster parameter estimation via MLE than MCMC
• P(X(t)=x | r, α, a, b) à approximate Unconditional Expectation
• P(alive | r, α, a, b, x, tx, T) à key component for Conditional Expectation
• Conditional Expected Transactions à “pretty good” approximation possible
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Erlang-k = Poisson with every kth event counted
34. PAGE 34
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Simulation Study
Design
324 scenarios covering a wide range of parameter settings – 5 repeats each
(similar to simulation design from BG/NBD paper)
• N = 4000, T.cal = 52, T.star = {4, 16, 52}
• r = {0.25, 0.50, 0.75}, α = {5, 10, 15}
• s = {0.50, 0.75, 1.00}, β = {2.5, 5, 10}
• k = {1, 2, 3, 4}
=> total of 1’300’000 simulated customers
Compare individual-level forecast accuracy of Pareto/GGG vs. Pareto/NBD
in terms of mean absolute error (MAE). Study relative improvement in terms
in MAE.
35. PAGE 35
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Simulation Study
Example
36. PAGE 36
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Simulation Study
Results
Regularity improves Predictability
• bigger lift for bigger regularity
• even for mildly regular patterns we see lift
37. PAGE 37
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Empirical Findings
Results
Findings
1. MBG/NBD either on par
or better than BG/NBD
2. MBG/CNBD-k sees lift in
forecast accuracy, if
regularity present
3. MBG/CNBD-k comes
close to P/GGG
38. PAGE 38
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
Empirical Findings
Results
Yet to come: Study Lift by Retail Category
39. PAGE 39
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
40. PAGE 40
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
BTYDplus
• https://github.com/mplatzer/BTYDplus
• GPL-3 license
• Implementations of
• MBG/NBD – Batislam et al. (2007)
• GammaGompertz/NBD – Bemmaor & Glady (2012)
• (M)BG/CNBD-k – Platzer and Reutterer (forthcoming)
• Pareto/NBD (MCMC) - Shao-Hui and Liu (2007)
• Pareto/NBD variant (MCMC) – Abe (2009)
• Pareto/GGG (MCMC) – Platzer and Reutterer (forthcoming)
• Fully tested and documented, incl. demos
• Vignette will be coming
…
Users
41. PAGE 41
1. Intro to BTYD models
2. On the Subject of Regularity
3. Our Pareto/GGG model
4. Our (M)BG/CNBD-k model
5. Our BTYDplus R package
BTYDplus
demo
> elog
cust date
1: 4 1997-01-18
2: 4 1997-08-02
3: 4 1997-12-12
4: 18 1997-01-04
5: 21 1997-01-01
---
6914: 23556 1997-07-26
6915: 23556 1997-09-27
6916: 23556 1998-01-03
6917: 23556 1998-06-07
6918: 23569 1997-03-25
> (cbs <- elog2cbs(elog, per="week",
T.cal=as.Date("1997-09-30"), T.tot=as.Date("1997-09-30")))
cust x t.x litt T.cal T.star x.star
1: 4 1 28.000000 3.3322045 36.42857 39 1
2: 18 0 0.000000 0.0000000 38.42857 39 0
3: 21 1 1.714286 0.5389965 38.85714 39 0
4: 50 0 0.000000 0.0000000 38.85714 39 0
5: 60 0 0.000000 0.0000000 34.42857 39 0
---
2353: 23537 0 0.000000 0.0000000 27.00000 39 2
2354: 23551 5 24.285714 5.5243721 27.00000 39 0
2355: 23554 0 0.000000 0.0000000 27.00000 39 1
2356: 23556 4 26.571429 6.3127713 27.00000 39 2
2357: 23569 0 0.000000 0.0000000 27.00000 39 0
calibration summary stats
x = Frequency
t.x = Recency
litt = Sum Over Logarithmic
Intertransaction Times
holdout summary stats
Transform event-log to summary stats
(optionally one can split data into calibration and holdout)
customer ID
46. ZBS: Clumpiness Measure C
a metric-based approach
Predicting Customer Value Using Clumpiness: From RFM to RFMC
Zhang, Bradlow, Small
• Introduce metric C which captures the “non-randomness” in timing patterns
• Straightforward calculation at individual-level;
• Useful for descriptive analysis and segmentation;
47. ZBS: Clumpiness Measure C
a metric-based approach
Main Empirical Findings
• Capturing timing patterns adds
predictive power
• When controlling for R and F, then
clumpy customers tend to be more
active in the future
both findings are supported
and can be explained by our
model-based approach
48. ZBS: Clumpiness Measure C
a metric-based approach
Shortcomings
• Requires many transactions at
individual-level
• Metric C will be skewed when
dealing with different acquisition
dates and churn settings
both are appropriately handled
by our model-based approach
49. ZBS: Clumpiness Measure C
a metric-based approach
à sparse individual-level data mandates a model-based approach
50. Parameter Estimation via MCMC
Component-wise Slice Sampling within Gibbs with Data Augmentation
SEITE 50