The document discusses Länsförsäkringar's journey from product-oriented to customer-oriented marketing by using scoring models and an offering engine. It describes how the offering engine calculates the probability and value of potential purchases for each customer to prioritize offerings. The scoring models assess multiple data points for each individual customer rather than customer groups to more efficiently target offerings. This approach has improved decision making and allowed Länsförsäkringar to develop customized, priority offerings for its entire customer base through its CRM system on a daily basis.
This document discusses various interest rates and yield curves. It begins with an overview of simple versus compound interest, nominal annual rate (NAR) versus effective annual rate (EAR), equivalent rates, and nominal versus real rates. It then covers the risk-free rate using US Treasury securities, yield to maturity (YTM), spot rates for zero-coupon bonds, and forward rates. Spot and forward rates are used to synthesize coupon bonds or determine future implied interest rates between two periods.
This document provides guidance on audience development strategies for magazines. It discusses determining goals, identifying the target audience, and developing an appropriate budget. Sources for acquiring and retaining readers that are covered include renewals, direct mail, gifts, insert cards, magazine cover wraps, internet promotions, paid search, and email marketing. Testing different approaches is emphasized to determine the most effective tactics. Budgeting is recommended based on calculating the average cost per new and renewed subscription from each source.
The document discusses using expected value in decision making and provides an example using a decision tree. It shows a scenario where an advertising budget can be small or large. If small, the expected profit is 46. If large, the expected profit is 68. Therefore, without sample information, the large budget should be chosen. The document then discusses taking a sample to get more information before making the decision.
This document provides an overview of yield curves and interest rate concepts. It defines key terms like spot rates, forward rates, and yield to maturity. It also discusses theories that aim to explain the shape of the yield curve, including expectations theory, liquidity preference theory, and market segmentation theory. While each theory provides some insights, the document concludes that the real shape of the yield curve at any point in time depends on expected inflation, liquidity preferences, and the supply and demand of funds in the market.
This document presents a theoretical model and empirical analysis of how the size of a firm's banking pool can signal the firm's quality to investors. The model suggests higher quality firms will borrow from fewer banks due to greater monitoring. Empirically, using a sample of European loans, the analysis finds that firms with higher Altman Z-scores, indicating better financial health, tend to borrow from smaller banking pools, supporting the signaling theory. The effect is weaker for larger, more transparent firms. Syndicated loans also mitigate the signaling through stronger coordination.
Research commentary from RBC analyst Eric Berg, following annoucement by Pru & GM to effect a $20+ bil group annuity contract to transfer GM salaried pension obligations
This investor presentation provides an overview of Multiplus S.A., TAM Airlines' loyalty program:
- Multiplus has over 8 million members and 160 commercial partnerships after starting as an independent business unit in 2009.
- It has a flexible business model where it sells points to partners to award customers and buys rewards from partners to deliver to members. This results in a spread between the point price and cost of rewards.
- In 2Q11, Multiplus saw growth in points issued and redeemed year-over-year. It aims to expand its partner network and redemption options while improving customer experience and maintaining operational efficiency.
The document discusses Länsförsäkringar's journey from product-oriented to customer-oriented marketing by using scoring models and an offering engine. It describes how the offering engine calculates the probability and value of potential purchases for each customer to prioritize offerings. The scoring models assess multiple data points for each individual customer rather than customer groups to more efficiently target offerings. This approach has improved decision making and allowed Länsförsäkringar to develop customized, priority offerings for its entire customer base through its CRM system on a daily basis.
This document discusses various interest rates and yield curves. It begins with an overview of simple versus compound interest, nominal annual rate (NAR) versus effective annual rate (EAR), equivalent rates, and nominal versus real rates. It then covers the risk-free rate using US Treasury securities, yield to maturity (YTM), spot rates for zero-coupon bonds, and forward rates. Spot and forward rates are used to synthesize coupon bonds or determine future implied interest rates between two periods.
This document provides guidance on audience development strategies for magazines. It discusses determining goals, identifying the target audience, and developing an appropriate budget. Sources for acquiring and retaining readers that are covered include renewals, direct mail, gifts, insert cards, magazine cover wraps, internet promotions, paid search, and email marketing. Testing different approaches is emphasized to determine the most effective tactics. Budgeting is recommended based on calculating the average cost per new and renewed subscription from each source.
The document discusses using expected value in decision making and provides an example using a decision tree. It shows a scenario where an advertising budget can be small or large. If small, the expected profit is 46. If large, the expected profit is 68. Therefore, without sample information, the large budget should be chosen. The document then discusses taking a sample to get more information before making the decision.
This document provides an overview of yield curves and interest rate concepts. It defines key terms like spot rates, forward rates, and yield to maturity. It also discusses theories that aim to explain the shape of the yield curve, including expectations theory, liquidity preference theory, and market segmentation theory. While each theory provides some insights, the document concludes that the real shape of the yield curve at any point in time depends on expected inflation, liquidity preferences, and the supply and demand of funds in the market.
This document presents a theoretical model and empirical analysis of how the size of a firm's banking pool can signal the firm's quality to investors. The model suggests higher quality firms will borrow from fewer banks due to greater monitoring. Empirically, using a sample of European loans, the analysis finds that firms with higher Altman Z-scores, indicating better financial health, tend to borrow from smaller banking pools, supporting the signaling theory. The effect is weaker for larger, more transparent firms. Syndicated loans also mitigate the signaling through stronger coordination.
Research commentary from RBC analyst Eric Berg, following annoucement by Pru & GM to effect a $20+ bil group annuity contract to transfer GM salaried pension obligations
This investor presentation provides an overview of Multiplus S.A., TAM Airlines' loyalty program:
- Multiplus has over 8 million members and 160 commercial partnerships after starting as an independent business unit in 2009.
- It has a flexible business model where it sells points to partners to award customers and buys rewards from partners to deliver to members. This results in a spread between the point price and cost of rewards.
- In 2Q11, Multiplus saw growth in points issued and redeemed year-over-year. It aims to expand its partner network and redemption options while improving customer experience and maintaining operational efficiency.
This document provides an overview of survival analysis. It defines key terms like survival, censoring, and hazard functions. It describes the Kaplan-Meier method for estimating survival functions from censored data and comparing survival curves between groups using the log-rank test. Censoring occurs when subjects are lost to follow-up before the event of interest. The Kaplan-Meier method accounts for censoring to calculate the probability of surviving up to different time points.
A gentle introduction to survival analysisAngelo Tinazzi
This document provides an introduction to survival analysis techniques for statistical programmers. It discusses key concepts in survival analysis including censoring, the Kaplan-Meier method for estimating survival probabilities, and assumptions of survival models. Programming aspects like creating time-to-event datasets and using SAS procedures for survival analysis are also covered.
This document discusses survival analysis and its application to analyzing the departure dynamics of Wikipedia editors. It begins by defining survival analysis and its goal of modeling time-to-event data using techniques that account for censoring. A case study is presented on analyzing data from 110,000 Wikipedia editors to determine who is likely to stop editing, how long they will continue editing, and why they stop. Statistical techniques like the Kaplan-Meier estimator, Cox proportional hazards models, and adjusted survival curves are used to analyze editing durations and identify covariates that impact the hazard rate of editors stopping contributions.
This document discusses survival analysis techniques. It begins with an overview of survival, censoring, and the need for survival analysis when not all patients have died or had the event of interest. It then describes the key techniques of life tables/actuarial analysis and the Kaplan-Meier method. Life tables involve constructing a hypothetical cohort and estimating survival at different ages based on mortality rates. The Kaplan-Meier method is commonly used to illustrate survival curves and gives partial credit to censored observations. A modified life table is also presented to analyze survival outcomes in different treatment groups.
Survival analysis is a branch of statistics used to analyze time-to-event data, such as time until death or failure. It estimates the probability that an individual survives past a given time and compares survival times between groups. Objectives include estimating survival probabilities, comparing survival between groups, and assessing how covariates relate to survival time. Survival data can be complete or censored. The Kaplan-Meier estimator is used to estimate survival when there is censoring. The log-rank test compares survival curves between treatment groups, and Cox regression incorporates covariates to predict survival probabilities.
This document provides an introduction to survival data analysis. It discusses key concepts like censoring, where the event of interest is not observed for some subjects due to other events. Right censoring is common, where the event was not observed by the end of the study. Conditioning is also important, where the risk of an event changes over time based on a subject's survival up to that point. Basic notation is introduced, including the survival function, hazard function, and density function for modeling event times. The document outlines topics like parametric and non-parametric models, regression methods, and complications in survival analysis.
Geo Livi has over 8 years of experience as a Temenos T24 consultant specializing in the retail banking module. He has extensive experience in business analysis, solution design, developing technical specifications, and managing implementation projects. Some of his project experience includes implementing product bundles and broker commissions for various T24 clients as well as performing an upgrade for Hypo Group Alpe Adria and providing support for the implementation at North Shore Credit Union, the first client to use T24's arrangement architecture module.
OA predicta net week data360 predictive analytics ralph overbeck publishedRalph Overbeck
The document discusses predictive analytics and its use of big data. It provides examples of predictive customer management and predictive asset management. Predictive analytics uses historical data and customer insights to predict future events and enable organizations to move from historical to forward-looking perspectives. It transforms data patterns into insights, insights into actions, and actions into profitable outcomes. Predictive analytics predicts with greater accuracy and significance than historical analytics.
Building Innovative Subscription-based Businesses: Lecture 2Martin Westhead
This document provides an overview and key concepts from a lecture on building subscription-based businesses. It discusses recurring revenue, lifetime revenue modeling using different approaches, and the core subscription business model of acquiring new customers, boosting revenue per customer through upsells and cross-sells, and reducing churn. It also covers important subscription health metrics like magic numbers, retention rates, recurring profit margins, and growth efficiencies. A case study from SendGrid reviews their use of metrics like monthly recurring revenue, customer counts, customer retention, customer acquisition costs, and lifetime value to measure growth and profitability in their subscription business.
This document discusses using survival analysis to model customer churn. It explains what customer lifetime value is and why it is important. It then covers how survival analysis can be used to predict when customers will churn rather than just whether they will churn. The document outlines the methodology used, including data cleaning, defining churn, and graphing results. It also covers parametric survival models and evaluating different survival distributions. The conclusions state that survival analysis provides powerful insights into how attrition risk changes over time.
The document discusses using high-performance computing (HPC) to value complex insurance guarantees like variable annuities. It describes the challenges in valuing variable annuities due to their path-dependent cash flows and exposure to market risks. VORtech is working with ING Re to optimize the HPC implementation for valuing variable annuities to improve performance while maintaining flexibility and transparency of the valuation code. Scenarios for implementing HPC include prototyping on more accessible platforms and optimizing parallelization and memory usage.
This document discusses hedging strategies for insurance companies. It notes that establishing hedging programs can mitigate earnings volatility and is viewed positively by rating agencies and analysts. The document outlines identifying risks such as interest rate, market, and credit risk; choosing hedging strategies such as static or dynamic hedging; and implementing hedges through financial modeling and executing trades. Effective hedging programs require quantifying goals, generating economic scenarios, and reviewing hedging effectiveness over time.
Modelling Customer Lifetime Revenue for Subscription BusinessDatabricks
Customer lifetime Value/Revenue(LTV/R) is the present value of the future profits/revenue from a customer. Estimating it, is important for businesses to optimise the marketing costs in acquiring and retaining the customers. Complex consumer behaviour and innumerable ways a consumer interacts with the business makes things challenging to estimate it. Years of ongoing research in this field has led to the development of various ML tools and techniques. We would like to take this opportunity to walkthrough some of these techniques and their applications in specific business contexts.
Condé Nast is a global media company that produces some of the world’s leading print, digital, video and social brands. These include Vogue, GQ, The New Yorker, Vanity Fair, Wired and Architectural Digest (AD), Condé Nast Traveler and La Cucina Italiana, among others. Subscription revenue is one of the major revenue streams for the organization and we’d like to demonstrate the implementation of LTV/R model for the subscription revenue for one of the brands using survival models and along with that illustrate the following.
Estimate the average lifetime (ALT) of a brand’s subscriber.
Estimate the average lifetime of various segments within the brand and identify the most valuable/least valuable segments, so marketing teams could device appropriate targeting strategies.
Finally attempt to estimate the lifetime at a subscriber level.
Key insights & findings through the analysis.
Demo of sample code
Leveraging databricks delta files for our big data processing needs.
The document defines key terms and deliverables for a Lean Six Sigma project. It explains that defining VOC (Voice of the Customer), VOB (Voice of Business), and CTQs (Critical to Quality requirements) is the first deliverable. This involves understanding the problem from the customer's perspective and how they define project success. Additional deliverables include defining the project scope, quantifying benefits, and developing a project management plan. Tools for each deliverable like affinity diagrams and project charters are also outlined.
Metrics: Talking about Churn and Retention in the Board RoomGainsight
Speakers:
David Spitz, Managing Director at Pacific Crest
Marc Linden, CFO at Intacct
Kathy Lord, VP Sales & CSM at Intacct
Dave Kellogg, CEO at Host Analytics
Alison Homlund, VP CSM at Host Analytics
Presented at Pulse Conference 2015.
Explore our students' cutting-edge project on predicting bank customer churn using advanced analytics techniques. This project employs machine learning algorithms to analyze customer data and forecast the likelihood of churn, offering valuable insights for financial institutions. Gain insights into customer retention strategies, predictive modeling, and the potential impact on banking operations. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Slide dari sesi Roundtable Disscussion : "Bagaimana membuat pitch deck yang menarik perhatian investor?"
Credit to : Kevin Darmawan, Managing Partner Coffee Ventures
Revenue Cycle: Tracking Reimbursement for DRGs, APCs and MPFSAudioEducator
Review the reimbursement tracking as part of the revenue cycle, and understand the basics of DRGs, APCs and MPFS in this audio session with Duane Abbey.
The document discusses various methods for calculating customer attrition rates in the electronic security industry. It recommends adopting the "Typical Lending Covenant RMR Method" as the industry standard, which calculates attrition by dividing cancelled recurring monthly revenue (RMR) for the reporting period by the sum of ending RMR for each month in the period. This method accounts for both internal growth and acquisitions, providing the most accurate representation of attrition under different company circumstances. Standardizing around this single method would allow for easier comparison of attrition rates across companies.
This document provides an overview of survival analysis. It defines key terms like survival, censoring, and hazard functions. It describes the Kaplan-Meier method for estimating survival functions from censored data and comparing survival curves between groups using the log-rank test. Censoring occurs when subjects are lost to follow-up before the event of interest. The Kaplan-Meier method accounts for censoring to calculate the probability of surviving up to different time points.
A gentle introduction to survival analysisAngelo Tinazzi
This document provides an introduction to survival analysis techniques for statistical programmers. It discusses key concepts in survival analysis including censoring, the Kaplan-Meier method for estimating survival probabilities, and assumptions of survival models. Programming aspects like creating time-to-event datasets and using SAS procedures for survival analysis are also covered.
This document discusses survival analysis and its application to analyzing the departure dynamics of Wikipedia editors. It begins by defining survival analysis and its goal of modeling time-to-event data using techniques that account for censoring. A case study is presented on analyzing data from 110,000 Wikipedia editors to determine who is likely to stop editing, how long they will continue editing, and why they stop. Statistical techniques like the Kaplan-Meier estimator, Cox proportional hazards models, and adjusted survival curves are used to analyze editing durations and identify covariates that impact the hazard rate of editors stopping contributions.
This document discusses survival analysis techniques. It begins with an overview of survival, censoring, and the need for survival analysis when not all patients have died or had the event of interest. It then describes the key techniques of life tables/actuarial analysis and the Kaplan-Meier method. Life tables involve constructing a hypothetical cohort and estimating survival at different ages based on mortality rates. The Kaplan-Meier method is commonly used to illustrate survival curves and gives partial credit to censored observations. A modified life table is also presented to analyze survival outcomes in different treatment groups.
Survival analysis is a branch of statistics used to analyze time-to-event data, such as time until death or failure. It estimates the probability that an individual survives past a given time and compares survival times between groups. Objectives include estimating survival probabilities, comparing survival between groups, and assessing how covariates relate to survival time. Survival data can be complete or censored. The Kaplan-Meier estimator is used to estimate survival when there is censoring. The log-rank test compares survival curves between treatment groups, and Cox regression incorporates covariates to predict survival probabilities.
This document provides an introduction to survival data analysis. It discusses key concepts like censoring, where the event of interest is not observed for some subjects due to other events. Right censoring is common, where the event was not observed by the end of the study. Conditioning is also important, where the risk of an event changes over time based on a subject's survival up to that point. Basic notation is introduced, including the survival function, hazard function, and density function for modeling event times. The document outlines topics like parametric and non-parametric models, regression methods, and complications in survival analysis.
Geo Livi has over 8 years of experience as a Temenos T24 consultant specializing in the retail banking module. He has extensive experience in business analysis, solution design, developing technical specifications, and managing implementation projects. Some of his project experience includes implementing product bundles and broker commissions for various T24 clients as well as performing an upgrade for Hypo Group Alpe Adria and providing support for the implementation at North Shore Credit Union, the first client to use T24's arrangement architecture module.
OA predicta net week data360 predictive analytics ralph overbeck publishedRalph Overbeck
The document discusses predictive analytics and its use of big data. It provides examples of predictive customer management and predictive asset management. Predictive analytics uses historical data and customer insights to predict future events and enable organizations to move from historical to forward-looking perspectives. It transforms data patterns into insights, insights into actions, and actions into profitable outcomes. Predictive analytics predicts with greater accuracy and significance than historical analytics.
Building Innovative Subscription-based Businesses: Lecture 2Martin Westhead
This document provides an overview and key concepts from a lecture on building subscription-based businesses. It discusses recurring revenue, lifetime revenue modeling using different approaches, and the core subscription business model of acquiring new customers, boosting revenue per customer through upsells and cross-sells, and reducing churn. It also covers important subscription health metrics like magic numbers, retention rates, recurring profit margins, and growth efficiencies. A case study from SendGrid reviews their use of metrics like monthly recurring revenue, customer counts, customer retention, customer acquisition costs, and lifetime value to measure growth and profitability in their subscription business.
This document discusses using survival analysis to model customer churn. It explains what customer lifetime value is and why it is important. It then covers how survival analysis can be used to predict when customers will churn rather than just whether they will churn. The document outlines the methodology used, including data cleaning, defining churn, and graphing results. It also covers parametric survival models and evaluating different survival distributions. The conclusions state that survival analysis provides powerful insights into how attrition risk changes over time.
The document discusses using high-performance computing (HPC) to value complex insurance guarantees like variable annuities. It describes the challenges in valuing variable annuities due to their path-dependent cash flows and exposure to market risks. VORtech is working with ING Re to optimize the HPC implementation for valuing variable annuities to improve performance while maintaining flexibility and transparency of the valuation code. Scenarios for implementing HPC include prototyping on more accessible platforms and optimizing parallelization and memory usage.
This document discusses hedging strategies for insurance companies. It notes that establishing hedging programs can mitigate earnings volatility and is viewed positively by rating agencies and analysts. The document outlines identifying risks such as interest rate, market, and credit risk; choosing hedging strategies such as static or dynamic hedging; and implementing hedges through financial modeling and executing trades. Effective hedging programs require quantifying goals, generating economic scenarios, and reviewing hedging effectiveness over time.
Modelling Customer Lifetime Revenue for Subscription BusinessDatabricks
Customer lifetime Value/Revenue(LTV/R) is the present value of the future profits/revenue from a customer. Estimating it, is important for businesses to optimise the marketing costs in acquiring and retaining the customers. Complex consumer behaviour and innumerable ways a consumer interacts with the business makes things challenging to estimate it. Years of ongoing research in this field has led to the development of various ML tools and techniques. We would like to take this opportunity to walkthrough some of these techniques and their applications in specific business contexts.
Condé Nast is a global media company that produces some of the world’s leading print, digital, video and social brands. These include Vogue, GQ, The New Yorker, Vanity Fair, Wired and Architectural Digest (AD), Condé Nast Traveler and La Cucina Italiana, among others. Subscription revenue is one of the major revenue streams for the organization and we’d like to demonstrate the implementation of LTV/R model for the subscription revenue for one of the brands using survival models and along with that illustrate the following.
Estimate the average lifetime (ALT) of a brand’s subscriber.
Estimate the average lifetime of various segments within the brand and identify the most valuable/least valuable segments, so marketing teams could device appropriate targeting strategies.
Finally attempt to estimate the lifetime at a subscriber level.
Key insights & findings through the analysis.
Demo of sample code
Leveraging databricks delta files for our big data processing needs.
The document defines key terms and deliverables for a Lean Six Sigma project. It explains that defining VOC (Voice of the Customer), VOB (Voice of Business), and CTQs (Critical to Quality requirements) is the first deliverable. This involves understanding the problem from the customer's perspective and how they define project success. Additional deliverables include defining the project scope, quantifying benefits, and developing a project management plan. Tools for each deliverable like affinity diagrams and project charters are also outlined.
Metrics: Talking about Churn and Retention in the Board RoomGainsight
Speakers:
David Spitz, Managing Director at Pacific Crest
Marc Linden, CFO at Intacct
Kathy Lord, VP Sales & CSM at Intacct
Dave Kellogg, CEO at Host Analytics
Alison Homlund, VP CSM at Host Analytics
Presented at Pulse Conference 2015.
Explore our students' cutting-edge project on predicting bank customer churn using advanced analytics techniques. This project employs machine learning algorithms to analyze customer data and forecast the likelihood of churn, offering valuable insights for financial institutions. Gain insights into customer retention strategies, predictive modeling, and the potential impact on banking operations. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Slide dari sesi Roundtable Disscussion : "Bagaimana membuat pitch deck yang menarik perhatian investor?"
Credit to : Kevin Darmawan, Managing Partner Coffee Ventures
Revenue Cycle: Tracking Reimbursement for DRGs, APCs and MPFSAudioEducator
Review the reimbursement tracking as part of the revenue cycle, and understand the basics of DRGs, APCs and MPFS in this audio session with Duane Abbey.
The document discusses various methods for calculating customer attrition rates in the electronic security industry. It recommends adopting the "Typical Lending Covenant RMR Method" as the industry standard, which calculates attrition by dividing cancelled recurring monthly revenue (RMR) for the reporting period by the sum of ending RMR for each month in the period. This method accounts for both internal growth and acquisitions, providing the most accurate representation of attrition under different company circumstances. Standardizing around this single method would allow for easier comparison of attrition rates across companies.
Analysing corporate performance with system dynamicsDaniel Jarosch
Premiere, a PayTV operator in Germany, has struggled to reach its subscriber growth targets. The document analyzes Premiere's performance using a system dynamics model. The model simulates different scenarios: the status quo, increasing customer acquisition marketing, and improving customer retention. The simulation shows that improving retention through better customer relationship management leads to more sustainable subscriber growth over time compared to temporary growth from increased acquisition alone.
This document discusses building technically sound simulation models in Crystal Ball. It covers:
- Common applications of simulation modeling and Crystal Ball software.
- The ModelAssist reference tool for simulation best practices.
- Key technical considerations like properly modeling multiplications as sums, distinguishing variability from uncertainty, and accounting for dependencies between variables.
- A checklist of best practices such as engaging decision-makers, keeping models simple, and clearly communicating results.
The document outlines the structure and key components of a 10-minute pitch for angel investors. It discusses introducing the problem being solved, presenting the proposed solution, demonstrating traction to date, describing the market opportunity and customers, outlining the sales strategy and revenue model, reviewing the competition, introducing the management team and advisory board, and projecting financials. The pitch aims to concisely communicate the high-level opportunity and need being addressed, solution being offered, progress to date, and future potential for growth and returns.
You are already familiar with research – but whether you are at the start of your journey, or you are already a power-user, you will get a lot of value from this session. Every registered attendee will receive a confidential, personalised report, on their system usage. We will ensure that we tailor each session to cover the areas of most value to you – and how to extract the maximum value from your research subscription.
- Projected premiums
- Quoting accelerated and standalone trauma and TPD together
- Report history
- Quoting life insurances with any health insurer
- Underwriting terms
- News
- Backing your preferred provider choices with evidence
- Help set our development agenda for new features for the next 12 to 24 months
- Join our research advisory group
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
National Security Agency - NSA mobile device best practices
Subscription Survival Analysis
1. Subscription Survival Analysis
SF Data Mining Meetup
San Francisco
June 2012
Jim Porzak – Senior Director, Business Intelligence
2. What We’ll Cover…
• Quick introduction to survival analysis.
• Rational for marketing & business.
• Subscription businesses past & present.
• The example data set. How real is it?
• Calculating survival, average tenure & LTV.
• Applications of Survival & Hazard curves.
• Discussion.
• Appendix (links & details).
2
3. Traditional Survival Analysis
• Modeling of “time to event” data
– Death in biological organisms (medicine)
– Failure of machines (reliability engineering)
– Political or sociological change (duration
analysis)
• These kinds of data share common gotcha:
– If an “individual” has not yet died, failed, or
changed, what can we say about the
expected time to the event?
3
4. Trick: Use what we know – everything!
• All have started, some have stopped, & some continue on.
– “Right censored”
• From this kind of data we can calculate probability of stopping.
– “Hazard Ratio” = f(tenure, … )
• Probability of survival at tenure, T, is just ( 1 – cumulative hazard(T))
4
6. The Subscription Business Model
1st Visit Acquisition Conversion Lapse Re-activation Today’s #’s
or Win-back
Re-
activate
Engage Win-Back
Acquire
Retain Retain
Convert
6
7. Aside: Subscription Stints – Rational & Method
• For each member we need to know subscription start &, if
terminated, stop dates for each member:
Subscriber A
• But data often has the accounting view with gaps in the subscription:
CC Issue Prod Chg Whatever
Subscriber A
• From the customer’s perspective, he had one continuous
subscription. It’s important to take that view since, in general, the
longer a subscriber is active the lower the risk of churn.
• A subscription stint algorithm typically ignores any gaps up
to, say, 30 days – which is also taken as the boundary between a
lapsed subscriber in the “re-activate” vrs “win-back” pools.
7
8. Why Subscription Survival?
• Expected churn probability of new subscribers
– Project future subscriber count
• Especially after a successful campaign
– Define tenure segment boundaries
• Expected tenure of a new subscriber & LTV
– As function of duration, product, channel, offer
– Evaluate marketing & site tests by value not rates
• Projected value of retention efforts
– How improved renewal rates increase value
• A new way of thinking about subscribers
– Retention metrics, as used in marketing, are deceptive
(see Appendix slide)
8
10. Assumptions
• Homogeneity of strata
– Each includes only one segment of customers
• Stability over time
– Hardest to satisfy?
– Propensity to renew a function of:
• General economic conditions
• Offer(s) at start of subscription
• Engagement efforts by marketing group
• Perceived long term value
10
11. Example Data Set
• 100k records:
– CustomerID
– SubStartDate
– SubEndDate (NULL if subscription active)
– Duration = [A, Q, M] for Annual, Quarterly, or Monthly
– Product = [B, P] for Basic or Professional
– Channel = [R, A, S, B, O] for Referral, Affiliate, Search, Banner, or Other
• Randomly generated with day-of-week and week-in-year
seasonality; 15% year over year growth for starts.
• Randomly generated number of renewals
– With different churn rates for A, Q, & M
• Mimics general properties of subscription data from
Playboy.com, LA Times, 24 Hour Fitness, Chicago Sun
Times, Ancestry.com, & Viadeo.com
– Additional parameters could be Offer, Promotion Cohort, …
• Assumes “no-return” policy, i.e. no early refunds.
11
12. Algorithm – Part 1: Hazard Ratio
(# terminated during day i)
______________________________________
HRi =
(# active at beginning of day i)
= 1/4 = 0.25
Remember N is typically huge in a
subscription business so we tend to get
smooth curves.
12
13. Algorithm – Part 2 Survival Curve
Hazard Ratio Rate
USDeluxe Domestic Acom Hazard
0.14
Duration
A (N = 204,538)
M (N = 196,345)
0.12
0.10
Hazard Rate
0.08
0.06
0.04
0.02
0.00
0 200 400 600 800 1000
Tenure (days)
Subscribers since 2006-01-01 (N = 400,883 subscribers)
Average 2-year Tenure Survival Probability
USDeluxe Domestic Acom Subscriber Survival
1.0
Duration
A (N = 204,538)
M (N = 196,345)
0.8
Probability of Survival
0.6
120 404
0.4
0.2
0.0
0 200 400 600 800 1000
Tenure (days)
Subscribers since 2006-01-01 (N = 400,883 subscribers)
LTR = (Daily Sales Price) * (Average Tenure)
13
14. Application: Average Tenure of Renewal Durations
Subscription Stint Survival by Duration
1-Year 1-year 2-Year 2-year 3-Year 3-year
Duration Half Life Average Probability Average Probability Average Probability
(Months) (days) Tenure of Survival Tenure of Survival Tenure of Survival
1 149 180.9 0.217 NA NA NA NA
3 273 260.9 0.380 354.4 0.131 387.7 0.058
12 456 364.7 0.880 557.8 0.452 629.9 0.159
24 822 363.8 0.991 723.1 0.978 840.3 0.125
14
15. Application: Evaluating a Conversion Test
We test a great new design for the web site conversion
page flow & get 1050 conversions for new flow vs. 1000 for
old flow - a 5% lift!
A: Control Page Flow B: New Page Flow
2-Yr Daily 2-Yr Projected 2- Projected 2-
Tenure ASP Value # Conv. Yr Value # Conv. Yr Value
M 220 0.15 $33.00 500 $16,500 650 $21,450
Q 350 0.12 $42.00 300 $12,600 275 $11,550
A 560 0.09 $50.40 200 $10,080 125 $6,300
Total 1000 $39,180 1050 $39,300
Revenue Per Unit (2-Year) $39.18 $37.43
15
16. Application: Projected Value of Base
So far we have looked at survival from start of subscription. Now we
need remaining survival for current subscribers. For example a 1-year
subscriber:
Just reset
Ps to 1.0
Projected n-year value of your current subscriber base is:
For all subscribers, Sum their
(daily sales price) *
(Area under appropriate survival curve
from their current tenure over the next n years) /
(Ps (current tenure))
16
17. Application: Thinking in Hazard Space
Picking Tenure Segment Boundaries
Simple minded customer segmentation. But very useful!
• Newbie: 0 - 100 days
___
• Builder: 101 - ___ days
___ 380
• Established: 381 - 735 days
___ ___
• Core: 736 days & up
___
Hazard Ratio
USDeluxe Domestic Acom Hazard Rate
0.14
Duration
A (N = 204,538)
M (N = 196,345)
0.12
0.10
Hazard Rate
0.08
0.06
0.04
0.02
0.00
0 200 400 600 800 1000
Tenure (days)
Subscribers since 2006-01-01 (N = 400,883 subscribers)
17
18. Application: Modeling in Hazard Space
What-if Newbie Churn Could be Reduced by 10%?
Red: Newbie
Hazard down by Red: Newbie
10% 2-year value up
by $10
18
19. Wrap-up. What we saw.
• Introduction to classical survival
• Subscribers & calculating their hazard & survival
• Applying survival & hazard to real-world
questions
• See Appendix for links & some more details.
19
22. Subscription Survival Background
• On subscription survival for customer intelligence:
– Gordon Linoff’s 2004 Article from Intelligent Enterprise (now
InformationWeek) http://www.data-miners.com/resources/Customer-
Insight-Article.pdf
– Will Potts’ technical white paper http://www.data-
miners.com/resources/Will%20Survival.pdf
– Berry & Linoff’s white paper for SAS, emphasizes forecasting
application http://www.data-
miners.com/companion/sas/forecastingWP_001.pdf
– Chapter 10 in http://amzn.to/mRAVpp
• Background on “classical” survival analysis:
– http://en.wikipedia.org/wiki/Survival_analysis
• R packages used
– survival by Terry Therneau
– ggplot2, plyr, lubridate by Hadley Wickham, et al
– zoo, xts by Achim Zeileis, Jeffrey Ryan, et al
22
23. Retention vs Survival Curves
• Retention Rate = 1 – Churn Rate
• Churn Rate (typically) defined as:
(# Subscribers leaving)
(Average # subscribers)
over some period.
• Which means:
– Ignores information out of period
– Not monotonically decreasing
23
24. Traditional vs. Subscription Survival
Propertity Traditional Subscription
N Smallish (10^1 – 10^3) Large to huge (10^5 &
up)
Hazard Assumed continuous Usually spiky
Survival curves Often Modeled Empirical
Survival Curve(s) Assumed continuous Usually stair step
24
Editor's Notes
Point out the two strata: CC & No-CC. Then rolled up to All.