Companies are doing a better and better job of collecting data that explains why consumers behave the way they do. These diverse data sets cause us to rethink some of the workhorse algorithms for data analysis. Specifically, the traditional binary response model leaves much room for improvement in how it embraces time. Cross–sectional models allow much rich data to fall through the cracks. We’ll discuss real-world scenarios and how to better use data with time to event modeling.
Webinar: Survival Analysis for Marketing Attribution - July 17, 2013Revolution Analytics
A central question in advertising is how to measure the effectiveness of different ad campaigns. In online advertising, including social media, it is possible to create thousands of different variations on an ad, and serve millions of impressions to targeted audiences each day. Rather too often, digital advertisers use the last click attribution model to evaluate the success of campaigns. In other words, when a user clicks on an ad impression, only the very last event is deemed as significant. This is convenient but doesn't help in making good marketing decisions.
Survival analysis is widely used in the modeling of living organisms and time to failure of components, but Chandler-Pepelnjak (2010) proposed to use survival analysis for marketing attribution analysis. Listen to our webinar to learn more about this theory and a big data case study, showing how DataSong used Revolution Analytics.
The Harvest Digital Guide to Attribution ModellingMike Teasdale
Balancing spend and developing strategy across channels like paid and organic search, display and social is one of the biggest challenges in digital marketing.
In the real world, attribution modelling often boils down to choosing a model and seeing whether we like the results it gives. But this is hardly scientific. So what would a data-driven process to defining and assessing a cross-channel attribution model look like?
One of the crucial trends nowadays will be the growth of attribution modeling. However, many savvy marketers are still missing the opportunity to reap the benefits of attribution modeling, hence this whitepaper is aimed at providing a comprehensive overview of what marketing attribution is all about.
Digital marketing ROI - An introduction to attribution modellingDifferent Spin
To help you get started in the potentially daunting realm of attribution modelling, we’ve crafted this whitepaper to explore what it is and how you can implement it for your business. We go through some of the most common attribution models and help define which of these is likely to be the best starting point for you.
Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data AgeAbsolutdata Analytics
This presentation was given by Eli Kling, Director - Analytics, AbsolutData at The Business Analytics Conference, AmsterDam, October 2013.
AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools
Operational Attribution with Google AnalyticsJonathan Breton
Multi-channel Attribution Modeling isn't necessary complex to implement.
Find here a framework and a methodology to start moving away from last-click vision
Webinar: Survival Analysis for Marketing Attribution - July 17, 2013Revolution Analytics
A central question in advertising is how to measure the effectiveness of different ad campaigns. In online advertising, including social media, it is possible to create thousands of different variations on an ad, and serve millions of impressions to targeted audiences each day. Rather too often, digital advertisers use the last click attribution model to evaluate the success of campaigns. In other words, when a user clicks on an ad impression, only the very last event is deemed as significant. This is convenient but doesn't help in making good marketing decisions.
Survival analysis is widely used in the modeling of living organisms and time to failure of components, but Chandler-Pepelnjak (2010) proposed to use survival analysis for marketing attribution analysis. Listen to our webinar to learn more about this theory and a big data case study, showing how DataSong used Revolution Analytics.
The Harvest Digital Guide to Attribution ModellingMike Teasdale
Balancing spend and developing strategy across channels like paid and organic search, display and social is one of the biggest challenges in digital marketing.
In the real world, attribution modelling often boils down to choosing a model and seeing whether we like the results it gives. But this is hardly scientific. So what would a data-driven process to defining and assessing a cross-channel attribution model look like?
One of the crucial trends nowadays will be the growth of attribution modeling. However, many savvy marketers are still missing the opportunity to reap the benefits of attribution modeling, hence this whitepaper is aimed at providing a comprehensive overview of what marketing attribution is all about.
Digital marketing ROI - An introduction to attribution modellingDifferent Spin
To help you get started in the potentially daunting realm of attribution modelling, we’ve crafted this whitepaper to explore what it is and how you can implement it for your business. We go through some of the most common attribution models and help define which of these is likely to be the best starting point for you.
Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data AgeAbsolutdata Analytics
This presentation was given by Eli Kling, Director - Analytics, AbsolutData at The Business Analytics Conference, AmsterDam, October 2013.
AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools
Operational Attribution with Google AnalyticsJonathan Breton
Multi-channel Attribution Modeling isn't necessary complex to implement.
Find here a framework and a methodology to start moving away from last-click vision
Connecting the Business Insights You Need With the Experience Your Customers Demand
Big data and the dynamic customer experience are two of the hottest trends in digital marketing and technology transformation. Both leverage next-generation machine learning and artificial intelligence to better understand and influence consumer behavior, offering marketers the power to predict the future based on previous interactions. The combination of the two techniques is critical to providing a more precisely targeted and personalized customer journey, increasing revenue and improving operational efficiency. Challenges remain, however. The large number of products available, the complexity of the systems involved, and the massive amounts of data generated can make it difficult to realize the full potential.
This innovative session details a clear path forward for organizations that adopt a more agile approach, balancing immediate gains with progress toward their long-term roadmap. After providing an overview of the considerable benefits and common challenges, the information-packed presentation explains why organizations need to embrace a unified approach to analytics and customer journeys. You’ll also learn about the most critical analytics and data modeling techniques—such as forecasting Customer Lifetime Value and Click Stream Behavior—and how insights from these reports can be incorporated into your customers’ journey, generating measurable return on investment. The content will include a unique, engaging combination of underlying principles and their practical applications, helping you obtain a better understanding of the overall space and providing recommendations you can immediately implement.
Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...Kissmetrics on SlideShare
In this webinar with Ryan Koonce, you’ll learn how to utilize UTM parameters to accurately track your marketing and advertising efforts, and report on them using different attribution models to correctly calculate your cost of acquisition (COA).
Ryan is the founder of SaaS Management Group, a leading growth marketing and analytics consultancy who has founded a number of startups and was a pioneer in viral marketing and landing-page testing and optimization.
In his talk, he wants to teach you how create effective tracking links using explicit UTM parameters instead of Google Auto-Tagging and what you should look out for if you are running Twitter, Facebook, and Google advertising campaigns when calculating an accurate COA.
- What is marketing attribution and why it matters for your business
- Best practices for creating different types of links with UTM parameters
- How common online advertisers (i.e. Google, Facebook, Twitter) attribute the success of their campaigns and what this means for you
- How to build reports to understand attribution and allocate cost data
This presentation describes a process that marketers and marketing analysts can implement to enable their organizations to become more data-driven. It describes the fundamental differences between performance measurement and hypothesis validation, and then describes a framework/process to "A.D.A.P.T. to Act and Learn" (Align, Discover hypotheses, Assess hypotheses, Prioritize hypotheses, Test hypotheses, Act on the results and Learn for the future).
If you are a marketer with a clutter of tools and data - take a look. This solution integrates your analytics into one planning platform for faster, more accurate, and comprehensive decisions.
Gilligan's Guide to Analysts as Community Managers' Best FriendsTim Wilson
Tim Wilson's Boston eMetrics 2012 presentation of tips and approaches that enable analysts to be highly effective and highly valued through the multi-faceted ways they support their community managers. This presentation is also available on YouTube (with voiceover) at: http://youtu.be/4kW5J8dj46k
Customer Journey Analyses: Requirements and Choices Digital Analytics Day 2014ro11 GmbH
20 min Vortrag "Customer Journey Analyses: Requirements and Choices" von Nic Diefenbach und Roland Markowski auf dem Digital Analytics Day 2014 in Hamburg.
Customer Churn. Everyone loves talking about how to calculate it, how to reduce it, and how bad it is (us included!). But isn’t it time to sit down and figure out how we can actually influence it, and what actions we can actually take, given certain characteristics?
The Actionable SaaS Metrics series goes beyond measuring and calculating. It takes a deeper look at some characteristics of common subscription metrics, with the goal of identifying key actionable steps to optimize them for your business.
Eliminate the use of inaccurate ‘last click’ attribution with OptimaHub MediaAttribution. Advertisers can use this tool to get an accurate marketing ROI across all channels and to optimise media spend accordingly.
Model-Based Diagnosis of Discrete Event Systems via Automatic PlanningLUCACERIANI1
This is the talk given for my PhD. dissertation at the University of Brescia (Italy) in March 2015. The slides are integrated with notes to help the reader.
Connecting the Business Insights You Need With the Experience Your Customers Demand
Big data and the dynamic customer experience are two of the hottest trends in digital marketing and technology transformation. Both leverage next-generation machine learning and artificial intelligence to better understand and influence consumer behavior, offering marketers the power to predict the future based on previous interactions. The combination of the two techniques is critical to providing a more precisely targeted and personalized customer journey, increasing revenue and improving operational efficiency. Challenges remain, however. The large number of products available, the complexity of the systems involved, and the massive amounts of data generated can make it difficult to realize the full potential.
This innovative session details a clear path forward for organizations that adopt a more agile approach, balancing immediate gains with progress toward their long-term roadmap. After providing an overview of the considerable benefits and common challenges, the information-packed presentation explains why organizations need to embrace a unified approach to analytics and customer journeys. You’ll also learn about the most critical analytics and data modeling techniques—such as forecasting Customer Lifetime Value and Click Stream Behavior—and how insights from these reports can be incorporated into your customers’ journey, generating measurable return on investment. The content will include a unique, engaging combination of underlying principles and their practical applications, helping you obtain a better understanding of the overall space and providing recommendations you can immediately implement.
Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...Kissmetrics on SlideShare
In this webinar with Ryan Koonce, you’ll learn how to utilize UTM parameters to accurately track your marketing and advertising efforts, and report on them using different attribution models to correctly calculate your cost of acquisition (COA).
Ryan is the founder of SaaS Management Group, a leading growth marketing and analytics consultancy who has founded a number of startups and was a pioneer in viral marketing and landing-page testing and optimization.
In his talk, he wants to teach you how create effective tracking links using explicit UTM parameters instead of Google Auto-Tagging and what you should look out for if you are running Twitter, Facebook, and Google advertising campaigns when calculating an accurate COA.
- What is marketing attribution and why it matters for your business
- Best practices for creating different types of links with UTM parameters
- How common online advertisers (i.e. Google, Facebook, Twitter) attribute the success of their campaigns and what this means for you
- How to build reports to understand attribution and allocate cost data
This presentation describes a process that marketers and marketing analysts can implement to enable their organizations to become more data-driven. It describes the fundamental differences between performance measurement and hypothesis validation, and then describes a framework/process to "A.D.A.P.T. to Act and Learn" (Align, Discover hypotheses, Assess hypotheses, Prioritize hypotheses, Test hypotheses, Act on the results and Learn for the future).
If you are a marketer with a clutter of tools and data - take a look. This solution integrates your analytics into one planning platform for faster, more accurate, and comprehensive decisions.
Gilligan's Guide to Analysts as Community Managers' Best FriendsTim Wilson
Tim Wilson's Boston eMetrics 2012 presentation of tips and approaches that enable analysts to be highly effective and highly valued through the multi-faceted ways they support their community managers. This presentation is also available on YouTube (with voiceover) at: http://youtu.be/4kW5J8dj46k
Customer Journey Analyses: Requirements and Choices Digital Analytics Day 2014ro11 GmbH
20 min Vortrag "Customer Journey Analyses: Requirements and Choices" von Nic Diefenbach und Roland Markowski auf dem Digital Analytics Day 2014 in Hamburg.
Customer Churn. Everyone loves talking about how to calculate it, how to reduce it, and how bad it is (us included!). But isn’t it time to sit down and figure out how we can actually influence it, and what actions we can actually take, given certain characteristics?
The Actionable SaaS Metrics series goes beyond measuring and calculating. It takes a deeper look at some characteristics of common subscription metrics, with the goal of identifying key actionable steps to optimize them for your business.
Eliminate the use of inaccurate ‘last click’ attribution with OptimaHub MediaAttribution. Advertisers can use this tool to get an accurate marketing ROI across all channels and to optimise media spend accordingly.
Model-Based Diagnosis of Discrete Event Systems via Automatic PlanningLUCACERIANI1
This is the talk given for my PhD. dissertation at the University of Brescia (Italy) in March 2015. The slides are integrated with notes to help the reader.
Planning is an essential step before you start any business. Event Managment is an area which yields a lot of money but similarly, a huge amount is invested in it. So, it becomes essential to perfectly plan your Event Management business and measure all the loops and holes inside it before its inception.
An Introduction To The Dick & Carey Instructional Design ModelLarry Weas
The nine basic steps (excluding Summative Evaluation) represent a set of procedures, which is referred to as the systems approach because it is made up of interacting components, each having its own input and output, which together produce predetermined products using the ADDIE process.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
I volunteered my time to share about big data to those looking to understand the space.
This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.
How artificial intelligence (AI) can help maximize customer intelligence ROIVincent de Stoecklin
The presentation aims to present key challenges and success factors when it comes to deploying high value customer-oriented AI projects. We focus on key use cases (churn, cross-sell, personalization…) and present best practices to help build and deploy AI projects, from scoping and data availability to operationalization and adoption.
Key takeaways:
● What are the key AI use cases in Customer Intelligence?
● How do I prioritize and assess the ROI of my use cases?
● How can I ensure my AI projects are successful?
The main task of this talk is to see how Data Science can influence big companies to generate new revenue and more profit.
Subjects that will be addressed in this talk are:
• Understanding a value it brings to corporations on long-term (direct revenue generation not only cost reduction);
• Data Science is important part of digital transformation. Are corporations ready?
• Management dedication on investment;
• Lack of Data Science managers acting as a link between Data Scientists and Business managers. Provide motivation/interesting tasks for Data Scientists while validating investments in business environment;
• Lack of skillful Data scientists;
• Compensation of Data Scientists among other Employees (obviously a different scales needs to be applied);
• Examples of Applied Data Science as revenue generators in Telenor Serbia;
Designing Outcomes For Usability Nycupa Hurst FinalWIKOLO
MarkoHurst.com :: My topic of discussion at the Feb 17 2009 NYC UPA.
Even as the pace of society, business, and the Internet continue to increase, many budgets and time lines continue to decrease. To compound this issue, there is a serious disconnect between business goals, user goals, and what visitors actually do on your site. UX practitioners need a simple and efficient way to reconcile these diverse needs while taking action on their data. Join us to learn about a new method for incorporating quantitative data such as web analytics and business intelligence into your qualitative user experience deliverables: personas, wireframes, and more. This presentation will include discussions of online business models, feedback loops for ensuring cross-discipline collaboration, and ongoing revisions.
Vortrag von Raj Venkatesan und Kim Whitler an der HWZ-Darden Konferenz vom 8. Juni 2017 an der HWZ Hochschule für Wirtschaft Zürich.
https://fh-hwz.ch/conference
Intro to machine learning for web folks @ BlendWebMixLouis Dorard
Get a business understanding of ML by going through key concepts and concrete use cases that illustrate its possibilities for web-based companies.
In this presentation I introduce new technology that makes ML more accessible, and I explain in simple terms the limitations to what can be achieved. Finally, I discuss pragmatic considerations of real-world applications and I give a sneak peak at the Machine Learning Canvas — a framework for describing a predictive system that uses ML to provide value to its end user.
--
L'utilisation du Machine Learning s'est fortement développée ces dernières années, jusqu'à être présent aujourd'hui dans environ la moitié des applications que nous utilisons sur smartphone. Même s'ils n'ont pas connaissance du Machine Learning (ML), les utilisateurs d'applications mobile et web sont devenus demandeurs de fonctionnalités prédictives que le ML rend possibles. Par ailleurs, dans le cadre de l'entreprise, le ML représente un avantage compétitif important qui permet de valoriser ses data en les couplant à une intelligence machine.
Auparavant réservée aux grosses entreprises, cette technologie se démocratise grâce aux nouveaux outils de ML-as-a-Service et aux APIs de prediction. Afin d'en tirer profit, nous verrons ensemble les clés de compréhension du fonctionnement du machine learning, qui sous-tendent ses possibilités et ses limites. Nous verrons également comment amorcer son utilisation dans votre propre projet, au travers du Machine Learning Canvas qui permet de décrire un système où le ML est au cœur de la création de valeur.
Similar to Time-to-Event Models, presented by DataSong and Revolution Analytics (20)
Presented to eRum (Budapest), May 2018
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe the doAzureParallel package, a backend to the "foreach" package that automates the process of spawning a cluster of virtual machines in the Azure cloud to process iterations in parallel. This will include an example of optimizing hyperparameters for a predictive model using the "caret" package.
By David Smith. Presented at Microsoft Build (Seattle), May 7 2018.
Your data scientists have created predictive models using open-source tools, proprietary software, or some combination of both, and now you are interested in lifting and shifting those models to the cloud. In this talk, I'll describe how data scientists can transition their existing workflows — while using mostly the same tools and processes — to train and deploy machine learning models based on open source frameworks to Azure. I'll provide guidance on keeping connections to data sources up-to-date, evaluating and monitoring models, and deploying applications that make use of those models.
Presentation delivered by David Smith to NY R Conference https://www.rstats.nyc/, April 2018:
Minecraft is an open-world creativity game, and a hit with kids. To get kids interested in learning to program with R, we created the "miner" package. This package is a collection of simple functions that allow you to connect with a Minecraft instance, manipulate the world within by creating blocks and controlling the player, and to detect events within the world and react accordingly.
The miner package is intended mainly for kids, to inspire them to learn R while playing Minecraft. But the development of the package also provides some useful insights into how to build an R package to interface with a persistent API, and how to instruct others on its use. In this talk I'll describe how to set up your own Minecraft server, and how to use and extend the package. I'll also provide a few examples of the package in action in a live Minecraft session.
While Python is a widely-used tool for AI development, in this talk I'll make the case for considering R as a platform for developing models for intelligent applications. Firstly, R provides a first-class experience working deep learning frameworks with its keras integration. Equally importantly, it provides the most comprehensive suite of statistical data analysis tools, which are extremely useful for many intelligent applications such as transfer learning. I'll give a few high-level examples in this talk, and we'll go into further detail in the accompanying interactive code lab.
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe several techniques available in R to speed up workloads like these, by running multiple iterations simultaneously, in parallel.
Many of these techniques require the use of a cluster of machines running R, and I'll provide examples of using cloud-based services to provision clusters for parallel computations. In particular, I will describe how you can use the SparklyR package to distribute data manipulations using the dplyr syntax, on a cluster of servers provisioned in the Azure cloud.
Presented by David Smith at Data Day Texas in Austin, January 27 2018.
A look at the changing perceptions of R, from the early days of the R project to today. Microsoft sponsor talk, presented by David Smith to the useR!2017 conference in Brussels, July 5 2017.
Predicting Loan Delinquency at One Million Transactions per SecondRevolution Analytics
Real-time applications of predictive models must be able to generate predictions at the rate that transactions are generated. Previously, such applications of models trained using R needed to be converted to other languages like C++ or Java to achieve the required throughput. In this talk, I’ll describe how to use the in-database R processing capabilities of Microsoft R Server to detect fraud in a SQL Server database of loan records at a rate exceeding one million transactions per second. I will also show the process of training the underlying gradient-boosted tree model on a large training set using the out-of-memory algorithms of Microsoft R.
Presented by David Smith at The Data Science Summit, Chicago, April 20 2017.
The ability to independently reproduce results is a critical issue within the scientific community today, and is equally important for collaboration and compliance in business. In this talk, I'll introduce several features available in R that help you make reproducibility a standard part of your data science workflow. The talk will include tips on working with data and files, combining code and output, and managing R's changing package ecosystem.
Presented by David Smith, R Community Lead (Microsoft), at Monktoberfest October 2016.
The value of open source isn’t just in the software itself. The communities that form around open source software provide just as much value and sometimes even more: in ongoing development, in documentation, in support, in marketing, and as a supply of ready-trained employees. Companies who build on open source tend to focus on the software, but neglect communities at their peril.
In this talk, I share some of my experiences in building community for an open-source software company, Revolution Analytics, and perspectives since the acquisition by Microsoft in 2015.
R is more than just a language. Many of the reasons why R has become such a popular tool for data science come from the ecosystem surrounding the R project. R users benefit from the many resources and packages created by the community, while commercial companies (including Microsoft) provide tools to extend and support R, and services to help people use R.
In this talk, I will give an overview of the R Ecosystem and describe how it has been a critical component of R’s success, and include several examples of Microsoft’s contributions to the ecosystem.
(Presented to EARL London, September 2016)
(Presented by David Smith at useR!2016, June 2016. Recording: https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/R-at-Microsoft )
Since the acquisition of Revolution Analytics in April 2015, Microsoft has embarked upon a project to build R technology into many Microsoft products, so that developers and data scientists can use the R language and R packages to analyze data in their data centers and in cloud environments.
In this talk I will give an overview (and a demo or two) of how R has been integrated into various Microsoft products. Microsoft data scientists are also big users of R, and I'll describe a couple of examples of R being used to analyze operational data at Microsoft. I'll also share some of my experiences in working with open source projects at Microsoft, and my thoughts on how Microsoft works with open source communities including the R Project.
Hadoop is famously scalable. Cloud Computing is famously scalable. R – the thriving and extensible open source Data Science software – not so much. But what if we seamlessly combined Hadoop, Cloud Computing, and R to create a scalable Data Science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based Web Service. Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms at scale.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
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Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
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(i.e., industry structure in the language of economics).
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4. DataSong at a Glance
Approaching $1 trillion in revenue analyzed. $2 billion in marketing spend under our lens.
Experienced 60 person team based out of San Francisco, with offices in Seattle, LA and India.
Founded in 2003 with a proven history of solving difficult analytics problems. Evolved from consulting
through close partnerships with clients.
Customer interaction insight that powers applications for customer level revenue attribution,
targeting, media optimization
Actionable and accurate information that drives customer acquisition and revenue growth for
modern direct marketers.
Patented big data approach models behavior at the individual consumer level.
5. DataSong Offerings
1. A regression modeling framework for prediction and inference
2. Automation of modelsets in Hadoop
3. Enterprise grade scoring in Hadoop
6. Modelset Creation: Current State
Flatten out the data
• 1. Aggregate a fact table (sum, count)
• 2. Join a dimension to a fact table and aggregate it (sum, count)
• 3. Superpose time
• If we have a dimension with a cardinality of 25 and 6 time periods of interest, that’s 150
variables for 1 dimension
AccountNo #SiteVisits
123456 5
AccountNo #Visits_SEO #Visits_EmailClick #Visits_SEM #Visits_...
123456 3 1 1 …
AccountNo #Visits_SEO_1Mo #Visits_SEO_3Mo #Visits_SEO_6Mo #Visits_SEO_...
123456 1 2 3 …
7. In Our Opinion
“Feature Engineering”
• Creating good variables is many times more important than choice of algorithm
Don’t lose track of time
• Age old practice of flattening data into 1 row per customer with 1000s of variables is
limiting
Aggregations can obfuscate
Time series without customer- level data overlook important causal relationships
8. New Challenges for Predictive Modeling
More and more of our input data is generated from log files
• Large observational data (or if you want to call it Big Data, you can)
• We are approaching an infinite number of variables to test
Increasing # of use cases for real time scoring
Increasing # of opportunities to use models for inference
10. What Are We Doing About it?
Survival Response Model
• Explains differences in response rate as we change exposure to marketing
• Know what was significant and what wasn’t
Account ID-level analysis follows customers and cookies over time
Time-dependent Outcome had an event or was censored
Time-dependent Covariates the effect of an event is not constant
Time-varying Covariates time may modify an event effect
Controls for non-marketing effects:
Baseline Hazard Rate
Customer-driven activity many customers are driven by loyalty vs. marketing
Anniversary Effects many sales driven by season demand vs. marketing
12. CUSTOMER
SERVICE
CUSTOMER INTERACTION OBJECTIVE TIME APPROACH OUTCOME
BEHAVIORAL
LOYALTY
SITE VISIT
SUBSCRIPTION-CENTRIC
IDLEVELTIMESTAMPDATA
INFERENCE
PREDICTION
TIME-TO-EVENT
POINT-IN-TIME
Response
ModelMARKETING
SERVICE
LOYALTY
TELEMATIC
PRICE/
PROMOTION
COMPETITION
SEASONALITY
UPGRADE
LEAVE
DEFAULT
MACRODATA
SITE VISIT
PURCHASE
13. CUSTOMER
SERVICE
CUSTOMER INTERACTION OBJECTIVE TIME APPROACH OUTCOME
BEHAVIORAL
LOYALTY
SITE VISIT
SUBSCRIPTION-CENTRIC
IDLEVELTIMESTAMPDATA
INFERENCE
PREDICTION
TIME-TO-EVENT
POINT-IN-TIME
Voluntary
Churn
Model
MARKETING
SERVICE
LOYALTY
TELEMATIC
PRICE/
PROMOTION
COMPETITION
SEASONALITY
UPGRADE
LEAVE
DEFAULT
MACRODATA
SITE VISIT
PURCHASE
14. CUSTOMER
SERVICE
CUSTOMER INTERACTION OBJECTIVE TIME APPROACH OUTCOME
BEHAVIORAL
LOYALTY
SITE VISIT
SUBSCRIPTION-CENTRIC
IDLEVELTIMESTAMPDATA
INFERENCE
PREDICTION
TIME-TO-EVENT
POINT-IN-TIME
Involuntary
Churn
Model
MARKETING
SERVICE
LOYALTY
TELEMATIC
PRICE/
PROMOTION
COMPETITION
SEASONALITY
UPGRADE
LEAVE
DEFAULT
MACRODATA
SITE VISIT
PURCHASE
15. SITE VISIT
CUSTOMER
SERVICE
PURCHASE
CUSTOMER INTERACTION OBJECTIVE TIME APPROACH OUTCOME
BEHAVIORAL
LOYALTY
SUBSCRIPTION-CENTRIC
IDLEVELTIMESTAMPDATA
INFERENCE
PREDICTION
TIME-TO-EVENT
POINT-IN-TIME
Simple
Attribution
Model
MARKETING
SERVICE
LOYALTY
TELEMATIC
PRICE/
PROMOTION
COMPETITION
SEASONALITY
UPGRADE
LEAVE
DEFAULT
MACRODATA
16. SITE VISIT
CUSTOMER
SERVICE
PURCHASE
CUSTOMER INTERACTION OBJECTIVE TIME APPROACH OUTCOME
BEHAVIORAL
LOYALTY
SUBSCRIPTION-CENTRIC
IDLEVELTIMESTAMPDATA
INFERENCE
PREDICTION
TIME-TO-EVENT
POINT-IN-TIME
Incremental
Attribution
Model
MARKETING
SERVICE
LOYALTY
TELEMATIC
PRICE/
PROMOTION
COMPETITION
SEASONALITY
UPGRADE
LEAVE
DEFAULT
MACRODATA
17. Customer
3
Customer
2
Customer
1
What Would the Model Say?
JANUARY FEBRUARY MARCH APRIL MAY JUNE
PURCHASE
CATALOG
EMAIL
CATALOG
EMAIL
EMAIL
EMAIL
CATALOG
EMAIL
$100 PURCHASE
PURCHASE $100 PURCHASE
PURCHASE $100 PURCHASEPURCHASE
DAYS SINCE TREATMENT SALES ALLOCATION
customer sales Catalog Email Retarget
Cumulative
Orders
Catalog Email Retarget Brand Loyalty
#1 $ 100 20 40 0 1 $ 95.66 $ 0.02 $ - $ 4.32
#2 $ 100 20 10 0 1 $ 77.52 $ 18.16 $ - $ 4.32
#3 $ 100 20 10 0 2 $ 69.94 $ 17.74 $ - $ 12.32
18. Functions Used Purpose
rxImport read in data from flat files
READ/WRITE rxDataStep read from XDF file, output to xdf file
rxReadXdf read from XDF file, can output to dataframe
rxSummary calculate summary stats on XDF file
rxCrossTabs build contingency tables of factors
EDA rxCube build contingency tables of factors
rxHistogram create histograms of numeric vars
rxQuantile calculate quantiles of numeric vars
rxLogit build logistic regression models
MODELING rxPredict score data from xdf with specifed model
rxRocCurve evaluate false and true positives of models
rxDTree* build classification and regression trees
Revolution R Enterprise ScaleR Functions Used
Run time for 30MM rows
and 30 variables is
approx 5 min
19. Prediction: Current State
How did we deliver?
Propensity Score (LOW HIGH)
Other models only use one dimension to predict
likelihood to purchase: PROPENSITY
20. Prediction: DataSong Approach
Incrementality Metric
Sensitivity
Score
● Breakthrough results from adding customer sensitivity score: 14% increase in response rate
● Reallocated marketing circulation: Identified best prospects to not mail that were likely to purchase
without receiving catalog
Propensity Score (LOW HIGH)
(LOWHIGH)
Response modeling single channel: swap set usage
INCREMENTALITY metric predicts sensitivity of the next
marketing treatment
21. Scoring Discussion
Scoring systems are like picture frames: good art is never without one
Your best model may never see the light of day
• Sharing your parameter estimates isn’t enough
Who should own scoring ?
• IT: Production support, high uptime mentality
• Analytics: often missing the software engineering discipline
Scale
Analytics teams should be able to manage dozens of models and score billions of
records everyday
22. DataSong Architecture
• ETL
• N marketing channels
• Behavioral variables
• Promotional data
• Overlay data
• Functions to read Hadoop output;
xdf creation
• Exploratory data analysis
• GAM survival models
• Scoring for inference
• Scoring for prediction
• 5 billion scores per day
per customer
DATASONG DATA
FORMAT (DDF)
CUSTOM VARIABLES
(PMML)
23. DataSong Contact
1. A regression modeling framework for prediction and inference
2. Automation of modelsets in Hadoop
3. Enterprise grade scoring in Hadoop
Linked In: www.linkedin.com/company/datasong
Facebook: www.facebook.com/datasong
Twitter: www.twitter.com/datasong
Phone: 877.540.5910
Email: info@datasong.com